diff --git a/.ipynb_checkpoints/Analogical Proj Experiments-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/Analogical Proj Experiments-Copy1-checkpoint.ipynb
deleted file mode 100644
index c154c5e..0000000
--- a/.ipynb_checkpoints/Analogical Proj Experiments-Copy1-checkpoint.ipynb	
+++ /dev/null
@@ -1,3989 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "historic-drive",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !isa_file=../data/P279_dataset/wikidata-P279.tsv\n",
-    "# !labels_file=../data/labels.en.tsv\n",
-    "!kgtk query -i ../data/P279_dataset/wikidata-P279.tsv -i ../data/labels.en.tsv \\\n",
-    "--match 'i: (x)-[:P279]->(y), l: (x)-[:label]->(lbl), (y)-[:label]->(lbl2)' \\\n",
-    "--where 'lbl =~ \".* .*\" AND instr(kgtk_unstringify(kgtk_lqstring_text(lbl)), kgtk_unstringify(kgtk_lqstring_text(lbl2))) AND lbl!=lbl2' \\\n",
-    "--return 'distinct x, y, kgtk_lqstring_text_string(lbl), kgtk_lqstring_text_string(lbl2)' \\\n",
-    "-o ../data/compositional.tsv\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "geographic-andorra",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "node1\tnode2\tkgtk_lqstring_text_string(graph_2_c2.\"node2\")\tkgtk_lqstring_text_string(graph_2_c3.\"node2\")\r\n",
-      "Q1000064\tQ11016\t\"nuclear reactor technology\"\t\"technology\"\r\n",
-      "Q1000120\tQ12510\t\"maximal torus\"\t\"torus\"\r\n",
-      "Q100061973\tQ1799072\t\"presentation method\"\t\"method\"\r\n",
-      "Q1000828\tQ918385\t\"team training\"\t\"training\"\r\n",
-      "Q1000850\tQ748\t\"Buddhism in Germany\"\t\"Buddhism\"\r\n",
-      "Q1000859\tQ748\t\"Buddhism in Iceland\"\t\"Buddhism\"\r\n",
-      "Q1000861\tQ748\t\"Buddhism in Ireland\"\t\"Buddhism\"\r\n",
-      "Q1000863\tQ376\t\"master clock\"\t\"clock\"\r\n",
-      "Q1000869\tQ748\t\"Buddhism in Bangladesh\"\t\"Buddhism\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!head ../data/compositional.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "prospective-commission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "comp = pd.read_csv('../data/compositional.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "personalized-liberal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp['node1_len'] = comp.node1.apply(lambda p: len(p))\n",
-    "comp['node2_len'] = comp.node2.apply(lambda p: len(p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "naval-python",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp = comp.sort_values(by=['node1_len', 'node2_len'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "postal-baseline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "      <th>node1_len</th>\n",
-       "      <th>node2_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>67979</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55355</th>\n",
-       "      <td>Q416</td>\n",
-       "      <td>Q41298</td>\n",
-       "      <td>disk magazine</td>\n",
-       "      <td>magazine</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98710</th>\n",
-       "      <td>Q944</td>\n",
-       "      <td>Q41217</td>\n",
-       "      <td>quantum mechanics</td>\n",
-       "      <td>mechanics</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49124</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41635</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1     node2 kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "67979  Q561      Q553                                   human tooth   \n",
-       "55355  Q416    Q41298                                 disk magazine   \n",
-       "98710  Q944    Q41217                             quantum mechanics   \n",
-       "49124  Q305   Q186361                           corporal punishment   \n",
-       "41635  Q250  Q1921606                             computer keyboard   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  node1_len  node2_len  \n",
-       "67979                                         tooth          4          4  \n",
-       "55355                                      magazine          4          6  \n",
-       "98710                                     mechanics          4          6  \n",
-       "49124                                    punishment          4          7  \n",
-       "41635                                      keyboard          4          8  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "median-singer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tabulate import tabulate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "charitable-abraham",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "      <th>node1_len</th>\n",
-       "      <th>node2_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>94514</th>\n",
-       "      <td>Q8141</td>\n",
-       "      <td>Q9288</td>\n",
-       "      <td>Modern Hebrew</td>\n",
-       "      <td>Hebrew</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41635</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93984</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98024</th>\n",
-       "      <td>Q9192</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Mandarin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67979</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69745</th>\n",
-       "      <td>Q5856</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>synthetic fiber</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87295</th>\n",
-       "      <td>Q6754</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>charm quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57146</th>\n",
-       "      <td>Q4543</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>beach volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99864</th>\n",
-       "      <td>Q9778</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>electronic music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88511</th>\n",
-       "      <td>Q699</td>\n",
-       "      <td>Q17991521</td>\n",
-       "      <td>fairy tale</td>\n",
-       "      <td>tale</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43353</th>\n",
-       "      <td>Q2642</td>\n",
-       "      <td>Q2638</td>\n",
-       "      <td>exponential-Golomb coding</td>\n",
-       "      <td>Golomb coding</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98991</th>\n",
-       "      <td>Q9585</td>\n",
-       "      <td>Q432</td>\n",
-       "      <td>Shia Islam</td>\n",
-       "      <td>Islam</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87447</th>\n",
-       "      <td>Q6786</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>bottom quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89314</th>\n",
-       "      <td>Q7141</td>\n",
-       "      <td>Q420</td>\n",
-       "      <td>cell biology</td>\n",
-       "      <td>biology</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69759</th>\n",
-       "      <td>Q5860</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>carbon fibers</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73413</th>\n",
-       "      <td>Q6311</td>\n",
-       "      <td>Q8104</td>\n",
-       "      <td>stellar atmosphere</td>\n",
-       "      <td>atmosphere</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95355</th>\n",
-       "      <td>Q8494</td>\n",
-       "      <td>Q8481</td>\n",
-       "      <td>human sternum</td>\n",
-       "      <td>sternum</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55941</th>\n",
-       "      <td>Q426</td>\n",
-       "      <td>Q2386606</td>\n",
-       "      <td>animal rights</td>\n",
-       "      <td>right</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49124</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94475</th>\n",
-       "      <td>Q8124</td>\n",
-       "      <td>Q58</td>\n",
-       "      <td>human penis</td>\n",
-       "      <td>penis</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94875</th>\n",
-       "      <td>Q8345</td>\n",
-       "      <td>Q8343</td>\n",
-       "      <td>bass clarinet</td>\n",
-       "      <td>clarinet</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58838</th>\n",
-       "      <td>Q4812</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>day sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18599</th>\n",
-       "      <td>Q1827</td>\n",
-       "      <td>Q282</td>\n",
-       "      <td>red wine</td>\n",
-       "      <td>wine</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88919</th>\n",
-       "      <td>Q7076</td>\n",
-       "      <td>Q6199</td>\n",
-       "      <td>individualist anarchism</td>\n",
-       "      <td>anarchism</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3014</th>\n",
-       "      <td>Q1111</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>electric charge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1      node2 kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "94514  Q8141      Q9288                                 Modern Hebrew   \n",
-       "41635   Q250   Q1921606                             computer keyboard   \n",
-       "93984  Q7991       Q336                               natural science   \n",
-       "98024  Q9192      Q7850                              Mandarin Chinese   \n",
-       "67979   Q561       Q553                                   human tooth   \n",
-       "69745  Q5856       Q161                               synthetic fiber   \n",
-       "87295  Q6754      Q6718                                   charm quark   \n",
-       "57146  Q4543      Q1734                              beach volleyball   \n",
-       "99864  Q9778       Q638                              electronic music   \n",
-       "88511   Q699  Q17991521                                    fairy tale   \n",
-       "43353  Q2642      Q2638                     exponential-Golomb coding   \n",
-       "98991  Q9585       Q432                                    Shia Islam   \n",
-       "87447  Q6786      Q6718                                  bottom quark   \n",
-       "89314  Q7141       Q420                                  cell biology   \n",
-       "69759  Q5860       Q161                                 carbon fibers   \n",
-       "73413  Q6311      Q8104                            stellar atmosphere   \n",
-       "95355  Q8494      Q8481                                 human sternum   \n",
-       "55941   Q426   Q2386606                                 animal rights   \n",
-       "49124   Q305    Q186361                           corporal punishment   \n",
-       "94475  Q8124        Q58                                   human penis   \n",
-       "94875  Q8345      Q8343                                 bass clarinet   \n",
-       "58838  Q4812       Q527                                       day sky   \n",
-       "18599  Q1827       Q282                                      red wine   \n",
-       "88919  Q7076      Q6199                       individualist anarchism   \n",
-       "3014   Q1111     Q73792                               electric charge   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  node1_len  node2_len  \n",
-       "94514                                        Hebrew          5          5  \n",
-       "41635                                      keyboard          4          8  \n",
-       "93984                                       science          5          4  \n",
-       "98024                                       Chinese          5          5  \n",
-       "67979                                         tooth          4          4  \n",
-       "69745                                         fiber          5          4  \n",
-       "87295                                         quark          5          5  \n",
-       "57146                                    volleyball          5          5  \n",
-       "99864                                         music          5          4  \n",
-       "88511                                          tale          4          9  \n",
-       "43353                                 Golomb coding          5          5  \n",
-       "98991                                         Islam          5          4  \n",
-       "87447                                         quark          5          5  \n",
-       "89314                                       biology          5          4  \n",
-       "69759                                         fiber          5          4  \n",
-       "73413                                    atmosphere          5          5  \n",
-       "95355                                       sternum          5          5  \n",
-       "55941                                         right          4          8  \n",
-       "49124                                    punishment          4          7  \n",
-       "94475                                         penis          5          3  \n",
-       "94875                                      clarinet          5          5  \n",
-       "58838                                           sky          5          4  \n",
-       "18599                                          wine          5          4  \n",
-       "88919                                     anarchism          5          5  \n",
-       "3014                                         charge          5          6  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp[:50].sample(n=25, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "random-dress",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       node1    node2      kgtk_lqstring_text_string(graph_2_c2.\"node2\")    kgtk_lqstring_text_string(graph_2_c3.\"node2\")      node1_len    node2_len\n",
-      "-----  -------  ---------  -----------------------------------------------  -----------------------------------------------  -----------  -----------\n",
-      "94514  Q8141    Q9288      Modern Hebrew                                    Hebrew                                                     5            5\n",
-      "41635  Q250     Q1921606   computer keyboard                                keyboard                                                   4            8\n",
-      "93984  Q7991    Q336       natural science                                  science                                                    5            4\n",
-      "98024  Q9192    Q7850      Mandarin Chinese                                 Chinese                                                    5            5\n",
-      "67979  Q561     Q553       human tooth                                      tooth                                                      4            4\n",
-      "69745  Q5856    Q161       synthetic fiber                                  fiber                                                      5            4\n",
-      "87295  Q6754    Q6718      charm quark                                      quark                                                      5            5\n",
-      "57146  Q4543    Q1734      beach volleyball                                 volleyball                                                 5            5\n",
-      "99864  Q9778    Q638       electronic music                                 music                                                      5            4\n",
-      "88511  Q699     Q17991521  fairy tale                                       tale                                                       4            9\n",
-      "43353  Q2642    Q2638      exponential-Golomb coding                        Golomb coding                                              5            5\n",
-      "98991  Q9585    Q432       Shia Islam                                       Islam                                                      5            4\n",
-      "87447  Q6786    Q6718      bottom quark                                     quark                                                      5            5\n",
-      "89314  Q7141    Q420       cell biology                                     biology                                                    5            4\n",
-      "69759  Q5860    Q161       carbon fibers                                    fiber                                                      5            4\n",
-      "73413  Q6311    Q8104      stellar atmosphere                               atmosphere                                                 5            5\n",
-      "95355  Q8494    Q8481      human sternum                                    sternum                                                    5            5\n",
-      "55941  Q426     Q2386606   animal rights                                    right                                                      4            8\n",
-      "49124  Q305     Q186361    corporal punishment                              punishment                                                 4            7\n",
-      "94475  Q8124    Q58        human penis                                      penis                                                      5            3\n",
-      "94875  Q8345    Q8343      bass clarinet                                    clarinet                                                   5            5\n",
-      "58838  Q4812    Q527       day sky                                          sky                                                        5            4\n",
-      "18599  Q1827    Q282       red wine                                         wine                                                       5            4\n",
-      "88919  Q7076    Q6199      individualist anarchism                          anarchism                                                  5            5\n",
-      " 3014  Q1111    Q73792     electric charge                                  charge                                                     5            6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tabulate(comp[:50].sample(n=25, random_state=13), headers = 'keys', tablefmt = 'simple'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "governing-execution",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>76391</th>\n",
-       "      <td>Q66502527</td>\n",
-       "      <td>Q27058096</td>\n",
-       "      <td>right fifth intercostal nerve</td>\n",
-       "      <td>fifth intercostal nerve</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64439</th>\n",
-       "      <td>Q5453438</td>\n",
-       "      <td>Q392316</td>\n",
-       "      <td>First Nations in Alberta</td>\n",
-       "      <td>First Nations</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54764</th>\n",
-       "      <td>Q3998922</td>\n",
-       "      <td>Q43304311</td>\n",
-       "      <td>clarinet-violin-piano trio</td>\n",
-       "      <td>piano trio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71844</th>\n",
-       "      <td>Q60687320</td>\n",
-       "      <td>Q29385978</td>\n",
-       "      <td>President of Communal Council of Tremithousa</td>\n",
-       "      <td>President of Communal Council</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26611</th>\n",
-       "      <td>Q21116244</td>\n",
-       "      <td>Q14878311</td>\n",
-       "      <td>glutamate catabolic process to 2-oxoglutarate</td>\n",
-       "      <td>glutamate catabolic process</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76415</th>\n",
-       "      <td>Q66502641</td>\n",
-       "      <td>Q66502435</td>\n",
-       "      <td>medial branch of cervicothoracic ganglion</td>\n",
-       "      <td>branch of cervicothoracic ganglion</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24160</th>\n",
-       "      <td>Q20901636</td>\n",
-       "      <td>Q705135</td>\n",
-       "      <td>Grand Officer of the Order of Adolphe of Nassau</td>\n",
-       "      <td>Order of Adolphe of Nassau</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27271</th>\n",
-       "      <td>Q21127225</td>\n",
-       "      <td>Q133492</td>\n",
-       "      <td>letters of Pliny the Younger</td>\n",
-       "      <td>letter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3924</th>\n",
-       "      <td>Q1152224</td>\n",
-       "      <td>Q12050496</td>\n",
-       "      <td>distinctive feature</td>\n",
-       "      <td>feature</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84513</th>\n",
-       "      <td>Q66571241</td>\n",
-       "      <td>Q66570136</td>\n",
-       "      <td>right cuneate fasciculus of medulla</td>\n",
-       "      <td>cuneate fasciculus of medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92282</th>\n",
-       "      <td>Q7624573</td>\n",
-       "      <td>Q1084</td>\n",
-       "      <td>strong noun</td>\n",
-       "      <td>noun</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23304</th>\n",
-       "      <td>Q20205194</td>\n",
-       "      <td>Q1644573</td>\n",
-       "      <td>Orthodox pilgrimage</td>\n",
-       "      <td>pilgrimage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9611</th>\n",
-       "      <td>Q14860562</td>\n",
-       "      <td>Q14862215</td>\n",
-       "      <td>positive regulation of cell motility</td>\n",
-       "      <td>regulation of cell motility</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92883</th>\n",
-       "      <td>Q77584712</td>\n",
-       "      <td>Q837924</td>\n",
-       "      <td>skew-symmetric bilinear form</td>\n",
-       "      <td>bilinear form</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42826</th>\n",
-       "      <td>Q2593298</td>\n",
-       "      <td>Q1319755</td>\n",
-       "      <td>European Speed Skating Championships for Men</td>\n",
-       "      <td>European Speed Skating Championships</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29359</th>\n",
-       "      <td>Q2213536</td>\n",
-       "      <td>Q193343</td>\n",
-       "      <td>extensional tectonics</td>\n",
-       "      <td>tectonics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38430</th>\n",
-       "      <td>Q22328939</td>\n",
-       "      <td>Q22328937</td>\n",
-       "      <td>nuclear subtelomeric heterochromatin</td>\n",
-       "      <td>subtelomeric heterochromatin</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51013</th>\n",
-       "      <td>Q32861734</td>\n",
-       "      <td>Q5758703</td>\n",
-       "      <td>Western Highland Chatino</td>\n",
-       "      <td>Highland Chatino</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70167</th>\n",
-       "      <td>Q5898386</td>\n",
-       "      <td>Q105688</td>\n",
-       "      <td>hypobaric hypoxia</td>\n",
-       "      <td>hypoxia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7736</th>\n",
-       "      <td>Q13631670</td>\n",
-       "      <td>Q588723</td>\n",
-       "      <td>Passenger rail transport in China</td>\n",
-       "      <td>rail transport in China</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           node1      node2    kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "76391  Q66502527  Q27058096                    right fifth intercostal nerve   \n",
-       "64439   Q5453438    Q392316                         First Nations in Alberta   \n",
-       "54764   Q3998922  Q43304311                       clarinet-violin-piano trio   \n",
-       "71844  Q60687320  Q29385978     President of Communal Council of Tremithousa   \n",
-       "26611  Q21116244  Q14878311    glutamate catabolic process to 2-oxoglutarate   \n",
-       "76415  Q66502641  Q66502435        medial branch of cervicothoracic ganglion   \n",
-       "24160  Q20901636    Q705135  Grand Officer of the Order of Adolphe of Nassau   \n",
-       "27271  Q21127225    Q133492                     letters of Pliny the Younger   \n",
-       "3924    Q1152224  Q12050496                              distinctive feature   \n",
-       "84513  Q66571241  Q66570136              right cuneate fasciculus of medulla   \n",
-       "92282   Q7624573      Q1084                                      strong noun   \n",
-       "23304  Q20205194   Q1644573                              Orthodox pilgrimage   \n",
-       "9611   Q14860562  Q14862215             positive regulation of cell motility   \n",
-       "92883  Q77584712    Q837924                     skew-symmetric bilinear form   \n",
-       "42826   Q2593298   Q1319755     European Speed Skating Championships for Men   \n",
-       "29359   Q2213536    Q193343                            extensional tectonics   \n",
-       "38430  Q22328939  Q22328937             nuclear subtelomeric heterochromatin   \n",
-       "51013  Q32861734   Q5758703                         Western Highland Chatino   \n",
-       "70167   Q5898386    Q105688                                hypobaric hypoxia   \n",
-       "7736   Q13631670    Q588723                Passenger rail transport in China   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  \n",
-       "76391                       fifth intercostal nerve  \n",
-       "64439                                 First Nations  \n",
-       "54764                                    piano trio  \n",
-       "71844                 President of Communal Council  \n",
-       "26611                   glutamate catabolic process  \n",
-       "76415            branch of cervicothoracic ganglion  \n",
-       "24160                    Order of Adolphe of Nassau  \n",
-       "27271                                        letter  \n",
-       "3924                                        feature  \n",
-       "84513                 cuneate fasciculus of medulla  \n",
-       "92282                                          noun  \n",
-       "23304                                    pilgrimage  \n",
-       "9611                    regulation of cell motility  \n",
-       "92883                                 bilinear form  \n",
-       "42826          European Speed Skating Championships  \n",
-       "29359                                     tectonics  \n",
-       "38430                  subtelomeric heterochromatin  \n",
-       "51013                              Highland Chatino  \n",
-       "70167                                       hypoxia  \n",
-       "7736                        rail transport in China  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp.sample(n=20, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "bacterial-commerce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       node1      node2      kgtk_lqstring_text_string(graph_2_c2.\"node2\")    kgtk_lqstring_text_string(graph_2_c3.\"node2\")\n",
-      "-----  ---------  ---------  -----------------------------------------------  -----------------------------------------------\n",
-      "76391  Q66502527  Q27058096  right fifth intercostal nerve                    fifth intercostal nerve\n",
-      "64439  Q5453438   Q392316    First Nations in Alberta                         First Nations\n",
-      "54764  Q3998922   Q43304311  clarinet-violin-piano trio                       piano trio\n",
-      "71844  Q60687320  Q29385978  President of Communal Council of Tremithousa     President of Communal Council\n",
-      "26611  Q21116244  Q14878311  glutamate catabolic process to 2-oxoglutarate    glutamate catabolic process\n",
-      "76415  Q66502641  Q66502435  medial branch of cervicothoracic ganglion        branch of cervicothoracic ganglion\n",
-      "24160  Q20901636  Q705135    Grand Officer of the Order of Adolphe of Nassau  Order of Adolphe of Nassau\n",
-      "27271  Q21127225  Q133492    letters of Pliny the Younger                     letter\n",
-      " 3924  Q1152224   Q12050496  distinctive feature                              feature\n",
-      "84513  Q66571241  Q66570136  right cuneate fasciculus of medulla              cuneate fasciculus of medulla\n",
-      "92282  Q7624573   Q1084      strong noun                                      noun\n",
-      "23304  Q20205194  Q1644573   Orthodox pilgrimage                              pilgrimage\n",
-      " 9611  Q14860562  Q14862215  positive regulation of cell motility             regulation of cell motility\n",
-      "92883  Q77584712  Q837924    skew-symmetric bilinear form                     bilinear form\n",
-      "42826  Q2593298   Q1319755   European Speed Skating Championships for Men     European Speed Skating Championships\n",
-      "29359  Q2213536   Q193343    extensional tectonics                            tectonics\n",
-      "38430  Q22328939  Q22328937  nuclear subtelomeric heterochromatin             subtelomeric heterochromatin\n",
-      "51013  Q32861734  Q5758703   Western Highland Chatino                         Highland Chatino\n",
-      "70167  Q5898386   Q105688    hypobaric hypoxia                                hypoxia\n",
-      " 7736  Q13631670  Q588723    Passenger rail transport in China                rail transport in China\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tabulate(comp.sample(n=20, random_state=13), headers = 'keys', tablefmt = 'simple'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "future-peace",
-   "metadata": {},
-   "source": [
-    "# Determine Top Parents based on transitive child count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "attempted-calgary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import gzip\n",
-    "\n",
-    "p279StarDF = pd.read_csv('../../wd-correctness/wikidata-20210215/derived.P279star.tsv.gz', sep='\\t', compression='gzip')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "blocked-monitor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts = p279StarDF.node2.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "informal-destruction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts = p279StarDF_node2_counts.sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "mathematical-mandate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts_dict = p279StarDF_node2_counts.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "median-details",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Q35120       2461316\n",
-       "Q99527517    2254480\n",
-       "Q28813620    1363013\n",
-       "Q16887380    1362538\n",
-       "Q488383      1286312\n",
-       "Q20937557    1267164\n",
-       "Q58415929    1201300\n",
-       "Q4406616     1175217\n",
-       "Q223557      1164930\n",
-       "Q6671777     1122346\n",
-       "Q337060      1079449\n",
-       "Q7184903     1072421\n",
-       "Q219858      1057006\n",
-       "Q50365914    1056956\n",
-       "Q23958946    1038346\n",
-       "Q97669203    1007383\n",
-       "Q15712714    1007381\n",
-       "Q3511065     1007335\n",
-       "Q3771876     1004686\n",
-       "Q37500013    1004683\n",
-       "Name: node2, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279StarDF_node2_counts[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "beginning-element",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q35120,entity,2461316\n",
-      "Q99527517,collection entity,2254480\n",
-      "Q28813620,set,1363013\n",
-      "Q16887380,group,1362538\n",
-      "Q488383,object,1286312\n",
-      "Q20937557,series,1267164\n",
-      "Q58415929,spatio-temporal entity,1201300\n",
-      "Q4406616,concrete object,1175217\n",
-      "Q223557,physical object,1164930\n",
-      "Q6671777,structure,1122346\n",
-      "Q337060,perceptible object,1079449\n",
-      "Q7184903,abstract object,1072421\n",
-      "Q219858,zone,1057006\n",
-      "Q50365914,biological region,1056956\n",
-      "Q23958946,individual entity,1038346\n",
-      "Q97669203,molecular conformation,1007383\n",
-      "Q15712714,biomolecular structure,1007381\n",
-      "Q3511065,biological sequence,1007335\n",
-      "Q3771876,nucleic acid structure,1004686\n",
-      "Q37500013,primary structure,1004683\n",
-      "Q863908,nucleic acid sequence,1004680\n",
-      "Q7187,gene,1004634\n",
-      "Q103940464,continuant,1003328\n",
-      "Q53617489,independent continuant,1003327\n",
-      "Q27043950,anatomical entity,1003325\n",
-      "Q28732711,physical substance,977526\n",
-      "Q4503801,part and whole,969424\n",
-      "Q1310239,component,960112\n",
-      "Q20747295,protein-coding gene,942018\n",
-      "Q58416391,spatial entity,927246\n",
-      "Q43460564,chemical entity,894604\n",
-      "Q79529,chemical substance,893534\n",
-      "Q28728771,material substance,892254\n",
-      "Q35758,matter,891736\n",
-      "Q66394244,physical anatomical entity,877724\n",
-      "Q5127848,class,875770\n",
-      "Q578779,pure substance,874023\n",
-      "Q19478619,metaclass,873959\n",
-      "Q20026787,chemical component,873903\n",
-      "Q11173,chemical compound,873902\n",
-      "Q29651224,natural object,866276\n",
-      "Q16686022,natural physical object,866273\n",
-      "Q53617407,material entity,865793\n",
-      "Q27043948,material anatomical entity,865792\n",
-      "Q28845870,biological component,862053\n",
-      "Q4936952,anatomical structure,862034\n",
-      "Q1621273,particle,856456\n",
-      "Q46344,quantum,856370\n",
-      "Q28693603,quantum particle,856367\n",
-      "Q2923525,massive particle,856265\n",
-      "Q28877390,massive quantum particle,856264\n",
-      "Q908049,bound state,856239\n",
-      "Q3366856,composite particle,856208\n",
-      "Q2393187,molecular entity,856080\n",
-      "Q2901852,compound of carbon,855799\n",
-      "Q174211,organic compound,855763\n",
-      "Q169336,mixture,853910\n",
-      "Q11369,molecule,853417\n",
-      "Q178593,macromolecule,853231\n",
-      "Q206229,biomolecule,852989\n",
-      "Q81163,polymer,851896\n",
-      "Q422649,biopolymer,851714\n",
-      "Q424689,gene product,851333\n",
-      "Q66560214,biological macromolecule,789641\n",
-      "Q181394,nutrient,788439\n",
-      "Q145273,polyamide,788369\n",
-      "Q8054,protein,788357\n",
-      "Q16686448,artificial entity,309666\n",
-      "Q26907166,temporal entity,281532\n",
-      "Q1190554,occurrence,273428\n",
-      "Q4026292,action,231193\n",
-      "Q15401930,product,227605\n",
-      "Q937228,property,219920\n",
-      "Q8205328,artificial physical object,218567\n",
-      "Q1914636,activity,198404\n",
-      "Q1207505,quality,193010\n",
-      "Q24229398,agent,156590\n",
-      "Q61788060,human activity,148527\n",
-      "Q830077,subject,141105\n",
-      "Q28877,goods,139125\n",
-      "Q1150070,change,134268\n",
-      "Q18336849,item with given name property,133824\n",
-      "Q795052,individual,131442\n",
-      "Q3249551,process,130568\n",
-      "Q2424752,product,129982\n",
-      "Q215627,person,129884\n",
-      "Q4897819,role,129416\n",
-      "Q26720107,subject of a right,128685\n",
-      "Q3778211,legal person,128684\n",
-      "Q7239,organism,125704\n",
-      "Q16889133,class,125538\n",
-      "Q11028,information,122503\n",
-      "Q159344,heterotroph,122144\n",
-      "Q72638,consumer,122136\n",
-      "Q154954,natural person,121570\n",
-      "Q45983014,organisms by adaptation,121568\n",
-      "Q164509,omnivore,121567\n",
-      "Q5,human,121566\n",
-      "Q930933,relation,121018\n",
-      "Q16722960,phenomenon,120253\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "\n",
-    "top_parents = []\n",
-    "\n",
-    "for qnode,qnode_count in zip(p279StarDF_node2_counts[:100].index, p279StarDF_node2_counts[:100]):\n",
-    "    top_parents.append((qnode,requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0],qnode_count))\n",
-    "    print(qnode+','+requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0] + \",\" + str(qnode_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "proud-remainder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_parents = pd.DataFrame(top_parents, columns=['qnode','label','count'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "asian-pleasure",
-   "metadata": {},
-   "source": [
-    "## Analyze Parents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cardiac-prison",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3caff386d7254beab10e799a453273bb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "child_parent_dict = defaultdict(set)\n",
-    "parent_child_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        child_parent_dict[line_s[1]].add(line_s[3])\n",
-    "        parent_child_dict[line_s[3]].add(line_s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "defined-darwin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_depths = {}\n",
-    "min_depths = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "bibliographic-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_max_depth(qnode, depth=0, visited=set()):\n",
-    "    global parent_child_dict\n",
-    "#     print(qnode, depth, visited)\n",
-    "    if qnode not in parent_child_dict:\n",
-    "        return depth\n",
-    "    max_val = depth\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child not in visited:\n",
-    "            if child not in max_depths:\n",
-    "                max_depths[child] = find_max_depth(child, depth+1, visited | {child})\n",
-    "            max_val = max(max_val, max_depths[child])\n",
-    "    return max_val\n",
-    "\n",
-    "def find_min_depth(qnode, depth=0, visited=set()):\n",
-    "    global parent_child_dict\n",
-    "#     print(qnode, depth, visited)\n",
-    "    if qnode not in parent_child_dict:\n",
-    "        return depth\n",
-    "    min_val = float('inf')\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child not in visited:\n",
-    "            if child not in min_depths:\n",
-    "                min_depths[child] = find_min_depth(child, depth+1, visited | {child})\n",
-    "            min_val = min(min_val, min_depths[child])\n",
-    "    if min_val == float('inf'):\n",
-    "        return depth\n",
-    "    else:\n",
-    "        return min_val"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "opponent-vertex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "def entropy_of_children(qnode):\n",
-    "    entropy_sum = 0\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child in p279StarDF_node2_counts_dict:\n",
-    "            assert p279StarDF_node2_counts_dict[child] <= p279StarDF_node2_counts_dict[qnode], (child, qnode)\n",
-    "            entropy_sum -= p279StarDF_node2_counts_dict[child] / p279StarDF_node2_counts_dict[qnode] * np.log2(p279StarDF_node2_counts_dict[child] / p279StarDF_node2_counts_dict[qnode])\n",
-    "    return float(entropy_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "widespread-storage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_reduction_ratio(qnode):\n",
-    "    return [p279StarDF_node2_counts_dict[qnode] / p279StarDF_node2_counts_dict[parent] for parent in child_parent_dict[qnode]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "exciting-representation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e0f343423654e6d93249f3f454fae38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newRows = []\n",
-    "for _, row in tqdm(top_parents.iterrows()):\n",
-    "    row['child_count'] = len(parent_child_dict[row['qnode']])\n",
-    "    row['max_no_of_siblings'] = max([len(parent_child_dict[parent]) - 1 for parent in child_parent_dict[row['qnode']]]) if child_parent_dict[row['qnode']] else 0\n",
-    "    row['min_no_of_siblings'] = min([len(parent_child_dict[parent]) - 1 for parent in child_parent_dict[row['qnode']]]) if child_parent_dict[row['qnode']] else 0\n",
-    "    row['max_depth'] = find_max_depth(row['qnode'])\n",
-    "    row['min_depth'] = find_min_depth(row['qnode'])\n",
-    "    row['entropy'] = entropy_of_children(row['qnode'])\n",
-    "    row['red_ratios'] = get_reduction_ratio(row['qnode'])\n",
-    "    newRows.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "lucky-teens",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Q15401930', 'Q28877', 'Q8205328'}"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parent_child_dict['Q2424752']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "numerical-sharp",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intense-bibliography",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blessed-biodiversity",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "common-cooking",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "chinese-competition",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "solid-final",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "known-logan",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "careful-linux",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>qnode</th>\n",
-       "      <th>label</th>\n",
-       "      <th>count</th>\n",
-       "      <th>child_count</th>\n",
-       "      <th>max_no_of_siblings</th>\n",
-       "      <th>min_no_of_siblings</th>\n",
-       "      <th>max_depth</th>\n",
-       "      <th>min_depth</th>\n",
-       "      <th>entropy</th>\n",
-       "      <th>red_ratios</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q35120</td>\n",
-       "      <td>entity</td>\n",
-       "      <td>2461316</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.986354</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99527517</td>\n",
-       "      <td>collection entity</td>\n",
-       "      <td>2254480</td>\n",
-       "      <td>6</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>29</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.201021</td>\n",
-       "      <td>[0.9159652803622127]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q28813620</td>\n",
-       "      <td>set</td>\n",
-       "      <td>1363013</td>\n",
-       "      <td>41</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.035319</td>\n",
-       "      <td>[0.6045797700578404]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q16887380</td>\n",
-       "      <td>group</td>\n",
-       "      <td>1362538</td>\n",
-       "      <td>147</td>\n",
-       "      <td>40</td>\n",
-       "      <td>40</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.786249</td>\n",
-       "      <td>[0.999651507359064]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q488383</td>\n",
-       "      <td>object</td>\n",
-       "      <td>1286312</td>\n",
-       "      <td>56</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.487792</td>\n",
-       "      <td>[0.5226114810125965]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>Q45983014</td>\n",
-       "      <td>organisms by adaptation</td>\n",
-       "      <td>121568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>146</td>\n",
-       "      <td>146</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>[0.08922173179757188]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>Q164509</td>\n",
-       "      <td>omnivore</td>\n",
-       "      <td>121567</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>[0.9953412589244777, 0.9999917741510924]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q5</td>\n",
-       "      <td>human</td>\n",
-       "      <td>121566</td>\n",
-       "      <td>254</td>\n",
-       "      <td>542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.311051</td>\n",
-       "      <td>[0.9999917740834272, 0.9999670971456774, 0.935...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q930933</td>\n",
-       "      <td>relation</td>\n",
-       "      <td>121018</td>\n",
-       "      <td>56</td>\n",
-       "      <td>44</td>\n",
-       "      <td>44</td>\n",
-       "      <td>29</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.937792</td>\n",
-       "      <td>[0.11284560820797056]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>Q16722960</td>\n",
-       "      <td>phenomenon</td>\n",
-       "      <td>120253</td>\n",
-       "      <td>9</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.341837</td>\n",
-       "      <td>[0.04885719671915349]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        qnode                    label    count  child_count  \\\n",
-       "0      Q35120                   entity  2461316           60   \n",
-       "1   Q99527517        collection entity  2254480            6   \n",
-       "2   Q28813620                      set  1363013           41   \n",
-       "3   Q16887380                    group  1362538          147   \n",
-       "4     Q488383                   object  1286312           56   \n",
-       "..        ...                      ...      ...          ...   \n",
-       "95  Q45983014  organisms by adaptation   121568            1   \n",
-       "96    Q164509                 omnivore   121567            1   \n",
-       "97         Q5                    human   121566          254   \n",
-       "98    Q930933                 relation   121018           56   \n",
-       "99  Q16722960               phenomenon   120253            9   \n",
-       "\n",
-       "    max_no_of_siblings  min_no_of_siblings  max_depth  min_depth   entropy  \\\n",
-       "0                    0                   0         29          1  4.986354   \n",
-       "1                   59                  59         29          2  1.201021   \n",
-       "2                    5                   5         25          2  0.035319   \n",
-       "3                   40                  40         25          2  0.786249   \n",
-       "4                   59                  59         29          1  1.487792   \n",
-       "..                 ...                 ...        ...        ...       ...   \n",
-       "95                 146                 146         21          2  0.000012   \n",
-       "96                   5                   0         21          2  0.000012   \n",
-       "97                 542                   0         21          2  0.311051   \n",
-       "98                  44                  44         29          2  1.937792   \n",
-       "99                  59                  59         25          2  0.341837   \n",
-       "\n",
-       "                                           red_ratios  \n",
-       "0                                                  []  \n",
-       "1                                [0.9159652803622127]  \n",
-       "2                                [0.6045797700578404]  \n",
-       "3                                 [0.999651507359064]  \n",
-       "4                                [0.5226114810125965]  \n",
-       "..                                                ...  \n",
-       "95                              [0.08922173179757188]  \n",
-       "96           [0.9953412589244777, 0.9999917741510924]  \n",
-       "97  [0.9999917740834272, 0.9999670971456774, 0.935...  \n",
-       "98                              [0.11284560820797056]  \n",
-       "99                              [0.04885719671915349]  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(newRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "interested-reply",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(newRows).to_csv('../data/Analogical_Dataset/top_parents_analysis.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sporting-radius",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "pacific-hughes",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "needed-grenada",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordinary-schema",
-   "metadata": {},
-   "source": [
-    "# Determine if training file has common parent or common ancestor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "anonymous-penalty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "comp = pd.read_csv('../data/compositional.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "statutory-supplier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compDict = {row['node1']: row['node2'] for _, row in comp.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "legal-handy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDF = pd.read_csv('../data/Analogical_Dataset/Analogical_Test_File.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "democratic-hebrew",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q444321</td>\n",
-       "      <td>Q2009740</td>\n",
-       "      <td>telephone keybad</td>\n",
-       "      <td>keypad</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q807980</td>\n",
-       "      <td>Q82744</td>\n",
-       "      <td>barcode reader</td>\n",
-       "      <td>image scanner</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2914650</td>\n",
-       "      <td>Q7163</td>\n",
-       "      <td>identity politics</td>\n",
-       "      <td>politics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q4259813</td>\n",
-       "      <td>Q9174</td>\n",
-       "      <td>liberal religion</td>\n",
-       "      <td>religion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q10404171</td>\n",
-       "      <td>Q186005</td>\n",
-       "      <td>thermal therapy</td>\n",
-       "      <td>physiotherapy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    node1     node2        node1_label node2_label  relation other_node1  \\\n",
-       "25   Q250  Q1921606  computer keyboard    keyboard   PURPOSE     Q444321   \n",
-       "26   Q250  Q1921606  computer keyboard    keyboard   PURPOSE     Q807980   \n",
-       "27  Q7991      Q336    natural science     science  PROPERTY    Q2914650   \n",
-       "28  Q7991      Q336    natural science     science  PROPERTY    Q4259813   \n",
-       "29  Q7991      Q336    natural science     science  PROPERTY   Q10404171   \n",
-       "\n",
-       "   other_node2  other_node1_label other_node2_label other_relation  \\\n",
-       "25    Q2009740   telephone keybad            keypad        PURPOSE   \n",
-       "26      Q82744     barcode reader     image scanner        PURPOSE   \n",
-       "27       Q7163  identity politics          politics       PROPERTY   \n",
-       "28       Q9174   liberal religion          religion       PROPERTY   \n",
-       "29     Q186005    thermal therapy     physiotherapy       PROPERTY   \n",
-       "\n",
-       "            type  \n",
-       "25  POS_SAME_PAR  \n",
-       "26  POS_SAME_PAR  \n",
-       "27  POS_SAME_PAR  \n",
-       "28  POS_SAME_PAR  \n",
-       "29  POS_SAME_PAR  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDF[trainingDF.type == 'POS_SAME_PAR'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "scientific-charles",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDFSubset = trainingDF[trainingDF.type == 'POS_SAME_PAR']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "moral-shaft",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "      <th>Unnamed: 11</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q444321</td>\n",
-       "      <td>Q2009740</td>\n",
-       "      <td>telephone keybad</td>\n",
-       "      <td>keypad</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q54854916</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>typewriter keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q807980</td>\n",
-       "      <td>Q82744</td>\n",
-       "      <td>barcode reader</td>\n",
-       "      <td>image scanner</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2914650</td>\n",
-       "      <td>Q7163</td>\n",
-       "      <td>identity politics</td>\n",
-       "      <td>politics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q1637030</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>food science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q4259813</td>\n",
-       "      <td>Q9174</td>\n",
-       "      <td>liberal religion</td>\n",
-       "      <td>religion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q16387</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>information science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q10404171</td>\n",
-       "      <td>Q186005</td>\n",
-       "      <td>thermal therapy</td>\n",
-       "      <td>physiotherapy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1     node2          node1_label node2_label  relation  \\\n",
-       "25       Q250  Q1921606    computer keyboard    keyboard   PURPOSE   \n",
-       "26  Q54854916  Q1921606  typewriter keyboard    keyboard   PURPOSE   \n",
-       "27      Q7991      Q336      natural science     science  PROPERTY   \n",
-       "28   Q1637030      Q336         food science     science  PROPERTY   \n",
-       "29     Q16387      Q336  information science     science  PROPERTY   \n",
-       "\n",
-       "   other_node1 other_node2  other_node1_label other_node2_label  \\\n",
-       "25     Q444321    Q2009740   telephone keybad            keypad   \n",
-       "26     Q807980      Q82744     barcode reader     image scanner   \n",
-       "27    Q2914650       Q7163  identity politics          politics   \n",
-       "28    Q4259813       Q9174   liberal religion          religion   \n",
-       "29   Q10404171     Q186005    thermal therapy     physiotherapy   \n",
-       "\n",
-       "   other_relation          type Unnamed: 11  \n",
-       "25        PURPOSE  POS_SAME_PAR         NaN  \n",
-       "26        PURPOSE  POS_SAME_PAR         NaN  \n",
-       "27       PROPERTY  POS_SAME_PAR         NaN  \n",
-       "28       PROPERTY  POS_SAME_PAR         NaN  \n",
-       "29       PROPERTY  POS_SAME_PAR         NaN  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "competitive-medication",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-44-dd012d3407e6>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  trainingDFSubset['pair1_exists'] = trainingDFSubset.apply(checkIfExists, args=('node1','node2'), axis=1)\n",
-      "<ipython-input-44-dd012d3407e6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  trainingDFSubset['pair2_exists'] = trainingDFSubset.apply(checkIfExists, args=('other_node1','other_node2'), axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def checkIfExists(row, node1_col, node2_col):\n",
-    "    return row[node1_col] in compDict and row[node2_col] == compDict[row[node1_col]]\n",
-    "trainingDFSubset['pair1_exists'] = trainingDFSubset.apply(checkIfExists, args=('node1','node2'), axis=1)\n",
-    "trainingDFSubset['pair2_exists'] = trainingDFSubset.apply(checkIfExists, args=('other_node1','other_node2'), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "wrapped-softball",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True     35\n",
-       "False    15\n",
-       "Name: pair2_exists, dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset['pair2_exists'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "physical-fireplace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25   -1\n",
-       "26   -1\n",
-       "27   -1\n",
-       "28   -1\n",
-       "29   -1\n",
-       "30   -1\n",
-       "31   -1\n",
-       "32   -1\n",
-       "33   -1\n",
-       "34   -1\n",
-       "35   -1\n",
-       "36   -1\n",
-       "37   -1\n",
-       "38   -1\n",
-       "39   -1\n",
-       "40   -1\n",
-       "41   -1\n",
-       "42   -1\n",
-       "43   -1\n",
-       "44   -1\n",
-       "45   -1\n",
-       "46   -1\n",
-       "47   -1\n",
-       "48   -1\n",
-       "49   -1\n",
-       "50   -1\n",
-       "51   -1\n",
-       "52   -1\n",
-       "53   -1\n",
-       "54   -1\n",
-       "55   -1\n",
-       "56   -1\n",
-       "57   -1\n",
-       "58   -1\n",
-       "59   -1\n",
-       "60   -1\n",
-       "61   -1\n",
-       "62   -1\n",
-       "63   -1\n",
-       "64   -1\n",
-       "65   -1\n",
-       "66   -1\n",
-       "67   -1\n",
-       "68   -1\n",
-       "69   -1\n",
-       "70   -1\n",
-       "71   -1\n",
-       "72   -1\n",
-       "73   -1\n",
-       "74   -1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def checkIfCommonParent(row):\n",
-    "    if row['node2'] not in compDict or row['other_node2'] not in compDict:\n",
-    "        return -1\n",
-    "    elif compDict[row['node2']] == compDict[row['other_node2']]:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0\n",
-    "trainingDFSubset.apply(checkIfCommonParent, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "according-shark",
-   "metadata": {},
-   "source": [
-    "# Same Ancestor Subset Top Parent Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "hourly-december",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDF = pd.read_csv('../data/Analogical_Dataset/Analogical_Test_File.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "alternate-bookmark",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDFSubset = trainingDF[trainingDF.type == 'POS_SAME_ANC']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "existing-gauge",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "      <th>Verified</th>\n",
-       "      <th>comment</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>pair1_ancestor</th>\n",
-       "      <th>pair2_ancestor</th>\n",
-       "      <th>top_pars_list</th>\n",
-       "      <th>Unnamed: 18</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q7708416</td>\n",
-       "      <td>Q327065</td>\n",
-       "      <td>Text display</td>\n",
-       "      <td>display device</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1921606:keyboard|Q864114:input device|Q47146:...</td>\n",
-       "      <td>Q327065:display device|Q778637:output device|Q...</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q1140700</td>\n",
-       "      <td>Q11635</td>\n",
-       "      <td>improvisational theatre</td>\n",
-       "      <td>theatre</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q336:science|Q11862829:academic discipline|Q10...</td>\n",
-       "      <td>Q11635:theater|Q184485:performing arts|Q201852...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>Q16387</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>information science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q107181871</td>\n",
-       "      <td>Q11032</td>\n",
-       "      <td>manuscript newspaper</td>\n",
-       "      <td>newspaper</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q336:science|Q11862829:academic discipline|Q10...</td>\n",
-       "      <td>Q11032:newspaper|Q1002697:periodical|Q234460:t...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>Q9192</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Mandarin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q1122452</td>\n",
-       "      <td>Q29536</td>\n",
-       "      <td>Germanic paganism</td>\n",
-       "      <td>paganism</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q7850:Chinese|Q33857:Sinitic languages|Q45961:...</td>\n",
-       "      <td>Q29536:paganism|Q9174:religion|Q3702971:person...</td>\n",
-       "      <td>entity,object,abstract object,property</td>\n",
-       "      <td>entity,object,abstract object,structure,proper...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>Q56479</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Jin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q702065</td>\n",
-       "      <td>Q9134</td>\n",
-       "      <td>Korean mythology</td>\n",
-       "      <td>mythology</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q7850:Chinese|Q33857:Sinitic languages|Q45961:...</td>\n",
-       "      <td>Q9134:mythology|Q49447:world view|Q20978643:po...</td>\n",
-       "      <td>entity,object,abstract object,property</td>\n",
-       "      <td>entity,object,abstract object,property,relatio...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>OWNERSHIP</td>\n",
-       "      <td>Q106907987</td>\n",
-       "      <td>Q106907993</td>\n",
-       "      <td>mammalian oocyte</td>\n",
-       "      <td>vertebrate oocyte</td>\n",
-       "      <td>OWNERSHIP</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>Q5856</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>synthetic fiber</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q16676963</td>\n",
-       "      <td>Q6584340</td>\n",
-       "      <td>mint syrup</td>\n",
-       "      <td>syrup</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q161:fiber|Q214609:material|Q2424752:product</td>\n",
-       "      <td>Q6584340:syrup|Q40050:drink|Q2095:food|Q119405...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>Q4543</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>beach volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q55491</td>\n",
-       "      <td>Q55488</td>\n",
-       "      <td>underground railway station</td>\n",
-       "      <td>railway station</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1734:volleyball|Q877517:ball game|Q4438147:sp...</td>\n",
-       "      <td>Q55488:railway station|Q1318558:operation poin...</td>\n",
-       "      <td>entity,object,action,phenomenon</td>\n",
-       "      <td>entity,object,physical object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>Q15728017</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>snow volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q99689267</td>\n",
-       "      <td>Q3305213</td>\n",
-       "      <td>leather painting</td>\n",
-       "      <td>painting</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1734:volleyball|Q877517:ball game|Q4438147:sp...</td>\n",
-       "      <td>Q3305213:painting|Q4502142:visual artwork|Q838...</td>\n",
-       "      <td>entity,object,action,phenomenon</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>Q9778</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>electronic music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q20358335</td>\n",
-       "      <td>Q8261</td>\n",
-       "      <td>political novel</td>\n",
-       "      <td>novel</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q638:music|Q735:art|Q2018526:arts|Q80083:human...</td>\n",
-       "      <td>Q8261:novel|Q571:book|Q49848:document|Q234460:...</td>\n",
-       "      <td>entity,object,abstract object,structure,action</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Q1045756</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>microtonal music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q5707594</td>\n",
-       "      <td>Q191067</td>\n",
-       "      <td>news article</td>\n",
-       "      <td>article</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q638:music|Q735:art|Q2018526:arts|Q80083:human...</td>\n",
-       "      <td>Q191067:article|Q234460:text|Q11028:informatio...</td>\n",
-       "      <td>entity,object,abstract object,structure,action</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>Q699</td>\n",
-       "      <td>Q17991521</td>\n",
-       "      <td>fairy tale</td>\n",
-       "      <td>tale</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q58079064</td>\n",
-       "      <td>Q2977</td>\n",
-       "      <td>protestant cathedral</td>\n",
-       "      <td>cathedral</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q17991521:tale|Q1318295:narrative|Q17537576:cr...</td>\n",
-       "      <td>Q2977:cathedral|Q16970:church building|Q137059...</td>\n",
-       "      <td>entity,object</td>\n",
-       "      <td>entity,object,physical object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>Q9585</td>\n",
-       "      <td>Q432</td>\n",
-       "      <td>Shia Islam</td>\n",
-       "      <td>Islam</td>\n",
-       "      <td>INSTANCE</td>\n",
-       "      <td>Q4775051</td>\n",
-       "      <td>Q132203</td>\n",
-       "      <td>Antiochian Greeks</td>\n",
-       "      <td>Greek diaspora</td>\n",
-       "      <td>INSTANCE</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,abstract object,structure,proper...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Q6786</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>bottom quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q55511397</td>\n",
-       "      <td>Q107968</td>\n",
-       "      <td>monoatomic anion</td>\n",
-       "      <td>anion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q6718:quark|Q52234516:elementary fermion|Q4311...</td>\n",
-       "      <td>Q107968:anion|Q36496:ion|Q2393187:molecular en...</td>\n",
-       "      <td>entity,object,physical object,particle</td>\n",
-       "      <td>entity,object,physical object,particle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>Q7141</td>\n",
-       "      <td>Q420</td>\n",
-       "      <td>cell biology</td>\n",
-       "      <td>biology</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q81197</td>\n",
-       "      <td>Q413</td>\n",
-       "      <td>nuclear physics</td>\n",
-       "      <td>physics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q420:biology|Q7991:natural science|Q336:scienc...</td>\n",
-       "      <td>Q413:physics|Q14632398:physical sciences|Q7991...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>Q5860</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>carbon fibers</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q5567094</td>\n",
-       "      <td>Q80228</td>\n",
-       "      <td>glass bottle</td>\n",
-       "      <td>bottle</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q161:fiber|Q214609:material|Q2424752:product</td>\n",
-       "      <td>Q80228:bottle|Q16896124:bin|Q987767:container|...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,structure,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>Q6311</td>\n",
-       "      <td>Q8104</td>\n",
-       "      <td>stellar atmosphere</td>\n",
-       "      <td>atmosphere</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q1037499</td>\n",
-       "      <td>Q36133</td>\n",
-       "      <td>lunar soil</td>\n",
-       "      <td>soil</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q8104:atmosphere|Q47495022:shell of an astrono...</td>\n",
-       "      <td>Q36133:soil|Q1155083:granular material|Q214609...</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>Q426</td>\n",
-       "      <td>Q2386606</td>\n",
-       "      <td>animal rights</td>\n",
-       "      <td>right</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2845837</td>\n",
-       "      <td>Q11210</td>\n",
-       "      <td>tripolar coordinates</td>\n",
-       "      <td>coordinate system</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q2386606:right|Q7748:law|Q1151067:rule|Q268459...</td>\n",
-       "      <td>Q11210:coordinate system|Q184876:frame of refe...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object,structure,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q3436733</td>\n",
-       "      <td>Q50000</td>\n",
-       "      <td>decisive victory</td>\n",
-       "      <td>victory</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q186361:punishment|Q2995644:result|Q733541:con...</td>\n",
-       "      <td>Q50000:victory|Q2995644:result|Q733541:consequ...</td>\n",
-       "      <td>entity,action</td>\n",
-       "      <td>entity,property</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>Q4812</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>day sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>Q9182675</td>\n",
-       "      <td>Q1107656</td>\n",
-       "      <td>perennial garden</td>\n",
-       "      <td>garden</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q527:sky|Q107:space|Q3054889:measurable set|Q3...</td>\n",
-       "      <td>Q1107656:garden|Q35145743:human-made landform|...</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>Q318887</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>evening sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>Q18354756</td>\n",
-       "      <td>Q344</td>\n",
-       "      <td>far future</td>\n",
-       "      <td>future</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q527:sky|Q107:space|Q3054889:measurable set|Q3...</td>\n",
-       "      <td>Q344:future|Q11471:time|Q3054889:measurable se...</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>Q104857623</td>\n",
-       "      <td>Q374</td>\n",
-       "      <td>raspberry vodka</td>\n",
-       "      <td>vodka</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q1757352</td>\n",
-       "      <td>Q13317</td>\n",
-       "      <td>soy yogurt</td>\n",
-       "      <td>yogurt</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q374:vodka|Q56139:liquor|Q17562878:spirit drin...</td>\n",
-       "      <td>Q281:whisky|Q1479546:grain spirit|Q56139:liquo...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q7076</td>\n",
-       "      <td>Q6199</td>\n",
-       "      <td>individualist anarchism</td>\n",
-       "      <td>anarchism</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2300258</td>\n",
-       "      <td>Q200726</td>\n",
-       "      <td>conditional probability distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q380528</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>color charge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q184550</td>\n",
-       "      <td>Q11379</td>\n",
-       "      <td>mechanical energy</td>\n",
-       "      <td>energy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q73792:charge|Q4373292:physical property|Q9372...</td>\n",
-       "      <td>Q11379:energy|Q107715:physical quantity|Q30931...</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>Q1364131</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>weak hypercharge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q240105</td>\n",
-       "      <td>Q11465</td>\n",
-       "      <td>radial velocity</td>\n",
-       "      <td>velocity</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q73792:charge|Q4373292:physical property|Q9372...</td>\n",
-       "      <td>Q11465:velocity|Q107715:physical quantity|Q309...</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         node1      node2              node1_label node2_label   relation  \\\n",
-       "75        Q250   Q1921606        computer keyboard    keyboard    PURPOSE   \n",
-       "76       Q7991       Q336          natural science     science   PROPERTY   \n",
-       "77      Q16387       Q336      information science     science   PROPERTY   \n",
-       "78       Q9192      Q7850         Mandarin Chinese     Chinese   LOCATION   \n",
-       "79      Q56479      Q7850              Jin Chinese     Chinese   LOCATION   \n",
-       "80        Q561       Q553              human tooth       tooth  OWNERSHIP   \n",
-       "81       Q5856       Q161          synthetic fiber       fiber   MATERIAL   \n",
-       "82       Q4543      Q1734         beach volleyball  volleyball   LOCATION   \n",
-       "83   Q15728017      Q1734          snow volleyball  volleyball   LOCATION   \n",
-       "84       Q9778       Q638         electronic music       music   PROPERTY   \n",
-       "85    Q1045756       Q638         microtonal music       music   PROPERTY   \n",
-       "86        Q699  Q17991521               fairy tale        tale   PROPERTY   \n",
-       "87       Q9585       Q432               Shia Islam       Islam   INSTANCE   \n",
-       "88       Q6786      Q6718             bottom quark       quark   PROPERTY   \n",
-       "89       Q7141       Q420             cell biology     biology   PROPERTY   \n",
-       "90       Q5860       Q161            carbon fibers       fiber   MATERIAL   \n",
-       "91       Q6311      Q8104       stellar atmosphere  atmosphere   LOCATION   \n",
-       "92        Q426   Q2386606            animal rights       right   PROPERTY   \n",
-       "93        Q305    Q186361      corporal punishment  punishment   PROPERTY   \n",
-       "94       Q4812       Q527                  day sky         sky   TEMPORAL   \n",
-       "95     Q318887       Q527              evening sky         sky   TEMPORAL   \n",
-       "96  Q104857623       Q374          raspberry vodka       vodka   MATERIAL   \n",
-       "97       Q7076      Q6199  individualist anarchism   anarchism   PROPERTY   \n",
-       "98     Q380528     Q73792             color charge      charge   PROPERTY   \n",
-       "99    Q1364131     Q73792         weak hypercharge      charge   PROPERTY   \n",
-       "\n",
-       "   other_node1 other_node2                     other_node1_label  \\\n",
-       "75    Q7708416     Q327065                          Text display   \n",
-       "76    Q1140700      Q11635               improvisational theatre   \n",
-       "77  Q107181871      Q11032                  manuscript newspaper   \n",
-       "78    Q1122452      Q29536                     Germanic paganism   \n",
-       "79     Q702065       Q9134                      Korean mythology   \n",
-       "80  Q106907987  Q106907993                      mammalian oocyte   \n",
-       "81   Q16676963    Q6584340                            mint syrup   \n",
-       "82      Q55491      Q55488           underground railway station   \n",
-       "83   Q99689267    Q3305213                      leather painting   \n",
-       "84   Q20358335       Q8261                       political novel   \n",
-       "85    Q5707594     Q191067                          news article   \n",
-       "86   Q58079064       Q2977                  protestant cathedral   \n",
-       "87    Q4775051     Q132203                     Antiochian Greeks   \n",
-       "88   Q55511397     Q107968                      monoatomic anion   \n",
-       "89      Q81197        Q413                      nuclear physics    \n",
-       "90    Q5567094      Q80228                          glass bottle   \n",
-       "91    Q1037499      Q36133                            lunar soil   \n",
-       "92    Q2845837      Q11210                  tripolar coordinates   \n",
-       "93    Q3436733      Q50000                      decisive victory   \n",
-       "94    Q9182675    Q1107656                      perennial garden   \n",
-       "95   Q18354756        Q344                            far future   \n",
-       "96    Q1757352      Q13317                            soy yogurt   \n",
-       "97    Q2300258     Q200726  conditional probability distribution   \n",
-       "98     Q184550      Q11379                     mechanical energy   \n",
-       "99     Q240105      Q11465                       radial velocity   \n",
-       "\n",
-       "           other_node2_label other_relation          type  Verified comment  \\\n",
-       "75            display device        PURPOSE  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "76                   theatre       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "77                 newspaper       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "78                  paganism       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "79                 mythology       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "80         vertebrate oocyte      OWNERSHIP  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "81                     syrup       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "82           railway station       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "83                  painting       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "84                     novel       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "85                   article       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "86                 cathedral       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "87            Greek diaspora       INSTANCE  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "88                     anion       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "89                   physics       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "90                    bottle       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "91                      soil       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "92         coordinate system       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "93                   victory       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "94                    garden       TEMPORAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "95                    future       TEMPORAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "96                    yogurt       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "97  probability distribution       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "98                    energy       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "99                  velocity       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "\n",
-       "   par_node par_label                                     pair1_ancestor  \\\n",
-       "75      NaN       NaN  Q1921606:keyboard|Q864114:input device|Q47146:...   \n",
-       "76      NaN       NaN  Q336:science|Q11862829:academic discipline|Q10...   \n",
-       "77      NaN       NaN  Q336:science|Q11862829:academic discipline|Q10...   \n",
-       "78      NaN       NaN  Q7850:Chinese|Q33857:Sinitic languages|Q45961:...   \n",
-       "79      NaN       NaN  Q7850:Chinese|Q33857:Sinitic languages|Q45961:...   \n",
-       "80      NaN       NaN                                                NaN   \n",
-       "81      NaN       NaN       Q161:fiber|Q214609:material|Q2424752:product   \n",
-       "82      NaN       NaN  Q1734:volleyball|Q877517:ball game|Q4438147:sp...   \n",
-       "83      NaN       NaN  Q1734:volleyball|Q877517:ball game|Q4438147:sp...   \n",
-       "84      NaN       NaN  Q638:music|Q735:art|Q2018526:arts|Q80083:human...   \n",
-       "85      NaN       NaN  Q638:music|Q735:art|Q2018526:arts|Q80083:human...   \n",
-       "86      NaN       NaN  Q17991521:tale|Q1318295:narrative|Q17537576:cr...   \n",
-       "87      NaN       NaN                                                NaN   \n",
-       "88      NaN       NaN  Q6718:quark|Q52234516:elementary fermion|Q4311...   \n",
-       "89      NaN       NaN  Q420:biology|Q7991:natural science|Q336:scienc...   \n",
-       "90      NaN       NaN       Q161:fiber|Q214609:material|Q2424752:product   \n",
-       "91      NaN       NaN  Q8104:atmosphere|Q47495022:shell of an astrono...   \n",
-       "92      NaN       NaN  Q2386606:right|Q7748:law|Q1151067:rule|Q268459...   \n",
-       "93      NaN       NaN  Q186361:punishment|Q2995644:result|Q733541:con...   \n",
-       "94      NaN       NaN  Q527:sky|Q107:space|Q3054889:measurable set|Q3...   \n",
-       "95      NaN       NaN  Q527:sky|Q107:space|Q3054889:measurable set|Q3...   \n",
-       "96      NaN       NaN  Q374:vodka|Q56139:liquor|Q17562878:spirit drin...   \n",
-       "97      NaN       NaN                                                NaN   \n",
-       "98      NaN       NaN  Q73792:charge|Q4373292:physical property|Q9372...   \n",
-       "99      NaN       NaN  Q73792:charge|Q4373292:physical property|Q9372...   \n",
-       "\n",
-       "                                       pair2_ancestor  \\\n",
-       "75  Q327065:display device|Q778637:output device|Q...   \n",
-       "76  Q11635:theater|Q184485:performing arts|Q201852...   \n",
-       "77  Q11032:newspaper|Q1002697:periodical|Q234460:t...   \n",
-       "78  Q29536:paganism|Q9174:religion|Q3702971:person...   \n",
-       "79  Q9134:mythology|Q49447:world view|Q20978643:po...   \n",
-       "80                                                NaN   \n",
-       "81  Q6584340:syrup|Q40050:drink|Q2095:food|Q119405...   \n",
-       "82  Q55488:railway station|Q1318558:operation poin...   \n",
-       "83  Q3305213:painting|Q4502142:visual artwork|Q838...   \n",
-       "84  Q8261:novel|Q571:book|Q49848:document|Q234460:...   \n",
-       "85  Q191067:article|Q234460:text|Q11028:informatio...   \n",
-       "86  Q2977:cathedral|Q16970:church building|Q137059...   \n",
-       "87                                                NaN   \n",
-       "88  Q107968:anion|Q36496:ion|Q2393187:molecular en...   \n",
-       "89  Q413:physics|Q14632398:physical sciences|Q7991...   \n",
-       "90  Q80228:bottle|Q16896124:bin|Q987767:container|...   \n",
-       "91  Q36133:soil|Q1155083:granular material|Q214609...   \n",
-       "92  Q11210:coordinate system|Q184876:frame of refe...   \n",
-       "93  Q50000:victory|Q2995644:result|Q733541:consequ...   \n",
-       "94  Q1107656:garden|Q35145743:human-made landform|...   \n",
-       "95  Q344:future|Q11471:time|Q3054889:measurable se...   \n",
-       "96  Q281:whisky|Q1479546:grain spirit|Q56139:liquo...   \n",
-       "97                                                NaN   \n",
-       "98  Q11379:energy|Q107715:physical quantity|Q30931...   \n",
-       "99  Q11465:velocity|Q107715:physical quantity|Q309...   \n",
-       "\n",
-       "                                        top_pars_list  \\\n",
-       "75  entity,object,physical object,abstract object,...   \n",
-       "76                      entity,object,abstract object   \n",
-       "77                      entity,object,abstract object   \n",
-       "78             entity,object,abstract object,property   \n",
-       "79             entity,object,abstract object,property   \n",
-       "80                      entity,object,physical object   \n",
-       "81              entity,object,physical object,product   \n",
-       "82                    entity,object,action,phenomenon   \n",
-       "83                    entity,object,action,phenomenon   \n",
-       "84     entity,object,abstract object,structure,action   \n",
-       "85     entity,object,abstract object,structure,action   \n",
-       "86                                      entity,object   \n",
-       "87  entity,object,abstract object,structure,proper...   \n",
-       "88             entity,object,physical object,particle   \n",
-       "89                      entity,object,abstract object   \n",
-       "90              entity,object,physical object,product   \n",
-       "91                      entity,object,physical object   \n",
-       "92                      entity,object,abstract object   \n",
-       "93                                      entity,action   \n",
-       "94             entity,object,abstract object,relation   \n",
-       "95             entity,object,abstract object,relation   \n",
-       "96              entity,object,physical object,product   \n",
-       "97                                                NaN   \n",
-       "98    entity,object,abstract object,property,relation   \n",
-       "99    entity,object,abstract object,property,relation   \n",
-       "\n",
-       "                                          Unnamed: 18  \n",
-       "75  entity,object,physical object,abstract object,...  \n",
-       "76            entity,object,abstract object,structure  \n",
-       "77  entity,object,physical object,abstract object,...  \n",
-       "78  entity,object,abstract object,structure,proper...  \n",
-       "79  entity,object,abstract object,property,relatio...  \n",
-       "80                                                NaN  \n",
-       "81              entity,object,physical object,product  \n",
-       "82            entity,object,physical object,structure  \n",
-       "83                      entity,object,physical object  \n",
-       "84  entity,object,physical object,abstract object,...  \n",
-       "85                      entity,object,abstract object  \n",
-       "86            entity,object,physical object,structure  \n",
-       "87                                                NaN  \n",
-       "88             entity,object,physical object,particle  \n",
-       "89                      entity,object,abstract object  \n",
-       "90    entity,object,physical object,structure,product  \n",
-       "91              entity,object,physical object,product  \n",
-       "92   entity,object,abstract object,structure,relation  \n",
-       "93                                    entity,property  \n",
-       "94  entity,object,physical object,abstract object,...  \n",
-       "95             entity,object,abstract object,relation  \n",
-       "96              entity,object,physical object,product  \n",
-       "97    entity,object,abstract object,property,relation  \n",
-       "98    entity,object,abstract object,property,relation  \n",
-       "99    entity,object,abstract object,property,relation  "
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "running-conspiracy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a178a238123f40b19e775fa110bcaaa6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "subset_nodes = set(trainingDFSubset.node1.to_list() + trainingDFSubset.node2.to_list() + trainingDFSubset.other_node1.to_list() + trainingDFSubset.other_node2.to_list())\n",
-    "parent_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279star.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        if line_s[0] in subset_nodes:\n",
-    "            parent_dict[line_s[0]].add(line_s[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "private-catch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_nodes = {'Q35120': 'entity', 'Q488383': 'object', 'Q223557': 'physical object', 'Q7184903': 'abstract object',\n",
-    "             'Q6671777': 'structure', 'Q219858': 'zone', 'Q1621273': 'particle', 'Q174211': 'organic compound', \n",
-    "             'Q4026292': 'action', 'Q937228': 'property', 'Q2424752': 'product', 'Q215627': 'person', 'Q4897819': 'role',\n",
-    "             'Q7239': 'organism', 'Q930933': 'relation', 'Q16722960': 'phenomenon'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "prepared-senegal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entity',\n",
-       " 'object',\n",
-       " 'physical object',\n",
-       " 'abstract object',\n",
-       " 'structure',\n",
-       " 'zone',\n",
-       " 'particle',\n",
-       " 'organic compound',\n",
-       " 'action',\n",
-       " 'property',\n",
-       " 'product',\n",
-       " 'person',\n",
-       " 'role',\n",
-       " 'organism',\n",
-       " 'relation',\n",
-       " 'phenomenon']"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(top_nodes.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detailed-painting",
-   "metadata": {},
-   "source": [
-    "# Pairwise Top Parent Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "brief-postcard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "369de020404143008c60621efca55090",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "child_parent_dict = defaultdict(set)\n",
-    "parent_child_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        child_parent_dict[line_s[1]].add(line_s[3])\n",
-    "        parent_child_dict[line_s[3]].add(line_s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "careful-divorce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_parent_path(curr, top_parent, path):\n",
-    "    if curr == top_parent:\n",
-    "        return path + [top_parent]\n",
-    "    elif curr not in child_parent_dict:\n",
-    "        return False\n",
-    "    \n",
-    "    for par in child_parent_dict[curr]:\n",
-    "        new_path = find_parent_path(par, top_parent, path + [curr])\n",
-    "        if new_path:\n",
-    "            return new_path\n",
-    "\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "id": "atlantic-cornwall",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_common_top_parents(node1, node2):\n",
-    "    pars1, pars2 = set(), set()\n",
-    "    for top_node in top_nodes:\n",
-    "        if top_node in parent_dict[node1]:\n",
-    "            pars1.add(top_node)\n",
-    "        if top_node in parent_dict[node2]:\n",
-    "            pars2.add(top_node)\n",
-    "    return pars1 & pars2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "black-confidentiality",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shortest_common_path(node1, node2):\n",
-    "    node1_shortest, node2_shortest = [], []\n",
-    "    shortest_len = float('inf')\n",
-    "    for top_par in find_common_top_parents(node1, node2):\n",
-    "        node1_path = find_parent_path(node1, top_par, [])\n",
-    "        node2_path = find_parent_path(node2, top_par, [])\n",
-    "        \n",
-    "        if node1_path and node2_path and max(len(node1_path), len(node2_path)) < shortest_len:\n",
-    "            node1_shortest = node1_path\n",
-    "            node2_shortest = node2_path\n",
-    "            shortest_len = max(len(node1_path), len(node2_path))\n",
-    "    node1_shortest = \"|\".join([node + ':' + fetch_label(node) for node in node1_shortest])\n",
-    "    node2_shortest = \"|\".join([node + ':' + fetch_label(node) for node in node2_shortest])\n",
-    "    pars1 = {}\n",
-    "    pars2 = {}\n",
-    "    for top_node in top_nodes:\n",
-    "        if top_node in parent_dict[row['node2']]:\n",
-    "            pars1[top_node] = top_nodes[top_node]\n",
-    "        if top_node in parent_dict[row['other_node2']]:\n",
-    "            pars2[top_node] = top_nodes[top_node]\n",
-    "    return node1_shortest + \";\" + node2_shortest + ';' + \",\".join(list(pars1.values())) + ';' + \",\".join(list(pars2.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "laden-charter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_label(qnode):\n",
-    "    try:\n",
-    "        return requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0]\n",
-    "    except:\n",
-    "        return str(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "material-interpretation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e73c4a3b211f45ec9d1ebc8d6895bbae",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths = []\n",
-    "for _,row in tqdm(trainingDFSubset.iterrows()):\n",
-    "    trainingSubsetCommonPaths.append(shortest_common_path(row['node2'], row['other_node2']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "id": "promotional-diamond",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Q1921606:keyboard|Q864114:input device|Q47146:user interface|Q23808:interface|Q230629:connection|Q930933:relation;Q327065:display device|Q778637:output device|Q47146:user interface|Q23808:interface|Q230629:connection|Q930933:relation;entity,object,physical object,abstract object,product,person,relation;entity,object,physical object,abstract object,product,relation',\n",
-       " 'Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q11635:theater|Q184485:performing arts|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object,structure',\n",
-       " 'Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q11032:newspaper|Q1002697:periodical|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,physical object,abstract object,structure,action',\n",
-       " 'Q7850:Chinese|Q33857:Sinitic languages|Q45961:Sino-Tibetan languages|Q7524744:Sino-Austronesian languages|Q20162172:human language|Q315:language|Q17376908:languoid|Q7184903:abstract object;Q29536:paganism|Q9174:religion|Q3702971:personal data|Q61151961:individual data|Q42848:data|Q11028:information|Q7184903:abstract object;entity,object,abstract object,property;entity,object,abstract object,structure,property,relation',\n",
-       " 'Q7850:Chinese|Q33857:Sinitic languages|Q45961:Sino-Tibetan languages|Q7524744:Sino-Austronesian languages|Q20162172:human language|Q315:language|Q17376908:languoid|Q7184903:abstract object;Q9134:mythology|Q49447:world view|Q20978643:point of view|Q595523:notion|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object,property;entity,object,abstract object,property,relation,phenomenon',\n",
-       " ';;entity,object,physical object;',\n",
-       " 'Q161:fiber|Q214609:material|Q2424752:product;Q6584340:syrup|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;entity,object,physical object,product;entity,object,physical object,product',\n",
-       " 'Q1734:volleyball|Q877517:ball game|Q4438147:sport games|Q11410:game|Q17538258:recreative work|Q386724:work|Q15401930:product|Q488383:object;Q55488:railway station|Q1318558:operation point|Q800279:railway facility|Q27096235:artificial geographic entity|Q8205328:artificial physical object|Q223557:physical object|Q4406616:concrete object|Q488383:object;entity,object,action,phenomenon;entity,object,physical object,structure',\n",
-       " 'Q1734:volleyball|Q877517:ball game|Q4438147:sport games|Q11410:game|Q17538258:recreative work|Q386724:work|Q15401930:product|Q488383:object;Q3305213:painting|Q4502142:visual artwork|Q838948:work of art|Q28877:goods|Q337060:perceptible object|Q223557:physical object|Q4406616:concrete object|Q488383:object;entity,object,action,phenomenon;entity,object,physical object',\n",
-       " 'Q638:music|Q735:art|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q8261:novel|Q571:book|Q49848:document|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object,structure,action;entity,object,physical object,abstract object,structure,action',\n",
-       " 'Q638:music|Q735:art|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q191067:article|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object,structure,action;entity,object,abstract object',\n",
-       " 'Q17991521:tale|Q1318295:narrative|Q17537576:creative work|Q15621286:intellectual work|Q386724:work|Q15401930:product|Q488383:object|Q35120:entity;Q2977:cathedral|Q16970:church building|Q1370598:place of worship|Q811979:architectural structure|Q811430:human-made geographic feature|Q35145743:human-made landform|Q27096235:artificial geographic entity|Q27096213:geographic entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object;entity,object,physical object,structure',\n",
-       " ';;entity,object,abstract object,structure,property,relation;',\n",
-       " 'Q6718:quark|Q52234516:elementary fermion|Q43116:elementary particle|Q63243922:elementary object|Q488383:object|Q35120:entity;Q107968:anion|Q36496:ion|Q2393187:molecular entity|Q43460564:chemical entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object,physical object,particle;entity,object,physical object,particle',\n",
-       " 'Q420:biology|Q7991:natural science|Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q413:physics|Q14632398:physical sciences|Q7991:natural science|Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object',\n",
-       " 'Q161:fiber|Q214609:material|Q2424752:product;Q80228:bottle|Q16896124:bin|Q987767:container|Q39546:tool|Q16798631:equipment|Q2424752:product;entity,object,physical object,product;entity,object,physical object,structure,product',\n",
-       " 'Q8104:atmosphere|Q47495022:shell of an astronomical object|Q23497974:covering|Q23497981:layer|Q223557:physical object;Q36133:soil|Q1155083:granular material|Q214609:material|Q2424752:product|Q28877:goods|Q337060:perceptible object|Q223557:physical object;entity,object,physical object;entity,object,physical object,product',\n",
-       " 'Q2386606:right|Q7748:law|Q1151067:rule|Q2684591:statement|Q108163:proposition|Q474328:declarative sentence|Q41796:sentence|Q9380010:denunciation|Q628523:message|Q11028:information|Q7184903:abstract object;Q11210:coordinate system|Q184876:frame of reference|Q20978643:point of view|Q595523:notion|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object,structure,relation',\n",
-       " 'Q186361:punishment|Q2995644:result|Q733541:consequence|Q408386:inference|Q3249551:process|Q20937557:series|Q16887380:group|Q28813620:set|Q99527517:collection entity|Q35120:entity;Q50000:victory|Q2995644:result|Q733541:consequence|Q408386:inference|Q3249551:process|Q20937557:series|Q16887380:group|Q28813620:set|Q99527517:collection entity|Q35120:entity;entity,action;entity,property',\n",
-       " 'Q527:sky|Q107:space|Q3054889:measurable set|Q36161:set|Q217594:class|Q16889133:class|Q99527517:collection entity|Q35120:entity;Q1107656:garden|Q35145743:human-made landform|Q27096235:artificial geographic entity|Q27096213:geographic entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object,abstract object,relation;entity,object,physical object,abstract object,structure,relation',\n",
-       " 'Q527:sky|Q107:space|Q3054889:measurable set|Q36161:set|Q217594:class|Q246672:mathematical object|Q7184903:abstract object;Q344:future|Q11471:time|Q3054889:measurable set|Q36161:set|Q217594:class|Q246672:mathematical object|Q7184903:abstract object;entity,object,abstract object,relation;entity,object,abstract object,relation',\n",
-       " 'Q374:vodka|Q56139:liquor|Q17562878:spirit drink|Q154:alcoholic beverage|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;Q13317:yogurt|Q26868224:fermented milk products, other than sour cream and cottage cheese|Q3506176:fermented milk product|Q147538:soft drink|Q2647467:non-alcoholic beverage|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;entity,object,physical object,product;entity,object,physical object,structure,action,property,product,phenomenon',\n",
-       " ';;;entity,object,abstract object,property,relation',\n",
-       " 'Q73792:charge|Q4373292:physical property|Q937228:property;Q11379:energy|Q107715:physical quantity|Q309314:quantity|Q937228:property;entity,object,abstract object,property,relation;entity,object,abstract object,property,relation',\n",
-       " 'Q73792:charge|Q4373292:physical property|Q937228:property;Q11465:velocity|Q107715:physical quantity|Q309314:quantity|Q937228:property;entity,object,abstract object,property,relation;entity,object,abstract object,property,relation']"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moderate-roller",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "diverse-clinton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchDescription(qnode):\n",
-    "    try:\n",
-    "        return requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['descriptions']['en'][0]\n",
-    "    except:\n",
-    "        return str(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "painful-craft",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0eaf93e5d0947d9ae34fd4739b07cdd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths = []\n",
-    "for _,row in tqdm(trainingDF.iterrows()):\n",
-    "    trainingSubsetCommonPaths.append((fetchDescription(row['node1']) + '&' + fetchDescription(row['node2']) + '&' + \\\n",
-    "                                     fetchDescription(row['other_node1']) + '&' + fetchDescription(row['other_node2'])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "id": "front-consortium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['device comprising an arrangement of buttons or keys used to input text in computers&data input device&keyboard of a typewriter&data input device',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&language family&group of languages that belongs to the Sino-Tibetan family',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&group of Chinese languages primarily spoken in the whole city of Shanghai, Zhejiang province and the southern half of Jiangsu province, as well as bordering areas.&group of languages that belongs to the Sino-Tibetan family',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&any fibre derived from natural sources such as plants, animals or minerals&natural or synthetic substance made of long, thin filaments',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&music performed in front of an audience&art form, and cultural activity, whose medium is sound',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&music genre&art form, and cultural activity, whose medium is sound',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&variante of tale that pertains to urbanity&prose fiction that can be either rooted in oral tradition or created by an author',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths',\n",
-       " 'type of quark&class of fundamental particles&type of quark&class of fundamental particles',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&academic discipline&science that studies living beings',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&atmosphere of astronomical objects outside our solar system&layer of gases surrounding a material body',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&right to use the dining facilities offered to the members (and possibly their guests, when accompanied by a member) of certain organisations such as universities, clubs, colleges and bodies such as the House of Lords, and the Hawks\\\\ Club&legal position allowing a person to require a certain thing from another person or other persons',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&False&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&the study of the recording and retrieval of information&systematic enterprise that builds and organizes knowledge',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&movement in political theology and political philosophy&political philosophy and movement',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&keypad that appears on a \\\\Touch Tone\\\\ telephone&computer input device',\n",
-       " 'keyboard of a typewriter&data input device&electronic device that can read and output printed barcodes to a computer&device that optically scans images, printed text, handwriting, or an object, and converts it to a digital image',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&political position based on the interests and perspectives of social groups with which people identify&practice and theory of influencing other people, as in government',\n",
-       " 'applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge&False&set of beliefs and practices for a group or community',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&therapeutic method of treatment&health profession that aims to address the illnesses or injuries that limit a person\\\\s abilities to function in everyday lives',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&field of accounting concerned with the summary, analysis and reporting of financial transactions related to a business&measurement, processing and communication of financial information about economic entities',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&False&East Asian language',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&dialect of English spoken in Nigeria&West Germanic language originating in England',\n",
-       " 'human organ&organ for the circulation of blood in animal circulatory systems&False&rigid organ that constitute part of the endoskeleton of vertebrates',\n",
-       " 'feature of the face&organ that smells and facilitates breathing&organ that serves as the center of the nervous system in apes and monkeys&organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals',\n",
-       " 'False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&essential respiration organ in many air-breathing animals',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&soft wood&fibrous material from trees or other plants',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&metal resistant to corrosion and oxidation in moist air, e.g. ruthenium, rhodium, palladium, silver, osmium, iridium, platinum, and gold&element, compound or alloy that is a good conductor of both electricity and heat',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&water sport&team sport played on a court with baskets on either end',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&game derived from association football adapted for play in an indoor arena&team sports that involve, to varying degrees, kicking a ball to score a goal',\n",
-       " 'team sport featuring constant motion, communication and bursts of explosiveness.&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&informal ad hoc variant of the game of cricket, played by people of all genders and all ages in gardens, back yards, on the street, in parks, carparks, beaches and any area not specifically intended for the purpose&team sport played with bats and balls',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&tennis variant&racket sport played on a court bisected by a net',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&literary genre&written art form',\n",
-       " 'circulation of literature beyond its country of origin&written art form&dance performed by a single dancer&rhythmic movement of the body',\n",
-       " 'False&creative work in which pictures and text convey information such as narratives&False&visual artwork, surface artistically covered with paint',\n",
-       " 'music genre&art form, and cultural activity, whose medium is sound&use of digital technology to distribute or project motion pictures as opposed to the historical use of reels of motion picture film&art of film making and projection',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&literary genre&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story',\n",
-       " 'False&publication type&blog on fashion&discussion or informational site published on the World Wide Web',\n",
-       " 'False&work comprising an ordered list of items&article that is published in news media&text that forms an independent part of a publication',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Christian doctrine professed by the Roman Catholic Church&monotheistic religious group based on the belief of Jesus being the Son of God',\n",
-       " 'denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&ethnic religion of the Yao people in China, heavily based on Taoism since the 13th century, similar to the communitarian Taoism in the earliest Taoist movements in China proper&religious or philosophical tradition of Chinese origin',\n",
-       " 'False&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&modernized form of Buddhism&religion founded by the Buddha',\n",
-       " 'type of quark&class of fundamental particles&ion having a negative charge&atom or molecule in which the total number of electrons is not equal to the total number of protons, giving the atom a net positive or negative electrical charge',\n",
-       " 'type of quark&class of fundamental particles&class of ions&atom or molecule in which the total number of electrons is not equal to the total number of protons, giving the atom a net positive or negative electrical charge',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&scientific study of minerals&study of the composition, structure, physical properties, and history of Earth\\\\s components, and the processes by which they are shaped',\n",
-       " 'field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force&subdiscipline within chemistry involving the scientific study of carbon-based compounds, hydrocarbons, and their derivatives&branch of physical science concerned with the composition, structure and properties of matter',\n",
-       " 'medical speciality dealing with the diseases of animals, animal welfare, etc.&field of study for diagnosing, treating and preventing disease&branch of psychology focused on personality&The scientific study of the way the human mind works and how it influences behaviour, or the influence of a particular person\\\\s character on their behaviour',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&wire made of iron&single, usually cylindrical, flexible strand or rod of metal',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&paint type&colored composition applied over a surface that dries as a solid film',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&atmospheric layer of Earth&atmospheric layer of Earth',\n",
-       " 'water-filled depression between coastal sand dunes&land area that is permanently or seasonally saturated with water&sub-continental land that is surrounded by water&contiguous area of land surrounded by ocean; large continuous area of land, either surrounded by sea or contiguous with another landmass',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&Ttx on shares transferred electronically without using a stock transfer form&method to impose financial charge or other levy upon a taxpayer by a government or functional equivalent',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&award for authors and literary associations&something given to a person or a group of people to recognize their merit or excellence',\n",
-       " 'land areas near the shore that are frequently or permanently covered by water&land area that is permanently or seasonally saturated with water&space separate from the rest of the residential garden&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'rock-like reefs, composed of dense aggregations of oysters&feature lying beneath the surface of the water&garden where flowers are grown and displayed&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&kind of beer&alcoholic drink',\n",
-       " 'wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes&Wikimedia disambiguation page&alcoholic drink',\n",
-       " 'False&distilled beverage&type of vodka flavored with raspberries&distilled alcoholic beverage',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&political ideology&political philosophy focused on retaining traditional social institutions',\n",
-       " 'movement in political theology and political philosophy&political philosophy and movement&nationalism based on the its activity on the internet&ideology and movement that promotes the interests of a particular nation (as in a group of people) especially with the aim of gaining and maintaining the nation\\\\s sovereignty (self-governance) over its homeland',\n",
-       " 'form of anarchism&political philosophy and movement&False&belief or attitude that some individuals, who form an elite, are those whose influence or authority is greater than that of others',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&third in a set of four quantum numbers, distinguishes the orbitals available within a subshell, and is used to calculate the azimuthal component of the orientation of orbital in space&notation for conserved quantities in physics and chemistry',\n",
-       " 'type of frequency&inverse of the period duration&Force that makes a moving body follow a curved path&influence that causes an object to change motion',\n",
-       " 'rate at which electric energy is transferred by an electric circuit&rate at which energy is transferred, used, or transformed&False&mass per volume',\n",
-       " 'average velocity attained by charged particles in a material due to electric field&rate of change of the position of an object as a function of time, and the direction of that change&potential and kinetic energy associated with the position and movement of a physical object&quantitative physical property transferred to objects to perform heating or work on them',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&False&output device for presentation of information in visual form',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&theatrical genre&collaborative form of performing and fine art',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&False&scheduled publication containing news of events, articles, features, editorials, and advertising',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&ethnic religion practiced by the Germanic peoples from the Iron Age until Christianisation&non-Abrahamic religion, or modern religious movement such as nature worship',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&Mythology that existed in the Korean Peninsula from ancient times.&stories of gods and fairies and fantastic creatures',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&False',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&railway station located below grade&railway facility where trains regularly stop to load or unload passengers and/or freight',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&type of artwork&visual artwork, surface artistically covered with paint',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&literary genre&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&article that is published in news media&text that forms an independent part of a publication',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&church that is the seat of a bishop of any of the Protestant churches&Christian church, which is seat of a bishop',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Christian ethnoreligious Group&diaspora',\n",
-       " 'type of quark&class of fundamental particles&anion consisting of one atom&ion having a negative charge',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&lidded container made from glass&cylindrical container',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&finely grained regolith on the Moon&natural body consisting of layers that are primarily composed of minerals',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&given a reference triangle, a coordinate system whose three coordinates are distances to each of the three vertices of the triangle&System for determining the position of a point by a tuple of scalars',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&A military victory determines the undisputed outcome of a war&above a gate is the word Invictus',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&False&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth&time beyond 10 000 AD&time which has yet to occur',\n",
-       " 'type of vodka flavored with raspberries&distilled alcoholic beverage&False&dairy product',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&mathematical function that describes the probability of occurrence of different possible outcomes in an experiment',\n",
-       " 'property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&potential and kinetic energy associated with the position and movement of a physical object&quantitative physical property transferred to objects to perform heating or work on them',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&subclass of velocity&rate of change of the position of an object as a function of time, and the direction of that change',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&paint that air dries to a hard, usually glossy, finish, used for coating surfaces that are outdoors or otherwise subject to hard wear or variations in temperature&colored composition applied over a surface that dries as a solid film',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&deep blue colored glass with cobalt&amorphous solid that exhibits a glass transition when heated towards the liquid state',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&organization or political movement leading a rebellion, or a non-violent social movement, against a colonial power or national government&type of group action',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&computer whose batteries use solar power&foldable portable personal computer for mobile use',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&False&material produced by twining, weaving, felting, knotting, or otherwise processing natural or synthetic fibers',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story&a written text that can be published in printed or electronic form',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&Temporary or emergency government body/authority created when none exists&system or group of people governing an organized community, often a state',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&form of dictatorial military rule&autocratic form of government which is ruled by a sole leader',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&stage of the English language from about the 12th through 15th centuries&West Germanic language originating in England',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&body exerting the executive power in France&system or group of people governing an organized community, often a state',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&False&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&type of wood&fibrous material from trees or other plants',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&music that developed in literate cultures, replacing prehistoric music&art form, and cultural activity, whose medium is sound',\n",
-       " 'type of quark&class of fundamental particles&civilization in control of a planetary system&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&social movement seeking to change anti-fat bias in social attitudes&type of group action',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&type of financial institution&financial institution',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&False&material or object that produces a magnetic field',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&Wikimedia list article&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&lands originally owned by the monarchy of the United Kingdom&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&collection of songs, poetry, dances etc. of China&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&cultural movement&cultural movement that spanned the period roughly from the 14th to the 17th century',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&dialect&West Germanic language originating in England',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&society\\\\s way of life within anthropology',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&ritual and ceremonial mask of Sub-Saharan Africa&any full or partial face covering, whether ceremonial, protective, decorative, or used as disguise',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&flora and fauna of the Philippines&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&False&investigation and reporting of events, issues and trends to a broad audience',\n",
-       " 'keyboard of a typewriter&data input device&ancient Egyptian funerary text&a written text that can be published in printed or electronic form',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&Expressive body of culture shared by a particular group.&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge&False&cultural movement that spanned the period roughly from the 14th to the 17th century',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&infusion made from roasted barley grains&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&False&system or group of people governing an organized community, often a state',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&wildlife that can live or thrive in urban environments&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&music genre&art form, and cultural activity, whose medium is sound',\n",
-       " 'human organ&organ for the circulation of blood in animal circulatory systems&current issue that has to be reported&communication of selected information on current events',\n",
-       " 'feature of the face&organ that smells and facilitates breathing&drink made from infusing fruit and other material in hot water&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&reference book containing an alphabetical list of slang&collection of words and their meanings',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&New model of banks initiative by Reserve Bank of India&financial institution',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&False&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&False&interaction among people in which they create, share, and/or exchange information and ideas in virtual communities and networks',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&economy with an increased emphasis on informational activities and information industry&area of the production, distribution and trade, as well as consumption of goods and services by different agents',\n",
-       " 'team sport featuring constant motion, communication and bursts of explosiveness.&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&drink made from sepals of Hibiscus sabdariffa&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&False&collection of words and their meanings',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&syrup usually made from the xylem sap of sugar maple, red maple, or black maple trees&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'circulation of literature beyond its country of origin&written art form&birds that live on or around water&class of tetrapods',\n",
-       " 'False&creative work in which pictures and text convey information such as narratives&type of garbage&unwanted or unusable material',\n",
-       " 'music genre&art form, and cultural activity, whose medium is sound&painted decoration or scene applied to a prepared wall or ceiling surface&visual artwork, surface artistically covered with paint',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&coffee brewing method originating from Turkey&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'False&publication type&aggregate of past events from the beginning of recorded human history to the Early Middle Ages&past events and their tracks or records, studied by various branches of human sciences of history',\n",
-       " 'False&work comprising an ordered list of items&continental geography&science that studies the terrestrial surface, the societies that inhabit it and the territories, landscapes, places or regions that form it when interacting with each other',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&method of brewing coffee&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&creation and maintenance of colonies by people from another territory&creation of an unequal relationship between states through domination',\n",
-       " 'False&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&type of writing tool&writing and drawing implement using liquid or paste ink',\n",
-       " 'type of quark&class of fundamental particles&insect which lives in the air or on land and breeds in water&class of arthropods',\n",
-       " 'type of quark&class of fundamental particles&bank that specializes in originating and/or servicing mortgage loans&financial institution',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&box containing cigars&container (usually cuboidal) with at least one roughly rectangular cross-section',\n",
-       " 'field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force&Variety of coffee plant&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'medical speciality dealing with the diseases of animals, animal welfare, etc.&field of study for diagnosing, treating and preventing disease&chocolate-flavored condiment&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&An economic system combining public and private production&area of the production, distribution and trade, as well as consumption of goods and services by different agents',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&fire lit at a campsite&rapid oxidation of a material',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&False&writing and drawing implement using liquid or paste ink',\n",
-       " 'water-filled depression between coastal sand dunes&land area that is permanently or seasonally saturated with water&False&science that studies the terrestrial surface, the societies that inhabit it and the territories, landscapes, places or regions that form it when interacting with each other',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&physical money&object or record accepted as payment',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&a pencil whose lead contains a dye so that its stroke is indelible&writing implement',\n",
-       " 'land areas near the shore that are frequently or permanently covered by water&land area that is permanently or seasonally saturated with water&type of private company&financial institution',\n",
-       " 'rock-like reefs, composed of dense aggregations of oysters&feature lying beneath the surface of the water&clean subterran source of water, rich in minerals and trace elements&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&pattern of human activity and associated with the minority peoples of Russia&society\\\\s way of life within anthropology',\n",
-       " 'wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes&thin grade of paper used for printing books which have many pages&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'False&distilled beverage&material discarded as a byproduct of agriculture&unwanted or unusable material',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&perennial woody plant',\n",
-       " 'movement in political theology and political philosophy&political philosophy and movement&cellulose-based paper that is used in baking&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'form of anarchism&political philosophy and movement&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&head of state and head of government of the Republic of Cyprus&leader of a country or part of a country, usually in republics',\n",
-       " 'type of frequency&inverse of the period duration&a volcano considered unlikely to erupt again&natural, surface vent or fissure usually in a mountainous form',\n",
-       " 'rate at which electric energy is transferred by an electric circuit&rate at which energy is transferred, used, or transformed&False&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'average velocity attained by charged particles in a material due to electric field&rate of change of the position of an object as a function of time, and the direction of that change&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&subdiscipline within chemistry involving the scientific study of carbon-based compounds, hydrocarbons, and their derivatives&branch of physical science concerned with the composition, structure and properties of matter',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&False&writing implement',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&water on the continents surface, rather than underground&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&False&outline of a strategy for achievement of an objective',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&branch of physiology and neuroscience&science of the function of living systems',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&positionرئيس الجمهوريه اليمنية&leader of a country or part of a country, usually in republics',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&electronically coded still image in video technology&data transmission unit',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&level of psychological well-being, or an absence of a mental disorder&state of physical, mental and social well-being in the absence of disease and infirmity',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&a recession that affects many countries around the world&business cycle contraction;\\xa0general slowdown in economic activity',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&tinplate container&container (usually cuboidal) with at least one roughly rectangular cross-section',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&compensation paid by an offender (usually a murderer) or their family to kin of the victim&object or record accepted as payment',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&pencil with lead of finely ground, highly compacted charcoal&writing implement',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Overall health of the population of Denmark&state of physical, mental and social well-being in the absence of disease and infirmity',\n",
-       " 'type of quark&class of fundamental particles&Paper with security features&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&False&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&recreational visitor attraction&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&article in an academic publication, usually peer reviewed&text that forms an independent part of a publication',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&water frozen into the solid state&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&False&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&money given in a bribe&object or record accepted as payment',\n",
-       " 'view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth&culture of an area&society\\\\s way of life within anthropology',\n",
-       " 'type of vodka flavored with raspberries&distilled alcoholic beverage&False&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&type of rotor craft in which lift and thrust are supplied by rotors',\n",
-       " 'property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&type of paper&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&False&writing implement']"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "upset-introduction",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blank-weekend",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "alpine-cooperation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recent-algorithm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cordless-subdivision",
-   "metadata": {},
-   "source": [
-    "# Find Candidates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "id": "historical-maryland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'False'"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fetchDescription('Q1757352')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "id": "composite-vienna",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "556d68601f09422292148ac709006b9a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/39 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q3711805</td>\n",
-       "      <td>singular distribution</td>\n",
-       "      <td>distribution concentrated on a set of measure ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q7893853</td>\n",
-       "      <td>univariate distribution</td>\n",
-       "      <td>probability distribution of only one random va...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q3258332</td>\n",
-       "      <td>Kent distribution</td>\n",
-       "      <td>probability distribution on the two-dimensiona...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q16000505</td>\n",
-       "      <td>symmetric probability distribution</td>\n",
-       "      <td>probability distribution which features a dens...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q670653</td>\n",
-       "      <td>marginal distribution</td>\n",
-       "      <td>probability distribution of a subset (the \\mar...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q4889567</td>\n",
-       "      <td>Benktander Weibull distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q7049201</td>\n",
-       "      <td>noncentral F-distribution</td>\n",
-       "      <td>continuous probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Q21051291</td>\n",
-       "      <td>joint distribution of random variables</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q3258268</td>\n",
-       "      <td>noncentral beta distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Q1472677</td>\n",
-       "      <td>heavy-tailed distribution</td>\n",
-       "      <td>probability distributions whose tails are not ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Q6664772</td>\n",
-       "      <td>location-scale family</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Q7857491</td>\n",
-       "      <td>Tweedie distribution</td>\n",
-       "      <td>family of probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q917918</td>\n",
-       "      <td>continuous probability distribution</td>\n",
-       "      <td>probability distribution which admits a cumula...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Q2671311</td>\n",
-       "      <td>Rice distribution</td>\n",
-       "      <td>probability distribution of the magnitude of a...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q85900768</td>\n",
-       "      <td>multimodal distribution</td>\n",
-       "      <td>probability distribution whose density has two...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Q3204440</td>\n",
-       "      <td>relativistic Breit–Wigner distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Q3711786</td>\n",
-       "      <td>compound Poisson distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Q17157111</td>\n",
-       "      <td>mixture distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Q7247759</td>\n",
-       "      <td>product distribution</td>\n",
-       "      <td>probability distribution, in mathematics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Q98098110</td>\n",
-       "      <td>board game tile distribution</td>\n",
-       "      <td>a distribution of game tiles and their scores ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Q1188504</td>\n",
-       "      <td>exponential family</td>\n",
-       "      <td>probablility distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Q5421539</td>\n",
-       "      <td>exponentiated Weibull distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Q1417619</td>\n",
-       "      <td>Voigt profile</td>\n",
-       "      <td>probability distribution often used in spectro...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>Q7604398</td>\n",
-       "      <td>statistical interference</td>\n",
-       "      <td>overlap of multiple probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Q7051758</td>\n",
-       "      <td>normal-inverse-gamma distribution</td>\n",
-       "      <td>four-parameter family of multivariate continuo...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q2421579</td>\n",
-       "      <td>log-Laplace distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q26898117</td>\n",
-       "      <td>discrete probability distribution</td>\n",
-       "      <td>probability distribution whose mass lies entir...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q5527846</td>\n",
-       "      <td>Gaussian q-distribution</td>\n",
-       "      <td>family of probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q3258303</td>\n",
-       "      <td>Benktander Gibrat distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q3773214</td>\n",
-       "      <td>sampling distribution</td>\n",
-       "      <td>probability distribution of the possible sampl...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Q12345341</td>\n",
-       "      <td>Asymptotic distribution</td>\n",
-       "      <td>probability distribution to which random varia...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Q59394126</td>\n",
-       "      <td>Uniform distribution</td>\n",
-       "      <td>term in probability theory</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Q1333358</td>\n",
-       "      <td>joint probability distribution</td>\n",
-       "      <td>probability distribution which can feature mor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>Q1934245</td>\n",
-       "      <td>stable distribution</td>\n",
-       "      <td>distribution of variables which satisfies a st...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Q5156926</td>\n",
-       "      <td>compound probability distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Q576072</td>\n",
-       "      <td>Student\\s t-distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>Q7051757</td>\n",
-       "      <td>Normal-inverse-Wishart distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>Q255598</td>\n",
-       "      <td>bimodal distribution</td>\n",
-       "      <td>probability distribution whose density has two...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Q2300258</td>\n",
-       "      <td>conditional probability distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0                                       1  \\\n",
-       "0    Q3711805                   singular distribution   \n",
-       "1    Q7893853                 univariate distribution   \n",
-       "2    Q3258332                       Kent distribution   \n",
-       "3   Q16000505      symmetric probability distribution   \n",
-       "4     Q670653                   marginal distribution   \n",
-       "5    Q4889567         Benktander Weibull distribution   \n",
-       "6    Q7049201               noncentral F-distribution   \n",
-       "7   Q21051291  joint distribution of random variables   \n",
-       "8    Q3258268            noncentral beta distribution   \n",
-       "9    Q1472677               heavy-tailed distribution   \n",
-       "10   Q6664772                   location-scale family   \n",
-       "11   Q7857491                    Tweedie distribution   \n",
-       "12    Q917918     continuous probability distribution   \n",
-       "13   Q2671311                       Rice distribution   \n",
-       "14  Q85900768                 multimodal distribution   \n",
-       "15   Q3204440  relativistic Breit–Wigner distribution   \n",
-       "16   Q3711786           compound Poisson distribution   \n",
-       "17  Q17157111                    mixture distribution   \n",
-       "18   Q7247759                    product distribution   \n",
-       "19  Q98098110            board game tile distribution   \n",
-       "20   Q1188504                      exponential family   \n",
-       "21   Q5421539      exponentiated Weibull distribution   \n",
-       "22   Q1417619                           Voigt profile   \n",
-       "23   Q7604398                statistical interference   \n",
-       "24   Q7051758       normal-inverse-gamma distribution   \n",
-       "25   Q2421579                log-Laplace distribution   \n",
-       "26  Q26898117       discrete probability distribution   \n",
-       "27   Q5527846                 Gaussian q-distribution   \n",
-       "28   Q3258303          Benktander Gibrat distribution   \n",
-       "29   Q3773214                   sampling distribution   \n",
-       "30  Q12345341                 Asymptotic distribution   \n",
-       "31  Q59394126                    Uniform distribution   \n",
-       "32   Q1333358          joint probability distribution   \n",
-       "33   Q1934245                     stable distribution   \n",
-       "34   Q5156926       compound probability distribution   \n",
-       "35    Q576072                Student\\s t-distribution   \n",
-       "36   Q7051757     Normal-inverse-Wishart distribution   \n",
-       "37    Q255598                    bimodal distribution   \n",
-       "38   Q2300258    conditional probability distribution   \n",
-       "\n",
-       "                                                    2  \n",
-       "0   distribution concentrated on a set of measure ...  \n",
-       "1   probability distribution of only one random va...  \n",
-       "2   probability distribution on the two-dimensiona...  \n",
-       "3   probability distribution which features a dens...  \n",
-       "4   probability distribution of a subset (the \\mar...  \n",
-       "5                                               False  \n",
-       "6                 continuous probability distribution  \n",
-       "7                                               False  \n",
-       "8                                               False  \n",
-       "9   probability distributions whose tails are not ...  \n",
-       "10                                              False  \n",
-       "11                family of probability distributions  \n",
-       "12  probability distribution which admits a cumula...  \n",
-       "13  probability distribution of the magnitude of a...  \n",
-       "14  probability distribution whose density has two...  \n",
-       "15                           probability distribution  \n",
-       "16                                              False  \n",
-       "17                           probability distribution  \n",
-       "18           probability distribution, in mathematics  \n",
-       "19  a distribution of game tiles and their scores ...  \n",
-       "20                          probablility distribution  \n",
-       "21                                              False  \n",
-       "22  probability distribution often used in spectro...  \n",
-       "23      overlap of multiple probability distributions  \n",
-       "24  four-parameter family of multivariate continuo...  \n",
-       "25                           probability distribution  \n",
-       "26  probability distribution whose mass lies entir...  \n",
-       "27                family of probability distributions  \n",
-       "28                                              False  \n",
-       "29  probability distribution of the possible sampl...  \n",
-       "30  probability distribution to which random varia...  \n",
-       "31                         term in probability theory  \n",
-       "32  probability distribution which can feature mor...  \n",
-       "33  distribution of variables which satisfies a st...  \n",
-       "34                                              False  \n",
-       "35                           probability distribution  \n",
-       "36                                              False  \n",
-       "37  probability distribution whose density has two...  \n",
-       "38                                              False  "
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "par_qnode = 'Q200726'\n",
-    "results = []\n",
-    "for child in tqdm(parent_child_dict[par_qnode]):\n",
-    "    results.append((child, fetch_label(child), fetchDescription(child)))\n",
-    "pd.DataFrame(results).head(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "atlantic-milton",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opposed-regulation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "liable-lincoln",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "encouraging-legend",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Analysis Files Analyser-checkpoint.ipynb b/.ipynb_checkpoints/Analysis Files Analyser-checkpoint.ipynb
deleted file mode 100644
index 8b82add..0000000
--- a/.ipynb_checkpoints/Analysis Files Analyser-checkpoint.ipynb	
+++ /dev/null
@@ -1,1783 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "sound-better",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "affected-driving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New Acc']\n",
-    "        oldAcc = row['Old Acc']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New Acc'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "numerous-local",
-   "metadata": {},
-   "outputs": [
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-       "      <td>-0.581395</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>68.023256</td>\n",
-       "      <td>0.872093</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.727273</td>\n",
-       "      <td>0.505747</td>\n",
-       "      <td>0.427184</td>\n",
-       "      <td>0.463158</td>\n",
-       "      <td>18.160508</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60.174419</td>\n",
-       "      <td>7.848837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>4.069767</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.812500</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.722222</td>\n",
-       "      <td>0.511628</td>\n",
-       "      <td>0.213592</td>\n",
-       "      <td>0.301370</td>\n",
-       "      <td>3.838469</td>\n",
-       "      <td>0</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>4.069767</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63.953488</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.038835</td>\n",
-       "      <td>0.073394</td>\n",
-       "      <td>4.033524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>63.953488</td>\n",
-       "      <td>0.290698</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.520000</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.577778</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.123894</td>\n",
-       "      <td>19.390705</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    0                   1   2     3   4          5   \\\n",
-       "0         text_7_props        19k_childPar   2  True   1  57.267442   \n",
-       "1         text_2_props         19k_2_class   2  True   1  56.686047   \n",
-       "2              complex        19k_childPar   1  True   3  67.151163   \n",
-       "3               transe  19k_class_childPar   1  True   1  63.662791   \n",
-       "4             abstract  19k_class_childPar   2  True   1  63.953488   \n",
-       "5  abstract_first_sent  19k_class_siblings   2  True   1  65.697674   \n",
-       "\n",
-       "          6         7    8         9   ...        15    16        17  \\\n",
-       "0  57.267442  0.000000  344  0.325000  ...  0.333333  0.75  0.461538   \n",
-       "1  56.104651 -0.581395  344  0.292683  ...  0.288889  0.65  0.400000   \n",
-       "2  68.023256  0.872093  344  0.916667  ...  0.923077  0.60  0.727273   \n",
-       "3  67.732558  4.069767  344  0.900000  ...  0.812500  0.65  0.722222   \n",
-       "4  64.244186  0.290698  344  0.461538  ...  0.468750  0.75  0.576923   \n",
-       "5  65.697674  0.000000  344  0.500000  ...  0.520000  0.65  0.577778   \n",
-       "\n",
-       "         18        19        20         21  22         23        24  \n",
-       "0  0.210526  0.038835  0.065574  12.082148   0  57.267442  0.000000  \n",
-       "1  0.210526  0.038835  0.065574  87.920485   0  56.686047 -0.581395  \n",
-       "2  0.505747  0.427184  0.463158  18.160508   0  60.174419  7.848837  \n",
-       "3  0.511628  0.213592  0.301370   3.838469   0  63.662791  4.069767  \n",
-       "4  0.666667  0.038835  0.073394   4.033524   0  63.953488  0.290698  \n",
-       "5  0.700000  0.067961  0.123894  19.390705   0  65.697674  0.000000  \n",
-       "\n",
-       "[6 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findMaximals(unsupSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "disciplinary-pioneer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findPrecRecIMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New I F1-Score']\n",
-    "        oldAcc = row['Old I F1-Score']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list()[:5], embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New I F1-Score'], embDict[emb]['maxRow']['New I F1-Score'] - embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Old I Precision'], embDict[emb]['maxRow']['New I Precision'], embDict[emb]['maxRow']['Old I Recall'], embDict[emb]['maxRow']['New I Recall'], ])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "million-particular",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>0.295455</td>\n",
-       "      <td>0.311111</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.727273</td>\n",
-       "      <td>0.106583</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.756757</td>\n",
-       "      <td>0.156757</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.823529</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.521739</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.055184</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.523810</td>\n",
-       "      <td>0.627451</td>\n",
-       "      <td>0.103641</td>\n",
-       "      <td>0.466667</td>\n",
-       "      <td>0.516129</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    0                   1   2     3   4         5         6   \\\n",
-       "0         text_7_props  19k_class_childPar   2  True   1  0.433333  0.468750   \n",
-       "1         text_2_props               19k_2   2  True   3  0.393443  0.430769   \n",
-       "2              complex  19k_class_childPar   1  True   3  0.620690  0.727273   \n",
-       "3               transe        19k_childPar   1  True   2  0.600000  0.756757   \n",
-       "4             abstract  19k_class_childPar   2  True   1  0.521739  0.576923   \n",
-       "5  abstract_first_sent        19k_childPar   2  True   3  0.523810  0.627451   \n",
-       "\n",
-       "         7         8         9     10    11  \n",
-       "0  0.035417  0.325000  0.340909  0.65  0.75  \n",
-       "1  0.037327  0.295455  0.311111  0.65  0.70  \n",
-       "2  0.106583  0.916667  0.923077  0.55  0.60  \n",
-       "3  0.156757  0.846154  0.823529  0.55  0.70  \n",
-       "4  0.055184  0.461538  0.468750  0.60  0.75  \n",
-       "5  0.103641  0.466667  0.516129  0.70  0.80  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findPrecRecIMaximals(unsupSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "smoking-burns",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findPrecRecUMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New U F1-Score']\n",
-    "        oldAcc = row['Old U F1-Score']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list()[:5], embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New U F1-Score'], embDict[emb]['maxRow']['New U F1-Score'] - embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Old U Precision'], embDict[emb]['maxRow']['New U Precision'], embDict[emb]['maxRow']['Old U Recall'], embDict[emb]['maxRow']['New U Recall'], ])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "premium-dakota",
-   "metadata": {},
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-       "1         text_2_props         19k_2_class   2  True   1  0.065574  0.065574   \n",
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-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findPrecRecUMaximals(unsupSumm)"
-   ]
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-   "execution_count": 39,
-   "id": "checked-scotland",
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-       "                                      0            1   2     3   4     5   \\\n",
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-    "concatSumm = pd.read_csv('../data/retrofitting/retro_concat_Sep_21_2021.csv')\n",
-    "findMaximals(concatSumm)"
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-       "<p>6 rows × 29 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "                    0                   1   2     3   4          5   \\\n",
-       "0         text_7_props  19k_class_siblings   2  True   1  76.334893   \n",
-       "1         text_2_props  19k_class_siblings   2  True   1  76.464910   \n",
-       "2              complex  19k_class_siblings   2  True   1  92.938685   \n",
-       "3               transe  19k_class_siblings   2  True   1  92.014775   \n",
-       "4             abstract  19k_class_siblings   2  True   1  93.098252   \n",
-       "5  abstract_first_sent  19k_class_siblings   2  True   1  94.562916   \n",
-       "\n",
-       "          6         7      8   9   ...  19  20        21        22        23  \\\n",
-       "0  73.079537 -3.255356  50837 NaN  ... NaN NaN  0.378092  0.405654  0.768794   \n",
-       "1  73.318887 -3.146023  50837 NaN  ... NaN NaN  0.344876  0.373852  0.770582   \n",
-       "2  92.425511 -0.513174  50666 NaN  ... NaN NaN  0.062191  0.066431  0.941644   \n",
-       "3  89.904950 -2.109825  50666 NaN  ... NaN NaN  0.131449  0.146290  0.931296   \n",
-       "4  91.391283 -1.706969  20695 NaN  ... NaN NaN  0.071378  0.077739  0.943133   \n",
-       "5  93.337602 -1.225314  20695 NaN  ... NaN NaN  0.065018  0.069258  0.958076   \n",
-       "\n",
-       "         24          25  26         27        28  \n",
-       "0  0.735391  129.360480   0  76.334893 -3.255356  \n",
-       "1  0.738268  131.612632   0  76.464910 -3.146023  \n",
-       "2  0.936380  104.645769   0  92.938685 -0.513174  \n",
-       "3  0.909689  100.077391   0  92.014775 -2.109825  \n",
-       "4  0.925732   42.654327   0  93.098252 -1.706969  \n",
-       "5  0.945590   42.566209   0  94.562916 -1.225314  \n",
-       "\n",
-       "[6 rows x 29 columns]"
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-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatSumm = pd.read_csv('../data/retrofitting/retro_WikiCS_Sep_27_2021.csv')\n",
-    "findMaximals(concatSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
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-   "id": "perfect-introduction",
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-       "<p>6 rows × 29 columns</p>\n",
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-       "                    0            1   2     3   4          5          6   \\\n",
-       "0         text_7_props  19k_2_class   1  True   1  18.991529  19.007664   \n",
-       "1         text_2_props  19k_2_class   2  True   3  18.991529  19.007664   \n",
-       "2              complex        19k_2   1  True   3  81.661960  81.920129   \n",
-       "3               transe        19k_2   1  True   3  82.412263  82.476805   \n",
-       "4             abstract  19k_2_class   2  True   3  18.112142  18.112142   \n",
-       "5  abstract_first_sent  19k_2_class   2  True   3  18.104074  18.112142   \n",
-       "\n",
-       "         7    8   9   ...  19  20        21        22        23        24  \\\n",
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-       "\n",
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-     },
-     "execution_count": 52,
-     "metadata": {},
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-   ],
-   "source": [
-    "concatSumm = pd.read_csv('../data/retrofitting/retro_conceptNet_Sep_27_2021.csv')\n",
-    "findMaximals(concatSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fiscal-actress",
-   "metadata": {},
-   "outputs": [],
-   "source": []
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-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "accepted-species",
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-   "execution_count": 23,
-   "id": "subjective-italy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findSVMMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['emb']\n",
-    "        basis = row['basis']\n",
-    "        acc = row['Acc']\n",
-    "        iteration = row['iteration']\n",
-    "        if iteration == 0:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': -1, 'maxRow': row, 'oldAcc': acc}\n",
-    "            else:\n",
-    "                embDict[emb] = {'maxNewAcc': embDict[emb]['maxNewAcc'], 'maxRow': embDict[emb]['maxRow'], 'oldAcc': acc}\n",
-    "        else:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': -1}\n",
-    "            else:\n",
-    "                if acc > embDict[emb]['maxNewAcc'] or (acc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                    embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Acc'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "digital-nursery",
-   "metadata": {},
-   "outputs": [
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-       "      <th>0</th>\n",
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-       "      <td>19k_class_childPar</td>\n",
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-       "      <td>True</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_19k_2_class_2_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>complex</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668487</td>\n",
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-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668403</td>\n",
-       "      <td>0.674454</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.712017</td>\n",
-       "      <td>0.683193</td>\n",
-       "      <td>0.028824</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent_19k_2_2_weighted</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.711681</td>\n",
-       "      <td>0.714622</td>\n",
-       "      <td>-0.002941</td>\n",
-       "    </tr>\n",
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-       "</div>"
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-      "text/plain": [
-       "                                            0                   1  \\\n",
-       "0  text_7_props_19k_class_childPar_1_weighted  19k_class_childPar   \n",
-       "1  text_2_props_19k_class_childPar_2_weighted  19k_class_childPar   \n",
-       "2              complex_19k_2_class_2_weighted         19k_2_class   \n",
-       "3               transe_19k_2_class_1_weighted         19k_2_class   \n",
-       "4      abstract_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "5        abstract_first_sent_19k_2_2_weighted               19k_2   \n",
-       "\n",
-       "                     2     3  4  5      6         7         8         9  \n",
-       "0         text_7_props  True  2  1  score  0.694370  0.680000  0.014370  \n",
-       "1         text_2_props  True  2  2  score  0.694622  0.677311  0.017311  \n",
-       "2              complex  True  2  2  score  0.668487  0.674538 -0.006050  \n",
-       "3               transe  True  2  1  score  0.668403  0.674454 -0.006050  \n",
-       "4             abstract  True  2  2  score  0.712017  0.683193  0.028824  \n",
-       "5  abstract_first_sent  True  1  2  score  0.711681  0.714622 -0.002941  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmSumm = pd.read_csv('../data/retrofitting/retro_SVM_Reg_test_Sept_21_2021.csv')\n",
-    "findSVMMaximals(svmSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "elementary-carrier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findSVMCombMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['emb_comb']\n",
-    "        basis = row['basis']\n",
-    "        acc = row['accuracy']\n",
-    "        iteration = row['iteration']\n",
-    "        if iteration == 0:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': -1, 'maxRow': row, 'oldAcc': acc}\n",
-    "            else:\n",
-    "                embDict[emb] = {'maxNewAcc': embDict[emb]['maxNewAcc'], 'maxRow': embDict[emb]['maxRow'], 'oldAcc': acc}\n",
-    "        else:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': -1}\n",
-    "            else:\n",
-    "                if acc > embDict[emb]['maxNewAcc'] or (acc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                    embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['accuracy'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "sunset-bahrain",
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>abstract_first_sent_19k_2_2_weighted</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>text_2_props &amp; abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.726555</td>\n",
-       "      <td>0.717815</td>\n",
-       "      <td>0.008739</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>abstract_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>complex &amp; transe &amp; abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.726303</td>\n",
-       "      <td>0.679916</td>\n",
-       "      <td>0.046387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>abstract_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>text_7_props &amp; text_2_props &amp; transe &amp; abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.724034</td>\n",
-       "      <td>0.697395</td>\n",
-       "      <td>0.026639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>abstract_first_sent_19k_class_childPar_2_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_7_props &amp; text_2_props &amp; transe &amp; abstrac...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.721092</td>\n",
-       "      <td>0.714790</td>\n",
-       "      <td>0.006303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>abstract_first_sent_19k_childPar_1_weighted</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>text_7_props &amp; complex &amp; abstract &amp; abstract_f...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.720924</td>\n",
-       "      <td>0.665294</td>\n",
-       "      <td>0.055630</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>text_2_props_19k_class_childPar_1_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_7_props &amp; text_2_props</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.694370</td>\n",
-       "      <td>0.674286</td>\n",
-       "      <td>0.020084</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>abstract_first_sent_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>abstract &amp; abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.691429</td>\n",
-       "      <td>0.682857</td>\n",
-       "      <td>0.008571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>transe_19k_siblings_2_weighted</td>\n",
-       "      <td>19k_siblings</td>\n",
-       "      <td>complex &amp; transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668655</td>\n",
-       "      <td>0.662689</td>\n",
-       "      <td>0.005966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_19k_2_class_2_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>complex</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668487</td>\n",
-       "      <td>0.674538</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668403</td>\n",
-       "      <td>0.674454</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>63 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    0                   1  \\\n",
-       "14               abstract_first_sent_19k_2_2_weighted               19k_2   \n",
-       "37             abstract_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "44                    abstract_19k_2_class_1_weighted         19k_2_class   \n",
-       "45  abstract_first_sent_19k_class_childPar_2_weighted  19k_class_childPar   \n",
-       "49        abstract_first_sent_19k_childPar_1_weighted        19k_childPar   \n",
-       "..                                                ...                 ...   \n",
-       "6          text_2_props_19k_class_childPar_1_weighted  19k_class_childPar   \n",
-       "20  abstract_first_sent_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "15                     transe_19k_siblings_2_weighted        19k_siblings   \n",
-       "2                      complex_19k_2_class_2_weighted         19k_2_class   \n",
-       "3                       transe_19k_2_class_1_weighted         19k_2_class   \n",
-       "\n",
-       "                                                    2     3  4  5      6  \\\n",
-       "14                 text_2_props & abstract_first_sent  True  1  2  score   \n",
-       "37                        complex & transe & abstract  True  3  2  score   \n",
-       "44    text_7_props & text_2_props & transe & abstract  True  3  1  score   \n",
-       "45  text_7_props & text_2_props & transe & abstrac...  True  3  2  score   \n",
-       "49  text_7_props & complex & abstract & abstract_f...  True  1  1  score   \n",
-       "..                                                ...   ... .. ..    ...   \n",
-       "6                         text_7_props & text_2_props  True  3  1  score   \n",
-       "20                     abstract & abstract_first_sent  True  3  2  score   \n",
-       "15                                   complex & transe  True  1  2  score   \n",
-       "2                                             complex  True  3  2  score   \n",
-       "3                                              transe  True  3  1  score   \n",
-       "\n",
-       "           7         8         9  \n",
-       "14  0.726555  0.717815  0.008739  \n",
-       "37  0.726303  0.679916  0.046387  \n",
-       "44  0.724034  0.697395  0.026639  \n",
-       "45  0.721092  0.714790  0.006303  \n",
-       "49  0.720924  0.665294  0.055630  \n",
-       "..       ...       ...       ...  \n",
-       "6   0.694370  0.674286  0.020084  \n",
-       "20  0.691429  0.682857  0.008571  \n",
-       "15  0.668655  0.662689  0.005966  \n",
-       "2   0.668487  0.674538 -0.006050  \n",
-       "3   0.668403  0.674454 -0.006050  \n",
-       "\n",
-       "[63 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmCombSumm = pd.read_csv('../data/retrofitting/retro_SVM_Reg_comb_test_Sept_21_2021.csv')\n",
-    "findSVMCombMaximals(svmCombSumm).sort_values(by=[7], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "employed-dover",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Evaluation Datasets-checkpoint.ipynb b/.ipynb_checkpoints/Evaluation Datasets-checkpoint.ipynb
deleted file mode 100644
index 8fce4b9..0000000
--- a/.ipynb_checkpoints/Evaluation Datasets-checkpoint.ipynb	
+++ /dev/null
@@ -1,1177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ultimate-subscriber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "import gzip\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot\n",
-    "from itertools import combinations\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "democratic-memorial",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "ALL_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/claims.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "detailed-founder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_OLD_SOURCE_FILE = \"../data/evaluation/source_files/wordsim_old.csv\"\n",
-    "WORDSIM_OLD_FINAL_FILE = \"../data/evaluation/wordsim_old.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "perceived-robert",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CONCEPTNET_SOURCE_FILE = \"../data/evaluation/source_files/kgtk_conceptnet.tsv\"\n",
-    "CONCEPTNET_INTERM_FILE = \"../data/evaluation/intermediate_files/kgtk_conceptnet.csv\"\n",
-    "CONCEPTNET_FINAL_FILE = \"../data/evaluation/kgtk_conceptnet_final.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "southeast-liberty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WIKI_CS_SOURCE_FILE = '../data/evaluation/source_files/wikidata-cs-20200504.tsv'\n",
-    "WIKI_CS_INTERM_FILE = '../data/evaluation/intermediate_files/wikidata-cs_categorized.csv'\n",
-    "WIKI_CS_FINAL_FILE = '../data/evaluation/wikidata-cs_final.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "caroline-initial",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "automatic-rotation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def uniformize_dataset(wikiDF):\n",
-    "    needed_size = len(wikiDF[wikiDF.category == 'I'])\n",
-    "    M_sampled_set = wikiDF[wikiDF.category == 'M'].sample(needed_size, random_state=13)\n",
-    "    subset = wikiDF[wikiDF.category == 'M']\n",
-    "    mainset = wikiDF\n",
-    "    \n",
-    "    mainset_pairs = set([(row['word1_kg_id'], row['word2_kg_id']) for _,row in mainset.iterrows()])\n",
-    "    nodes = set([row['word1_kg_id'] for _,row in subset.iterrows()] \n",
-    "            + [row['word2_kg_id'] for _,row in subset.iterrows()])\n",
-    "    node_label_mappings = {row['word1_kg_id']: row['node1;label'] for _, row in subset.iterrows()}\n",
-    "    node_label_mappings.update({row['word2_kg_id']: row['node2;label'] for _, row in subset.iterrows()})\n",
-    "    \n",
-    "    all_pairs = set([pair for pair in random.sample(list(combinations(list(nodes), 2)), 10*needed_size)])\n",
-    "    removed_pairs = set()\n",
-    "\n",
-    "    first_line = True\n",
-    "    \n",
-    "    with gzip.open(ALL_EDGES_FILE, 'r') as claims_file:\n",
-    "        for line in tqdm(claims_file, total=491297976): #, total=1034927835):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            curr_pair = (line[1], line[3])\n",
-    "            if curr_pair in all_pairs:\n",
-    "                removed_pairs.add(curr_pair)\n",
-    "    newDF = []\n",
-    "    count = 0\n",
-    "    \n",
-    "    for pair in all_pairs - removed_pairs - mainset_pairs:\n",
-    "        if pair is None or pair[0] is None or pair[1] is None:\n",
-    "            continue\n",
-    "        try:\n",
-    "            newDF.append((pair[0], pair[1], 'U', node_label_mappings.get(pair[0],\"\"), node_label_mappings.get(pair[1],\"\"), None, None, None, None, None))\n",
-    "            count += 1\n",
-    "            if count == needed_size:\n",
-    "                break\n",
-    "        except:\n",
-    "            print(f\"Error while processing pair: {pair}\")\n",
-    "            \n",
-    "    return pd.concat([\n",
-    "            wikiDF[wikiDF.category == 'I'],\n",
-    "            M_sampled_set,\n",
-    "            pd.DataFrame(newDF, columns=subset.columns)\n",
-    "        ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "found-correspondence",
-   "metadata": {},
-   "source": [
-    "# Wordsim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "executed-guard",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df = pd.read_csv(WORDSIM_OLD_SOURCE_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "consecutive-spelling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df['Avg'] = (10 - old_wordsim_df['Human (Mean)']) * 3 / 10 + 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "concrete-apple",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>Avg</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.33920</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "      <td>2.76250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "      <td>2.01250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "      <td>3.38125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "      <td>1.84375</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class      Avg  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  2.33920  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  2.76250  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  2.01250  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  3.38125  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  1.84375  "
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "old_wordsim_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "recreational-cuisine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    349.000000\n",
-       "mean       2.784180\n",
-       "std        0.613491\n",
-       "min        1.000000\n",
-       "25%        2.387500\n",
-       "50%        2.818750\n",
-       "75%        3.287500\n",
-       "max        3.906250\n",
-       "Name: Avg, dtype: float64"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "old_wordsim_df.Avg.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "large-rover",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df['category'] = old_wordsim_df.Avg.apply(lambda score: 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "searching-hollow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df.to_csv(WORDSIM_OLD_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-mozambique",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accredited-consistency",
-   "metadata": {},
-   "source": [
-    "# ConceptNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "steady-mitchell",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "beb4ee57be0a42afaedfece6dac99064",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No. of conceptNet nodes fetched:  1165190\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8bc3ec3c5db74a4eb8c6ba97d08a7a31",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "labelsMap = {}\n",
-    "with open(CONCEPTNET_SOURCE_FILE) as conceptFile:\n",
-    "    firstLine = True\n",
-    "    for line in tqdm(conceptFile):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = line.split('\\t')\n",
-    "        labelsMap[line[3][1:-1]] = None\n",
-    "        labelsMap[line[4][1:-1]] = None\n",
-    "print(\"No. of conceptNet nodes fetched: \",len(labelsMap))\n",
-    "with gzip.open(LABELS_FILE, 'r') as labelsFile:\n",
-    "    firstLine = True\n",
-    "    for line in tqdm(labelsFile):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        line[3] = line[3][1:-5]\n",
-    "        qnode, label = line[1], line[3]\n",
-    "#         print(qnode, label)\n",
-    "        if label in labelsMap:\n",
-    "            if labelsMap[label] == None:\n",
-    "                labelsMap[label] = []\n",
-    "            labelsMap[label].append(qnode)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "studied-pharmacology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchQnode(label):\n",
-    "    if pd.isna(label):\n",
-    "        return None\n",
-    "    label = label[1:-1]\n",
-    "    if label not in labelsMap:\n",
-    "        return None\n",
-    "    else:\n",
-    "        return labelsMap[label]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "imperial-genetics",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept Net dataset size reduced from 3423004 pairs to 99052 pairs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-54-009d05c37ef7>:6: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF1['node1;qnode;len'] = conceptDF1['node1;qnode'].apply(len)\n",
-      "<ipython-input-54-009d05c37ef7>:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF1['node2;qnode;len'] = conceptDF1['node2;qnode'].apply(len)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept Net dataset size reduced from 99052 pairs to 2084 pairs by keeping only pairs which have one-one mapping with qnodes\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "related to                   758\n",
-       "synonym                      708\n",
-       "form of                      219\n",
-       "etymologically related to     82\n",
-       "derived from                  77\n",
-       "is a                          63\n",
-       "has context                   45\n",
-       "at location                   29\n",
-       "antonym                       15\n",
-       "manner of                     15\n",
-       "similar to                    15\n",
-       "part of                       12\n",
-       "causes                         9\n",
-       "distinct from                  6\n",
-       "used for                       5\n",
-       "receives action                4\n",
-       "has prerequisite               4\n",
-       "genus                          2\n",
-       "has a                          2\n",
-       "instance of                    2\n",
-       "has subevent                   2\n",
-       "capital                        2\n",
-       "motivated by goal              2\n",
-       "has last subevent              1\n",
-       "has property                   1\n",
-       "causes desire                  1\n",
-       "genre                          1\n",
-       "has first subevent             1\n",
-       "language                       1\n",
-       "Name: relation;label, dtype: int64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptDF = pd.read_csv(CONCEPTNET_SOURCE_FILE, sep='\\t')\n",
-    "conceptDF['node1;qnode'] = conceptDF['node1;label'].apply(fetchQnode)\n",
-    "conceptDF['node2;qnode'] = conceptDF['node2;label'].apply(fetchQnode)\n",
-    "conceptDF1 = conceptDF[(~conceptDF['node1;qnode'].isna()) & (~conceptDF['node2;qnode'].isna())]\n",
-    "print(f\"Concept Net dataset size reduced from {len(conceptDF)} pairs to {len(conceptDF1)} pairs\")\n",
-    "conceptDF1['node1;qnode;len'] = conceptDF1['node1;qnode'].apply(len)\n",
-    "conceptDF1['node2;qnode;len'] = conceptDF1['node2;qnode'].apply(len)\n",
-    "conceptDF2 = conceptDF1[(conceptDF1['node1;qnode;len'] == 1) & (conceptDF1['node2;qnode;len'] == 1)]\n",
-    "print(f\"Concept Net dataset size reduced from {len(conceptDF1)} pairs to {len(conceptDF2)} pairs by keeping only pairs which have one-one mapping with qnodes\")\n",
-    "conceptDF2['relation;label'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "elder-final",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-55-ffe0073c31e5>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['category'] = conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M')\n",
-      "<ipython-input-55-ffe0073c31e5>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['word1_kg_id'] = conceptDF2['node1;qnode'].apply(lambda p: p[0])\n",
-      "<ipython-input-55-ffe0073c31e5>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['word2_kg_id'] = conceptDF2['node2;qnode'].apply(lambda p: p[0])\n"
-     ]
-    }
-   ],
-   "source": [
-    "conceptDF2['category'] = conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M')\n",
-    "conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M').value_counts()\n",
-    "conceptDF2['word1_kg_id'] = conceptDF2['node1;qnode'].apply(lambda p: p[0])\n",
-    "conceptDF2['word2_kg_id'] = conceptDF2['node2;qnode'].apply(lambda p: p[0])\n",
-    "conceptDF2[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label', 'relation', 'relation;label', 'relation;dimension', 'source', 'sentence']].to_csv(CONCEPTNET_INTERM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "distinguished-mustang",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "18277bf62d5e4098a980327e5a94f8a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/491297976 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df = uniformize_dataset(pd.read_csv(CONCEPTNET_INTERM_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "exact-birthday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "conceptnet_uniform_df.to_csv(CONCEPTNET_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "equivalent-creek",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I    723\n",
-       "U    723\n",
-       "M    723\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "eastern-cargo",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1350</th>\n",
-       "      <td>Q29736947</td>\n",
-       "      <td>Q50414211</td>\n",
-       "      <td>I</td>\n",
-       "      <td>arco</td>\n",
-       "      <td>bowed</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[arco]] is similar to [[bowed]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1351</th>\n",
-       "      <td>Q29782278</td>\n",
-       "      <td>Q12835706</td>\n",
-       "      <td>I</td>\n",
-       "      <td>auxo</td>\n",
-       "      <td>thallo</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1352</th>\n",
-       "      <td>Q50414211</td>\n",
-       "      <td>Q29736947</td>\n",
-       "      <td>I</td>\n",
-       "      <td>bowed</td>\n",
-       "      <td>arco</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[bowed]] is similar to [[arco]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1353</th>\n",
-       "      <td>Q1146652</td>\n",
-       "      <td>Q12835706</td>\n",
-       "      <td>I</td>\n",
-       "      <td>dike</td>\n",
-       "      <td>thallo</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1354</th>\n",
-       "      <td>Q61504726</td>\n",
-       "      <td>Q3366001</td>\n",
-       "      <td>I</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>stripped</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[empty]] is similar to [[stripped]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025</th>\n",
-       "      <td>Q190527</td>\n",
-       "      <td>Q2655953</td>\n",
-       "      <td>I</td>\n",
-       "      <td>value</td>\n",
-       "      <td>prize</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[value]] is a synonym of [[prize]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2037</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>P3828</td>\n",
-       "      <td>I</td>\n",
-       "      <td>weary</td>\n",
-       "      <td>aweary</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[weary]] is a synonym of [[aweary]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2038</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>Q29713327</td>\n",
-       "      <td>I</td>\n",
-       "      <td>weary</td>\n",
-       "      <td>tire</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[weary]] is a synonym of [[tire]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2045</th>\n",
-       "      <td>Q103859907</td>\n",
-       "      <td>Q213449</td>\n",
-       "      <td>I</td>\n",
-       "      <td>whipping</td>\n",
-       "      <td>beating</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[whipping]] is a synonym of [[beating]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2059</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>Q97138958</td>\n",
-       "      <td>I</td>\n",
-       "      <td>years</td>\n",
-       "      <td>days</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[years]] is a synonym of [[days]]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>76 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     word1_kg_id word2_kg_id category node1;label node2;label      relation  \\\n",
-       "1350   Q29736947   Q50414211        I        arco       bowed  /r/SimilarTo   \n",
-       "1351   Q29782278   Q12835706        I        auxo      thallo  /r/SimilarTo   \n",
-       "1352   Q50414211   Q29736947        I       bowed        arco  /r/SimilarTo   \n",
-       "1353    Q1146652   Q12835706        I        dike      thallo  /r/SimilarTo   \n",
-       "1354   Q61504726    Q3366001        I       empty    stripped  /r/SimilarTo   \n",
-       "...          ...         ...      ...         ...         ...           ...   \n",
-       "2025     Q190527    Q2655953        I       value       prize    /r/Synonym   \n",
-       "2037    Q1128240       P3828        I       weary      aweary    /r/Synonym   \n",
-       "2038    Q1128240   Q29713327        I       weary        tire    /r/Synonym   \n",
-       "2045  Q103859907     Q213449        I    whipping     beating    /r/Synonym   \n",
-       "2059    Q1128240   Q97138958        I       years        days    /r/Synonym   \n",
-       "\n",
-       "     relation;label relation;dimension source  \\\n",
-       "1350     similar to                NaN     CN   \n",
-       "1351     similar to                NaN     CN   \n",
-       "1352     similar to                NaN     CN   \n",
-       "1353     similar to                NaN     CN   \n",
-       "1354     similar to                NaN     CN   \n",
-       "...             ...                ...    ...   \n",
-       "2025        synonym                NaN     CN   \n",
-       "2037        synonym                NaN     CN   \n",
-       "2038        synonym                NaN     CN   \n",
-       "2045        synonym                NaN     CN   \n",
-       "2059        synonym                NaN     CN   \n",
-       "\n",
-       "                                      sentence  \n",
-       "1350          [[arco]] is similar to [[bowed]]  \n",
-       "1351                                       NaN  \n",
-       "1352          [[bowed]] is similar to [[arco]]  \n",
-       "1353                                       NaN  \n",
-       "1354      [[empty]] is similar to [[stripped]]  \n",
-       "...                                        ...  \n",
-       "2025       [[value]] is a synonym of [[prize]]  \n",
-       "2037      [[weary]] is a synonym of [[aweary]]  \n",
-       "2038        [[weary]] is a synonym of [[tire]]  \n",
-       "2045  [[whipping]] is a synonym of [[beating]]  \n",
-       "2059        [[years]] is a synonym of [[days]]  \n",
-       "\n",
-       "[76 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df[(conceptnet_uniform_df.category == 'I') & (conceptnet_uniform_df.word1_kg_id != conceptnet_uniform_df.word2_kg_id)].head(100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "structural-league",
-   "metadata": {},
-   "source": [
-    "# Wiki-CS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "incomplete-range",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     72707\n",
-       "meronymy         6886\n",
-       "context          5541\n",
-       "distinctness     4934\n",
-       "utility          2243\n",
-       "antonymy         2184\n",
-       "ordering         2107\n",
-       "material         1426\n",
-       "synonymy         1070\n",
-       "properties       1049\n",
-       "derivation        540\n",
-       "causation         510\n",
-       "similarity        345\n",
-       "attribution       187\n",
-       "other              42\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF = pd.read_csv(WIKI_CS_SOURCE_FILE,sep='\\t')\n",
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])\n",
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')\n",
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "practical-credits",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100701\n",
-       "I      1070\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "agreed-strand",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]\n",
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "secondary-episode",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})\n",
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label', 'relation', 'relation;label', 'relation;dimension', 'source', 'sentence']].to_csv(WIKI_CS_INTERM_FILE,index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "artificial-fantasy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0dd5091b52c44849cf5dbfd30090864",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/491297976 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wikiCS_uniform_df = uniformize_dataset(pd.read_csv(WIKI_CS_INTERM_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "informative-section",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiCS_uniform_df.to_csv(WIKI_CS_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "strong-romance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    1415\n",
-       "I    1415\n",
-       "U    1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiCS_uniform_df.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "discrete-theory",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "convinced-intro",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Master P279 Dataset - Subpart-checkpoint.ipynb b/.ipynb_checkpoints/Master P279 Dataset - Subpart-checkpoint.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/.ipynb_checkpoints/Master P279 Dataset - Subpart-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Master P279 Emb Generation-checkpoint.ipynb b/.ipynb_checkpoints/Master P279 Emb Generation-checkpoint.ipynb
deleted file mode 100644
index f205382..0000000
--- a/.ipynb_checkpoints/Master P279 Emb Generation-checkpoint.ipynb	
+++ /dev/null
@@ -1,5505 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "incorrect-routine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "import os\n",
-    "import h5py\n",
-    "import json\n",
-    "import gzip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "moderate-drunk",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# DWD V2 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1OIZegxxrs_Hv2ZhDsSO-zLVARCR60P01\n",
-    "# SITELINKS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/sitelinks.en.tsv.gz\"\n",
-    "CLAIMS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/claims.tsv.gz\"\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/descriptions.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "exceptional-funeral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikidata-20210215 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1NGtob1BFQ03sXf4yQyYvP13ly3u1Ul5u\n",
-    "# SITELINKS_FILE_V1 = \"../source_dataset_files/wikidata-20210215/sitelinks.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "compressed-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikidata-20201208 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1qbbgjo7pddMdDvQzOSeSaL6lYwj_f5gi\n",
-    "SITELINKS_FILE_V2 = \"../source_dataset_files/wikidata-20201208/sitelinks.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fiscal-appointment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Embedding Related Files\n",
-    "DBPEDIA_SHORT_ABSTRACTS_TTL = \"../data/evaluation/source_files/short-abstracts_lang=en.ttl\"\n",
-    "DBPEDIA_SHORT_ABSTRACTS_CSV = \"../data/evaluation/source_files/short-abstracts_lang=en.csv\"\n",
-    "ABSTRACTS_INTERMEDIATE_FILE = \"../data/embeddings/intermediate_files/abstracts.csv\"\n",
-    "\n",
-    "COMPLEX_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/wikidatadwd.complEx.graph-embeddings.txt\"\n",
-    "TRANSE_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/wikidatadwd.transE.graph-embeddings.txt\"\n",
-    "TEXT_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/text-embeddings-concatenated.tsv.gz\"\n",
-    "\n",
-    "COMPLEX_EMB_FINAL_FILE = \"../data/embeddings/complex_orig_embedding_dict.json\"\n",
-    "TRANSE_EMB_FINAL_FILE = \"../data/embeddings/transe_orig_embedding_dict.json\"\n",
-    "TEXT_EMB_FINAL_FILE = \"../data/embeddings/text_7_props_orig_embedding_dict.json\"\n",
-    "ABS_EMB_FINAL_FILE = \"../data/embeddings/abstract_orig_embedding_dict.json\"\n",
-    "ABS_FIRST_SENT_EMB_FINAL_FILE = \"../data/embeddings/abstract_first_sent_orig_embedding_dict.json\"\n",
-    "\n",
-    "LABELS_EMB_FINAL_FILE = \"../data/embeddings/labels_orig_embedding_dict.json\"\n",
-    "LABELS_DESC_EMB_FINAL_FILE = \"../data/embeddings/labels_n_desc_orig_embedding_dict.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "departmental-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# HAS Embedding Related Files\n",
-    "A_SOURCE_FILE = \"../source_dataset_files/A_walks_analysis/a_embeddings_10x10,min_count=0.kv\"\n",
-    "A_OP_FILE = \"../data/embeddings/has_a_orig_embedding_dict.json\"\n",
-    "\n",
-    "H_SOURCE_FILE = \"../source_dataset_files/H_walks_analysis/h_embeddings_5x8,min_count=21.kv\"\n",
-    "H_OP_FILE = \"../data/embeddings/has_h_orig_embedding_dict.json\"\n",
-    "\n",
-    "S_SOURCE_FILE = \"../source_dataset_files/S_walks_analysis/s_embeddings_5x10,min_count=0.kv\"\n",
-    "S_OP_FILE = \"../data/embeddings/has_s_orig_embedding_dict.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "failing-talent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_CLASS_SIM_FILE = '../data/embeddings/wordsim_class_sim.csv'\n",
-    "WORDSIM_JC_SIM_FILE = '../data/embeddings/wordsim_jc_sim.csv'\n",
-    "WORDSIM_TOP_SIM_FILE = '../data/embeddings/wordsim_top_sim.csv'\n",
-    "\n",
-    "WORDSIM_OLD_CLASS_SIM_FILE = '../data/embeddings/wordsim_old_class_sim.csv'\n",
-    "WORDSIM_OLD_JC_SIM_FILE = '../data/embeddings/wordsim_old_jc_sim.csv'\n",
-    "WORDSIM_OLD_TOP_SIM_FILE = '../data/embeddings/wordsim_old_top_sim.csv'\n",
-    "\n",
-    "DBPEDIA_MC_30_CLASS_SIM_FILE = '../data/embeddings/dbpedia_mc_30_class_sim.csv'\n",
-    "DBPEDIA_MC_30_JC_SIM_FILE = '../data/embeddings/dbpedia_mc_30_jc_sim.csv'\n",
-    "DBPEDIA_MC_30_TOP_SIM_FILE = '../data/embeddings/dbpedia_mc_30_top_sim.csv'\n",
-    "\n",
-    "DBPEDIA_RG_65_CLASS_SIM_FILE = '../data/embeddings/dbpedia_rg_65_class_sim.csv'\n",
-    "DBPEDIA_RG_65_JC_SIM_FILE = '../data/embeddings/dbpedia_rg_65_jc_sim.csv'\n",
-    "DBPEDIA_RG_65_TOP_SIM_FILE = '../data/embeddings/dbpedia_rg_65_top_sim.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "elementary-desktop",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_CHILD_PAR_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_ChildPar.all-distilroberta-v1.csv\"\n",
-    "WORDSIM_FILE = \"../data/evaluation/wordsim353_with_r3.csv\"\n",
-    "WORDSIM_OLD_FILE = \"../data/evaluation/wordsim_old.csv\"\n",
-    "DBPEDIA_MC_30_FINAL_FILE = \"../data/evaluation/mc-30_DBpedia.csv\"\n",
-    "DBPEDIA_RG_65_FINAL_FILE = \"../data/evaluation/rg-65_DBpedia.csv\"\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "noble-draft",
-   "metadata": {},
-   "source": [
-    "# Common Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "broadband-background",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "241698\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_all_nodes():\n",
-    "    \"\"\"\n",
-    "    This function generates the set of all nodes needed for execution\n",
-    "    \"\"\"\n",
-    "    p279ChildPar = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "    wordsim_df = pd.read_csv(WORDSIM_FILE)\n",
-    "    dbpedia_mc_30_df = pd.read_csv(DBPEDIA_MC_30_FINAL_FILE)\n",
-    "    dbpedia_rg_65_df = pd.read_csv(DBPEDIA_RG_65_FINAL_FILE)\n",
-    "#     wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "#     concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + dbpedia_mc_30_df['word1_kg_id'].to_list()\n",
-    "                        + dbpedia_mc_30_df['word2_kg_id'].to_list()\n",
-    "                        + dbpedia_rg_65_df['word1_kg_id'].to_list()\n",
-    "                        + dbpedia_rg_65_df['word2_kg_id'].to_list())\n",
-    "#                         + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "#                         + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "#                         + concept_net_df['word1_kg_id'].to_list()\n",
-    "#                         + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parliamentary-documentation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fillCoverage(embedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv(WORDSIM_FILE)\n",
-    "    wordSim353AnnotDF_set = set(wordSim353AnnotDF_New['word1_kg_id'].to_list() + wordSim353AnnotDF_New['word2_kg_id'].to_list())\n",
-    "    embed_size = len(embedDict[next(iter(embedDict))])\n",
-    "#     print(embed_size)\n",
-    "    count = 0\n",
-    "    for word in wordSim353AnnotDF_set:\n",
-    "        if word not in embedDict:\n",
-    "            embedDict[word] = np.zeros((embed_size))\n",
-    "            count += 1\n",
-    "    print(f\"Added {count} corrections\")\n",
-    "    return embedDict\n",
-    "\n",
-    "def deserializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = np.array(embedDict[key2])\n",
-    "    return embedDict\n",
-    "\n",
-    "def serializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = embedDict[key2].tolist() if type(embedDict[key2]) != list else embedDict[key2]\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "constant-guarantee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels(node_set):\n",
-    "    labels_dict = {}\n",
-    "    first_line = True\n",
-    "    with gzip.open(LABELS_FILE, 'r') as labelsFile:\n",
-    "        firstLine = True\n",
-    "        for line in tqdm(labelsFile, total=41845781):\n",
-    "            if firstLine:\n",
-    "                firstLine = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split('\\t')\n",
-    "            line[3] = line[3][1:-5]\n",
-    "            qnode, label = line[1], line[3]\n",
-    "    #         print(qnode, label)\n",
-    "            if qnode in node_set:\n",
-    "                labels_dict[qnode] = label\n",
-    "    return labels_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "thick-charter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels_n_desc(node_set):\n",
-    "    labels_dict = get_labels(node_set)\n",
-    "    first_line = True\n",
-    "    with gzip.open(DESCRIPTIONS_FILE, 'r') as labelsFile:\n",
-    "        firstLine = True\n",
-    "        for line in tqdm(labelsFile, total=34700043):\n",
-    "            if firstLine:\n",
-    "                firstLine = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split('\\t')\n",
-    "            line[3] = line[3][1:-5]\n",
-    "            qnode, label = line[1], line[3]\n",
-    "    #         print(qnode, label)\n",
-    "            if qnode in node_set:\n",
-    "                if qnode in labels_dict:\n",
-    "                    labels_dict[qnode] += ' ' + label\n",
-    "                else:\n",
-    "                    raise \"Label not present\"\n",
-    "    return labels_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "driven-yeast",
-   "metadata": {},
-   "source": [
-    "# Complex + Transe Embeddings Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "dimensional-milton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "complex_emb_dict = json.load(open(COMPLEX_EMB_FINAL_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "correct-gentleman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "64deda8236084d79bce85a2fd249dec9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "complex_emb_dict = {}\n",
-    "with open(COMPLEX_EMB_SOURCE_FILE) as complex_file:\n",
-    "    for line in tqdm(complex_file, total=53002671):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.strip().split()\n",
-    "        if line[0] in allNodes and line[0] not in complex_emb_dict:\n",
-    "            complex_emb_dict[line[0]] = [float(elem) for elem in line[1:]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "proved-buffer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(complex_emb_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "hazardous-amazon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(complex_emb_dict, open(COMPLEX_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "genuine-storm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transe_emb_dict = json.load(open(TRANSE_EMB_FINAL_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "needed-passion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8636f544aa484f6d9785723a4a96e83b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "transe_emb_dict = {}\n",
-    "with open(TRANSE_EMB_SOURCE_FILE) as complex_file:\n",
-    "    for line in tqdm(complex_file, total=53002671):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.strip().split()\n",
-    "        if line[0] in allNodes and line[0] not in transe_emb_dict:\n",
-    "            transe_emb_dict[line[0]] = [float(elem) for elem in line[1:]]\n",
-    "len(transe_emb_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "classified-chick",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(transe_emb_dict, open(TRANSE_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "steady-cliff",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suffering-zealand",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "brief-timer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "238889\n"
-     ]
-    }
-   ],
-   "source": [
-    "# p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "# print(len(set(p279ChildPar.node1.to_list() \n",
-    "#                         + p279ChildPar.node2.to_list())))\n",
-    "\n",
-    "# # Load complex, transe embedding files and entity names file\n",
-    "# compf = h5py.File('../data/complTrans/complEx.h5','r')\n",
-    "# transf = h5py.File('../data/complTrans/transE.h5','r')\n",
-    "# ent_names = json.load(open('../data/complTrans/entity_names_all_0.json'))\n",
-    "# allNodes = get_all_nodes()\n",
-    "# # json.dump(list(p279QnodesList), open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json', 'w'))\n",
-    "\n",
-    "# complexEmb = {qnode: emb for emb, qnode in zip(compf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "# transeEmb = {qnode: emb for emb, qnode in zip(transf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "# print(f\"Out of {len(ent_names)} embeddings, retaining {len(transeEmb)} embeddings\")\n",
-    "\n",
-    "# def serialize_embedding_dict(embed_dict):\n",
-    "#     for key2 in embed_dict.keys():\n",
-    "#         embed_dict[key2] = embed_dict[key2].tolist() if type(embed_dict[key2]) != list else embed_dict[key2]\n",
-    "#     return embed_dict\n",
-    "\n",
-    "# json.dump(serialize_embedding_dict(complexEmb),open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json','w'))\n",
-    "# json.dump(serialize_embedding_dict(transeEmb),open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json','w'))\n",
-    "# # complexEmb = json.load(open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json'))\n",
-    "# # transeEmb = json.load(open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continued-locking",
-   "metadata": {},
-   "source": [
-    "# Text Embedding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "polished-divorce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f97b30760e841da87be90aebef9c8cd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "text_emb_dict = {}\n",
-    "with gzip.open(TEXT_EMB_SOURCE_FILE) as file:\n",
-    "    for line in tqdm(file):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        if line[1] == 'text_embedding' and line[0] in allNodes:\n",
-    "            text_emb_dict[line[0]] = [float(elem) for elem in line[2].split(',')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "raising-boost",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text_emb_dict, open(TEXT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "pressed-atlanta",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_emb_dict = json.load(open('../data/embeddings/archived/text_7_props_orig_embedding_dict.json.old'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "composed-complement",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a75bc167e9b449f88e4df7ebb19bca77",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/241698 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "missing_nodes = []\n",
-    "for node in tqdm(allNodes):\n",
-    "    if node not in text_emb_dict:\n",
-    "        missing_nodes.append(node)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "ceramic-southeast",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0db4a305d6dc4f1e85dfdc02ea564537",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "missing_nodes_set = set(missing_nodes)\n",
-    "new_file = []\n",
-    "with gzip.open(CLAIMS_FILE, 'r') as all_claims_file:\n",
-    "    firstLine = True\n",
-    "    for ogline in tqdm(all_claims_file, total=491297976):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = ogline.decode('utf-8').strip().split('\\t')\n",
-    "        line[3] = line[3][1:-5]\n",
-    "        qnode, label = line[1], line[3]\n",
-    "#         print(qnode, label)\n",
-    "        if qnode in missing_nodes_set:\n",
-    "            new_file.append(ogline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "nearby-market",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allowed_props = set(['P31', 'P279', 'P106', 'P39', 'P1382', 'P373', 'P452'])\n",
-    "new_file1 = []\n",
-    "for line in new_file:\n",
-    "    line1 = line.decode('utf-8').strip().split('\\t')\n",
-    "    if line1[2] in allowed_props:\n",
-    "        new_file1.append(line.decode('utf-8'))\n",
-    "new_file1 = ['id\\tnode1\\tlabel\\tnode2\\trank\\tnode2;wikidatatype\\n'] + new_file1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "liberal-brown",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../output/text-embeddings/missing_nodes.tsv', 'w') as f:\n",
-    "    f.writelines(new_file1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "dimensional-observation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#     --model sentence-transformers/roberta-large-nli-mean-tokens   \\\n",
-    "q1 = \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../output/text-embeddings/missing_nodes.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file \" + LABELS_FILE + \"  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence > ../output/text-embeddings/P279-text-embedding-7-props-missing-qnodes.tsv\"\n",
-    "os.system(q1 + \" \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "worth-desperate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7_missingnodes = pd.read_csv(\"../output/text-embeddings/P279-text-embedding-7-props-missing-qnodes.tsv\", sep='\\t')\n",
-    "text7_missingnodes = text7_missingnodes[text7_missingnodes.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "universal-alignment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7_missingnodes['value'] = text7_missingnodes['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "interested-phenomenon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7EmbDict = {row['node']: row['value'] for _,row in text7_missingnodes.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "lucky-windsor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in text7EmbDict.keys():\n",
-    "    if key not in text_emb_dict:\n",
-    "        text_emb_dict[key] = text7EmbDict[key]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "limited-present",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text_emb_dict, open(TEXT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "complex-mexico",
-   "metadata": {},
-   "source": [
-    "## Old technique follows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "neural-gibson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "p279QnodesList = list(set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "instructional-weather",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingNodes = allNodes - set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "specified-clear",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37038"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingNodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "conditional-brooks",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Split main file into sub-files for groups of properties for multi-processing\n",
-    "\n",
-    "# bsize = len(p279QnodesList) // 250\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(p279QnodesList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(p279QnodesList[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "# #     print(len(q1))\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "utility-fleet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for cnt in range(290,503):\n",
-    "    os.system(\"rm ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "alleged-strength",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Split main file into sub-files for groups of properties for multi-processing\n",
-    "missingNodes = list(missingNodes)\n",
-    "bsize = 1000\n",
-    "cnt = 252\n",
-    "for i in tqdm(range(0, len(missingNodes), bsize)):\n",
-    "    q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(missingNodes[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     print(len(q1))\n",
-    "    cnt += 1\n",
-    "#     print(q1)\n",
-    "    os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "signal-island",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def checkIfFileContainsLines(file):\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "fatal-broadway",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countFileLines(file):\n",
-    "    count = 0\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            count += 1\n",
-    "    return count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "separate-satin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "runCommCnt = 1\n",
-    "# 252\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 = \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(11*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "surprising-burning",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "for cnt in tqdm(range(1,290)):\n",
-    "    if countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") != 4097:\n",
-    "        print(cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bottom-lodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "# roberta-large-nli-mean-tokens\n",
-    "runCommCnt = 0\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 += \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(13*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "damaged-browse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "collective-april",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-1.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "decent-yorkshire",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.74755263,1.6350263,-0.73952675,1.0463063,-0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>night shift, work shift during nighttime hours...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.25261465,0.06285462,0.029052094,0.50796187,0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>avenue, thoroughfare named \\\"avenue\\\" is thoro...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98970128</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node            property  \\\n",
-       "0  Q99738027      text_embedding   \n",
-       "1  Q99738027  embedding_sentence   \n",
-       "2  Q99228502      text_embedding   \n",
-       "3  Q99228502  embedding_sentence   \n",
-       "4  Q98970128      text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  0.74755263,1.6350263,-0.73952675,1.0463063,-0....  \n",
-       "1  night shift, work shift during nighttime hours...  \n",
-       "2  0.25261465,0.06285462,0.029052094,0.50796187,0...  \n",
-       "3  avenue, thoroughfare named \\\"avenue\\\" is thoro...  \n",
-       "4  0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# temp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "satisfactory-speech",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text2EmbArr.append(temp)\n",
-    "text2Emb = pd.concat(text2EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unavailable-competition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "concerned-april",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text7EmbArr.append(temp)\n",
-    "text7Emb = pd.concat(text7EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "australian-enforcement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb = text2Emb[text2Emb.node.apply(lambda p: p in allNodes)]\n",
-    "text7Emb = text7Emb[text7Emb.node.apply(lambda p: p in allNodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "closed-treatment",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have 2prop text embeddings for 278467 nodes and 7prop for 277587 nodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"We have 2prop text embeddings for {len(text2Emb)} nodes and 7prop for {len(text7Emb)} nodes\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "duplicate-agency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb['value'] = text2Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "text7Emb['value'] = text7Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "framed-third",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2EmbDict = {row['node']: row['value'] for _,row in text2Emb.iterrows()}\n",
-    "text7EmbDict = {row['node']: row['value'] for _,row in text7Emb.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "peaceful-andrew",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text2EmbDict, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump(text7EmbDict, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "considered-river",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sustained-playback",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "requested-state",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suited-going",
-   "metadata": {},
-   "source": [
-    "# Abstract Embeddings Generation\n",
-    "\n",
-    "Downloaded short abstracts file from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)\n",
-    "\n",
-    "Then, we extract the abstracts file from the bz2 file using: `bzip2 -d short-abstracts_lang=en.ttl.bz2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "former-editor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0038713a1604ccb9c2e5499615fbc43",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# cnt = 0\n",
-    "# p1s = []\n",
-    "# p11s = []\n",
-    "# p2s = []\n",
-    "# lines = []\n",
-    "# with open(DBPEDIA_SHORT_ABSTRACTS_TTL, 'r', encoding='utf-8') as f:\n",
-    "#     for line in tqdm(f):\n",
-    "#         p1 = line[:line.find(\" \")]\n",
-    "#         p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "#         line = line[line.find(\" \")+1:]\n",
-    "#         p2 = line[:line.find(\" \")]\n",
-    "#         line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "#         p1s.append(p1)\n",
-    "#         p11s.append(p11)\n",
-    "#         p2s.append(p2)\n",
-    "#         lines.append(line)\n",
-    "# df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "# df1.to_csv(DBPEDIA_SHORT_ABSTRACTS_CSV)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "massive-camera",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n",
-      "From 116088 Qnodes, there are 5134 sitelink Qnodes which do not have a short abstract i.e 110954 have a short abstract\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51a5ed2cfc8e45c8815fac2f8855a3be",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/241698 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(DBPEDIA_SHORT_ABSTRACTS_CSV, skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "df1.loc[df1[df1.index.duplicated()].index]\n",
-    "sitelinksDF = pd.read_csv(SITELINKS_FILE_V2, sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in allNodes)]\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}\n",
-    "\n",
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "\n",
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)\n",
-    "\n",
-    "currNodes = set(abstractsDF2.node1.tolist())\n",
-    "\n",
-    "correctedRows = []\n",
-    "for key in tqdm(allNodes):\n",
-    "    if key not in currNodes:\n",
-    "        correctedRows.append([None, None, key, None, None, None, None, None, None, labelsDict[key][1:-4] if key in labelsDict else None, descDict[key][1:-4] if key in descDict else None, None])\n",
-    "        \n",
-    "abstractsDF3 = pd.concat([abstractsDF2, pd.DataFrame(correctedRows, columns=abstractsDF2.columns)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "geographic-penguin",
-   "metadata": {},
-   "source": [
-    "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-    "There are 8637003 sitelinks present in the dataset corresponding to 8637003 unique node1s (Qxxx), 8563928 unique labels (text)\n",
-    "From 116430 Qnodes, there are 5707 sitelink Qnodes which do not have a short abstract i.e 110723 have a short abstract\n",
-    "\n",
-    "\n",
-    "\n",
-    "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-    "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n",
-    "\n",
-    "From 122585 Qnodes, there are 5661 sitelink Qnodes which do not have a short abstract i.e 116924 have a short abstract\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "genetic-sympathy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "241698\n"
-     ]
-    }
-   ],
-   "source": [
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] != '' and row['node1_desc'] != '' and not(pd.isna(row['node1_label'])) and not(pd.isna(row['node1_desc'])):\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']\n",
-    "    elif row['node1_label'] != '' and not(pd.isna(row['node1_label'])):\n",
-    "        return row['node1_label']\n",
-    "    else:\n",
-    "        return None\n",
-    "    \n",
-    "abstractsDF3['abstract'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF3['abstract_firstSent'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))\n",
-    "abstractsDF3 = abstractsDF3[~abstractsDF3.abstract.isna()]\n",
-    "abstractsDF3 = abstractsDF3.reset_index()\n",
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0']).reset_index()\n",
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0'])\n",
-    "print(len(abstractsDF3))\n",
-    "abstractsDF3.to_csv(ABSTRACTS_INTERMEDIATE_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wicked-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "desirable-fifteen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (1,2,4,5,7,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2 = pd.read_csv(ABSTRACTS_INTERMEDIATE_FILE)\n",
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "administrative-wesley",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF2[abstractsDF2.abstract == (abstractsDF2.node1_label +  ' ' + abstractsDF2.node1_desc)].to_csv('../data/Master_P279_dataset/temppppp.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "crude-result",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:2')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "impressive-comedy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "coordinate-assurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc9488221a204b5e9558749419087c8c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "316.05097579956055 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3eb77d77acb549d6b3b91b3a31b7f4d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "260.16796946525574 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "absEmbSeries = getSentEmbeddings(abstractsDF2.abstract, modelName)\n",
-    "absFirstSentEmbSeries = getSentEmbeddings(abstractsDF2.abstract_firstSent, modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "valued-toilet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "absEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absEmbSeries)}\n",
-    "absFirstSentEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absFirstSentEmbSeries)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "refined-belarus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(absEmbDict, open(ABS_EMB_FINAL_FILE, 'w'))\n",
-    "json.dump(absFirstSentEmbDict, open(ABS_FIRST_SENT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "knowing-relay",
-   "metadata": {},
-   "source": [
-    "# H,A,S embeddings\n",
-    "\n",
-    "Fetched from `sita` `/data02/profiling/kgtk/entity_profiling/output/wikidata-20210215-dwd/`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "ideal-accreditation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def store_relevant_embeddings(wvec, fname):\n",
-    "    tempEmb = {key: wvec[key] for key in wvec.index_to_key if key in allNodes}\n",
-    "    print(f\"Original Length: {len(wvec.index_to_key)}, No. of keys stored: {len(tempEmb)}\")\n",
-    "    json.dump(serializeEmbeddingDict(tempEmb),open(fname, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "wireless-playing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gensim.models import KeyedVectors, Word2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "cordless-bachelor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Length: 12106870, No. of keys stored: 27876\n",
-      "Original Length: 19593942, No. of keys stored: 166201\n",
-      "Original Length: 39030788, No. of keys stored: 116993\n",
-      "CPU times: user 5min 54s, sys: 2min 1s, total: 7min 56s\n",
-      "Wall time: 18min 51s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "a_key_vec = KeyedVectors.load(A_SOURCE_FILE)\n",
-    "store_relevant_embeddings(a_key_vec, A_OP_FILE)\n",
-    "\n",
-    "h_key_vec = KeyedVectors.load(H_SOURCE_FILE)\n",
-    "store_relevant_embeddings(h_key_vec, H_OP_FILE)\n",
-    "\n",
-    "s_key_vec = KeyedVectors.load(S_SOURCE_FILE)\n",
-    "store_relevant_embeddings(s_key_vec, S_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "technological-anime",
-   "metadata": {},
-   "source": [
-    "# Label Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "emerging-money",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_dict = get_labels(allNodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "accepted-consultancy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99bb9a58eb3f4f97b94b592d389920c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "98.77918219566345 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "embs = getSentEmbeddings(pd.Series(labels_dict.values()), modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "native-president",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_emb_dict = {k:v.tolist() for k, v in (zip(labels_dict.keys(), embs))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "chronic-figure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(labels_emb_dict, open(LABELS_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "worldwide-theorem",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "geographic-relationship",
-   "metadata": {},
-   "source": [
-    "# Label + Desc Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "raising-taxation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b13c4c43b704c5d82c81f2442265f62",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/41845781 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbffd7e813e04d80ac616f1650211f47",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/34700043 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "240aaa8b6c4746f2af9ad9480aa82141",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "203.93888425827026 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "labels_desc_dict = get_labels_n_desc(allNodes)\n",
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "embs = getSentEmbeddings(pd.Series(labels_desc_dict.values()), modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "atlantic-wayne",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_desc_emb_dict = {k:v.tolist() for k, v in (zip(labels_desc_dict.keys(), embs))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "light-yugoslavia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(labels_desc_emb_dict, open(LABELS_DESC_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "entitled-validity",
-   "metadata": {},
-   "source": [
-    "# Direct sim files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "standing-prevention",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9e72fb0e29145ff8397285e8571b532",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/334 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "35d88da70505434989e021fbaf44804d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/334 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f6f979b050d428eae27738044df06ea",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/334 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(WORDSIM_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(WORDSIM_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(WORDSIM_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(WORDSIM_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "physical-swift",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "      <th>embedding_cos_sim</th>\n",
-       "      <th>Resp_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.982734</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.969884</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.951077</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "      <td>1.264601</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ... P_Dim  Avg     Stdev  H_orig  H_reversed  word1_kg_id  word2_kg_id  \\\n",
-       "0  ...   NaN  3.6  0.547723  2.1250      7.8750       Q34211         Q454   \n",
-       "1  ...   NaN  3.6  0.547723  3.0625      6.9375       Q34211    Q13648784   \n",
-       "2  ...   NaN  3.6  0.547723  4.0625      5.9375        Q8333      Q178022   \n",
-       "3  ...     u  3.0  0.000000  5.0625      4.9375        Q8333    Q21004260   \n",
-       "4  ...     s  2.2  0.447214  4.8750      5.1250       Q13371       Q49112   \n",
-       "\n",
-       "  category embedding_cos_sim         Resp_code  \n",
-       "0        U          3.982734  <Response [200]>  \n",
-       "1        U          3.969884  <Response [200]>  \n",
-       "2        U          4.000000  <Response [200]>  \n",
-       "3        M          3.951077  <Response [200]>  \n",
-       "4        M          1.264601  <Response [200]>  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "word_sim_class_sim_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "operational-survival",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4899ff6e2e264789be04ed8d2b8ca6fd",
-       "version_major": 2,
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "19e8420e998247799b6400f96fdcde97",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "edf81b9b4fa940da9d2f2ed53787af66",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(WORDSIM_OLD_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(WORDSIM_OLD_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(WORDSIM_OLD_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(WORDSIM_OLD_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "polish-reputation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25e3efd53b874d659a6d0d1f4c191bd4",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a92e91dc168249d18e6be8ea04e29f5f",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf7de27a4ce04f42baeaf158bc5967b4",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(DBPEDIA_MC_30_FINAL_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(DBPEDIA_MC_30_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(DBPEDIA_MC_30_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(DBPEDIA_MC_30_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "quick-worker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33a2e30b210a4b349ce96b317490fefd",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51f0858cbd1e4b25838f2fe0e27487a6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "90936fa725984c0cb5b50e31476c4a38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(DBPEDIA_RG_65_FINAL_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(DBPEDIA_RG_65_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(DBPEDIA_RG_65_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(DBPEDIA_RG_65_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spatial-excerpt",
-   "metadata": {},
-   "source": [
-    "# Summary of embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "wooden-medicare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))\n",
-    "\n",
-    "complexEmb = json.load(open('../data/Master_P279_dataset/masterComplexEmb.json'))\n",
-    "transeEmb = json.load(open('../data/Master_P279_dataset/masterTranseEmb.json'))\n",
-    "\n",
-    "text2Emb = json.load(open('../data/Master_P279_dataset/text2Emb.json'))\n",
-    "text7Emb = json.load(open('../data/Master_P279_dataset/text7Emb.json'))\n",
-    "\n",
-    "abstractEmb = json.load(open('../data/Master_P279_dataset/abstractEmb.json'))\n",
-    "abstractFirstSentEmb = json.load(open('../data/Master_P279_dataset/abstractFirstSentEmb.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "distributed-magazine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump({key:val for key, val in complexEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in transeEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text2Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text7Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractFirstSentEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_first_sent_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "disturbed-better",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "advised-cyprus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summArr = []\n",
-    "cnt = countOverlap(complexEmb, p279QnodesList)\n",
-    "summArr.append(['complex', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(transeEmb, p279QnodesList)\n",
-    "summArr.append(['transe', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text2Emb, p279QnodesList)\n",
-    "summArr.append(['text2', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text7Emb, p279QnodesList)\n",
-    "summArr.append(['text7', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractEmb, p279QnodesList)\n",
-    "summArr.append(['abstract', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractFirstSentEmb, p279QnodesList)\n",
-    "summArr.append(['abstractFirstSent', cnt, cnt / len(p279QnodesList) * 100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "czech-keeping",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "238889"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279QnodesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "employed-christmas",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>text2</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>text7</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstractFirstSent</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           embedding   count  Coverage Percentage\n",
-       "0            complex  238448            99.815395\n",
-       "1             transe  238448            99.815395\n",
-       "2              text2  238889           100.000000\n",
-       "3              text7  238889           100.000000\n",
-       "4           abstract  105828            44.300072\n",
-       "5  abstractFirstSent  105828            44.300072"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(summArr, columns=['embedding', 'count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changing-strategy",
-   "metadata": {},
-   "source": [
-    "# Embeddings correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "purple-raising",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedDictMaster = {}\n",
-    "subsetEmbedDictMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "round-product",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedKeys = ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in masterEmbedKeys:\n",
-    "    masterEmbedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "metallic-insulin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subsetEmbedKeys = ['text_7props', 'text_2props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in subsetEmbedKeys:\n",
-    "    subsetEmbedDictMaster[key1] = json.load(open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "assigned-parameter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "olympic-yemen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_pairs = {(row['word1_kg_id'], row['word2_kg_id']) for _, row in wordSim353AnnotDF_New.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "welcome-disorder",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by text_7props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by text_2props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by complex embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by transe embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by abstract embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by abstract_first_sent embeddings created for 19k retrofitting: 343\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in subsetEmbedKeys:\n",
-    "    print(f\"Pair Coverage by {key1} embeddings created for 19k retrofitting: {sum([row[0] in subsetEmbedDictMaster[key1] and row[1] in subsetEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "northern-psychiatry",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by old text_7_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old text_2_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old complex embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old transe embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old abstract embeddings created for 19k retrofitting: 183\n",
-      "Pair Coverage by old abstract_first_sent embeddings created for 19k retrofitting: 183\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by old {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "contrary-casino",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_set = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "alleged-polish",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "masterEmbCorrections = defaultdict(list)\n",
-    "for node in wordSim353AnnotDF_New_set:\n",
-    "    for i in range(len(masterEmbedKeys)):\n",
-    "        if node not in masterEmbedDictMaster[masterEmbedKeys[i]] and node in wordSim353AnnotDF_New_set:\n",
-    "            masterEmbCorrections[masterEmbedKeys[i]].append(node)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "periodic-buffer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['abstract', 'abstract_first_sent', 'text_7_props', 'text_2_props', 'complex', 'transe'])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "masterEmbCorrections.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "awful-signal",
-   "metadata": {},
-   "source": [
-    "## Complex, Transe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "exceptional-acting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# correctedComplexEmb = {}\n",
-    "# correctedTranseEmb = {}\n",
-    "# for wordID in masterEmbCorrections['complex']:\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "#         correctedComplexEmb[wordID] = [float(p) for p in resp['graph_embedding_complex'].split(',')]\n",
-    "#         correctedTranseEmb[wordID] = [float(p) for p in resp['graph_embeddings_transe'].split(',')]\n",
-    "#     except:\n",
-    "#         print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "assigned-journey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51850ec9544547f293820bd9e94091f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4de00c52596f4ce3b5d17ee9ef73b068",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1h 33min 17s, sys: 2min 38s, total: 1h 35min 56s\n",
-      "Wall time: 1h 35min 28s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "correctedComplexEmb = {qnode: emb for emb, qnode in tqdm(zip(f['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}\n",
-    "correctedTranseEmb = {qnode: emb for emb, qnode in tqdm(zip(transf['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "female-scope",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(list(correctedComplexEmb.items())[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "valuable-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(masterEmbedDictMaster['complex'][list(masterEmbedDictMaster['complex'].keys())[0]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "religious-celebration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for node, emb in correctedComplexEmb.items():\n",
-    "    masterEmbedDictMaster['complex'][node] = emb\n",
-    "for node, emb in correctedTranseEmb.items():\n",
-    "    masterEmbedDictMaster['transe'][node] = emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spatial-writer",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "periodic-laundry",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "motivated-soundtrack",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imported-advance",
-   "metadata": {},
-   "source": [
-    "## Text Embeddings Correction file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "historical-conservative",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(masterEmbCorrections['text_7_props']) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv -v True\"\n",
-    "os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "overhead-commission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "cooked-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "static-failure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv', sep='\\t')\n",
-    "corrected2Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "spare-flexibility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = corrected7Emb[corrected7Emb.property == 'text_embedding']\n",
-    "corrected7Emb['value'] = corrected7Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "\n",
-    "corrected2Emb = corrected2Emb[corrected2Emb.property == 'text_embedding']\n",
-    "corrected2Emb['value'] = corrected2Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "minute-oakland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in corrected7Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_7_props'][row['node']] = row['value']\n",
-    "for _, row in corrected2Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_2_props'][row['node']] = row['value']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documentary-fluid",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "analyzed-naples",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "psychological-brighton",
-   "metadata": {},
-   "source": [
-    "## Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "meaning-spanking",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "masterEmbCorrections_abs_set = set(masterEmbCorrections['abstract'])\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "offensive-enclosure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "better-tuner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 58 Qnodes, there are 16 sitelink Qnodes which do not have a short abstract i.e 42 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "# abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "invalid-fiction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] == \"\" and row['node1_desc'] == \"\":\n",
-    "        return None\n",
-    "    else:\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "opened-drink",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)\n",
-    "abstractsDF2 = abstractsDF2.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "affected-reproduction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['abstract'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "actual-communication",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "quantitative-tumor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "turned-retail",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "heard-freedom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2.drop(columns=['index']).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "forty-southeast",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>level_0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Luxuries</td>\n",
-       "      <td>Q10953913-wikipedia_sitelink-538fe3-0</td>\n",
-       "      <td>Q10953913</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Luxuries</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>luxuryBehavior, expenses or equipment that far...</td>\n",
-       "      <td>luxury</td>\n",
-       "      <td>Behavior, expenses or equipment that far excee...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Potato</td>\n",
-       "      <td>Q10998-wikipedia_sitelink-56b85c-0</td>\n",
-       "      <td>Q10998</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Potato</td>\n",
-       "      <td>10709.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Potato&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "      <td>potato</td>\n",
-       "      <td>species of plant</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>Q111-wikipedia_sitelink-9ff296-0</td>\n",
-       "      <td>Q111</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Mars</td>\n",
-       "      <td>1803088.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Mars&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>fourth planet from the Sun</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Dawn</td>\n",
-       "      <td>Q11326182-wikipedia_sitelink-ae2918-0</td>\n",
-       "      <td>Q11326182</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dawn</td>\n",
-       "      <td>97544.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Dawn&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "      <td>dawn</td>\n",
-       "      <td>time that marks the beginning of the twilight ...</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Change_(philosophy)</td>\n",
-       "      <td>Q1150070-wikipedia_sitelink-81cf5f-0</td>\n",
-       "      <td>Q1150070</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Change_(philosophy)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>changeprocess, event or action that deviates f...</td>\n",
-       "      <td>change</td>\n",
-       "      <td>process, event or action that deviates from th...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level_0  index         trimmedNode2                                     id  \\\n",
-       "0        0      0             Luxuries  Q10953913-wikipedia_sitelink-538fe3-0   \n",
-       "1        1      1               Potato     Q10998-wikipedia_sitelink-56b85c-0   \n",
-       "2        2      2                 Mars       Q111-wikipedia_sitelink-9ff296-0   \n",
-       "3        3      3                 Dawn  Q11326182-wikipedia_sitelink-ae2918-0   \n",
-       "4        4      4  Change_(philosophy)   Q1150070-wikipedia_sitelink-81cf5f-0   \n",
-       "\n",
-       "       node1               label  \\\n",
-       "0  Q10953913  wikipedia_sitelink   \n",
-       "1     Q10998  wikipedia_sitelink   \n",
-       "2       Q111  wikipedia_sitelink   \n",
-       "3  Q11326182  wikipedia_sitelink   \n",
-       "4   Q1150070  wikipedia_sitelink   \n",
-       "\n",
-       "                                              node2     ignore  \\\n",
-       "0             http://en.wikipedia.org/wiki/Luxuries        NaN   \n",
-       "1               http://en.wikipedia.org/wiki/Potato    10709.0   \n",
-       "2                 http://en.wikipedia.org/wiki/Mars  1803088.0   \n",
-       "3                 http://en.wikipedia.org/wiki/Dawn    97544.0   \n",
-       "4  http://en.wikipedia.org/wiki/Change_(philosophy)        NaN   \n",
-       "\n",
-       "                                    url  \\\n",
-       "0                                   NaN   \n",
-       "1  <http://dbpedia.org/resource/Potato>   \n",
-       "2    <http://dbpedia.org/resource/Mars>   \n",
-       "3    <http://dbpedia.org/resource/Dawn>   \n",
-       "4                                   NaN   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "                                            abstract node1_label  \\\n",
-       "0  luxuryBehavior, expenses or equipment that far...      luxury   \n",
-       "1  The potato is a root vegetable native to the A...      potato   \n",
-       "2  Mars is the fourth planet from the Sun and the...        Mars   \n",
-       "3  Dawn is the time that marks the beginning of t...        dawn   \n",
-       "4  changeprocess, event or action that deviates f...      change   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0  Behavior, expenses or equipment that far excee...   \n",
-       "1                                   species of plant   \n",
-       "2                         fourth planet from the Sun   \n",
-       "3  time that marks the beginning of the twilight ...   \n",
-       "4  process, event or action that deviates from th...   \n",
-       "\n",
-       "                                  abstract_firstSent  \n",
-       "0                                                nan  \n",
-       "1  The potato is a root vegetable native to the A...  \n",
-       "2  Mars is the fourth planet from the Sun and the...  \n",
-       "3  Dawn is the time that marks the beginning of t...  \n",
-       "4                                                nan  "
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "multiple-offer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getIndSentEmbeddings(sent, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode([sent])\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "sustainable-breakdown",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.6419482231140137 s\n",
-      "0.5260367393493652 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "abstractsDF2['abs_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract, 'bert-base-nli-mean-tokens')))\n",
-    "abstractsDF2['abs_firstSent_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "usual-selling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in abstractsDF2.iterrows():\n",
-    "    masterEmbedDictMaster['abstract'][row['node1']] = row['abs_emb']\n",
-    "    masterEmbedDictMaster['abstract_first_sent'][row['node1']] = row['abs_firstSent_emb']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "promising-owner",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.37706875801086426 s\n",
-      "0.3001420497894287 s\n",
-      "0.370746374130249 s\n",
-      "0.6896324157714844 s\n",
-      "0.33779358863830566 s\n",
-      "0.3965473175048828 s\n",
-      "0.3200962543487549 s\n",
-      "0.3489806652069092 s\n",
-      "0.3413431644439697 s\n",
-      "0.32114505767822266 s\n",
-      "0.3811838626861572 s\n",
-      "0.34630370140075684 s\n",
-      "0.37790727615356445 s\n",
-      "0.26860570907592773 s\n",
-      "0.3601953983306885 s\n",
-      "0.3713240623474121 s\n",
-      "0.34137582778930664 s\n",
-      "0.33736181259155273 s\n",
-      "0.37023448944091797 s\n",
-      "0.31382036209106445 s\n",
-      "0.35136938095092773 s\n",
-      "0.37309718132019043 s\n",
-      "0.33543896675109863 s\n",
-      "0.38199710845947266 s\n",
-      "0.3740067481994629 s\n",
-      "0.3278031349182129 s\n",
-      "0.32283997535705566 s\n",
-      "0.34000563621520996 s\n",
-      "0.31502628326416016 s\n",
-      "0.34996771812438965 s\n",
-      "0.3871273994445801 s\n",
-      "0.3487060070037842 s\n",
-      "0.35172486305236816 s\n",
-      "0.3280646800994873 s\n",
-      "0.3519773483276367 s\n",
-      "0.3354451656341553 s\n",
-      "0.3633551597595215 s\n",
-      "0.3226644992828369 s\n",
-      "0.33882975578308105 s\n",
-      "0.36072254180908203 s\n",
-      "0.3833494186401367 s\n",
-      "0.2929043769836426 s\n",
-      "0.32875680923461914 s\n",
-      "0.36334872245788574 s\n",
-      "0.34148168563842773 s\n",
-      "0.3569769859313965 s\n",
-      "0.37468576431274414 s\n",
-      "0.399524450302124 s\n",
-      "0.3516504764556885 s\n",
-      "0.333402156829834 s\n",
-      "0.3851203918457031 s\n",
-      "0.34867238998413086 s\n",
-      "0.3607771396636963 s\n",
-      "0.38669753074645996 s\n",
-      "0.33347272872924805 s\n",
-      "0.36278390884399414 s\n",
-      "0.3602781295776367 s\n",
-      "0.3322322368621826 s\n",
-      "0.36807823181152344 s\n",
-      "0.3407411575317383 s\n",
-      "0.3837134838104248 s\n",
-      "0.38958096504211426 s\n",
-      "0.3332521915435791 s\n",
-      "0.3331124782562256 s\n",
-      "0.35001134872436523 s\n",
-      "0.32433485984802246 s\n",
-      "0.36315059661865234 s\n",
-      "0.34323906898498535 s\n",
-      "0.3112339973449707 s\n",
-      "0.30588483810424805 s\n",
-      "0.30704236030578613 s\n",
-      "0.31201720237731934 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "acquired-manitoba",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.32213783264160156 s\n",
-      "0.357776403427124 s\n",
-      "0.37949395179748535 s\n",
-      "0.35210466384887695 s\n",
-      "0.28103041648864746 s\n",
-      "0.3626406192779541 s\n",
-      "0.35109710693359375 s\n",
-      "0.34203338623046875 s\n",
-      "0.32386112213134766 s\n",
-      "0.3354361057281494 s\n",
-      "0.3063056468963623 s\n",
-      "0.3441202640533447 s\n",
-      "0.32869935035705566 s\n",
-      "0.42442989349365234 s\n",
-      "0.37239527702331543 s\n",
-      "0.38650059700012207 s\n",
-      "0.3191685676574707 s\n",
-      "0.3609733581542969 s\n",
-      "0.3115823268890381 s\n",
-      "0.36015963554382324 s\n",
-      "0.3338603973388672 s\n",
-      "0.3487727642059326 s\n",
-      "0.3250617980957031 s\n",
-      "0.35145044326782227 s\n",
-      "0.33944034576416016 s\n",
-      "0.31502413749694824 s\n",
-      "0.3611795902252197 s\n",
-      "0.35285043716430664 s\n",
-      "0.3575010299682617 s\n",
-      "0.304781436920166 s\n",
-      "0.4003562927246094 s\n",
-      "0.3315858840942383 s\n",
-      "0.36008763313293457 s\n",
-      "0.36187100410461426 s\n",
-      "0.32981252670288086 s\n",
-      "0.3378865718841553 s\n",
-      "0.31662964820861816 s\n",
-      "0.32143092155456543 s\n",
-      "0.3152732849121094 s\n",
-      "0.38222813606262207 s\n",
-      "0.3846759796142578 s\n",
-      "0.33153700828552246 s\n",
-      "0.37013936042785645 s\n",
-      "0.33272790908813477 s\n",
-      "0.29526567459106445 s\n",
-      "0.3218040466308594 s\n",
-      "0.3795340061187744 s\n",
-      "0.3576061725616455 s\n",
-      "0.35764193534851074 s\n",
-      "0.36867713928222656 s\n",
-      "0.3807237148284912 s\n",
-      "0.33266758918762207 s\n",
-      "0.33878159523010254 s\n",
-      "0.38289546966552734 s\n",
-      "0.38695788383483887 s\n",
-      "0.33074188232421875 s\n",
-      "0.32749414443969727 s\n",
-      "0.33860039710998535 s\n",
-      "0.36585235595703125 s\n",
-      "0.33011841773986816 s\n",
-      "0.3293156623840332 s\n",
-      "0.3491702079772949 s\n",
-      "0.3720529079437256 s\n",
-      "0.3078622817993164 s\n",
-      "0.3844125270843506 s\n",
-      "0.32468104362487793 s\n",
-      "0.3186354637145996 s\n",
-      "0.3438723087310791 s\n",
-      "0.36643028259277344 s\n",
-      "0.34279680252075195 s\n",
-      "0.3625810146331787 s\n",
-      "0.35865354537963867 s\n",
-      "0.3503103256225586 s\n",
-      "0.37160682678222656 s\n",
-      "0.3268110752105713 s\n",
-      "0.2564544677734375 s\n",
-      "0.37343525886535645 s\n",
-      "0.33298277854919434 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract_first_sent']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "veterinary-thailand",
-   "metadata": {},
-   "source": [
-    "## Updated coverage details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "intimate-campus",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by new text_7_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new text_2_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new complex embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new transe embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new abstract embeddings created for 19k retrofitting: 339\n",
-      "Pair Coverage by new abstract_first_sent embeddings created for 19k retrofitting: 339\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by new {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "lovely-token",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in masterEmbedDictMaster.keys():\n",
-    "    for key2 in masterEmbedDictMaster[key1].keys():\n",
-    "        if type(masterEmbedDictMaster[key1][key2]) != list:\n",
-    "            masterEmbedDictMaster[key1][key2] = masterEmbedDictMaster[key1][key2].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "exact-surfing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in ['complex', 'transe']:\n",
-    "    json.dump(masterEmbedDictMaster[key1], open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "behavioral-spain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt\n",
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "hawaiian-brain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>total count</th>\n",
-       "      <th>overlap count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             embedding  total count  overlap count  Coverage Percentage\n",
-       "0         text_7_props       238930         238889           100.000000\n",
-       "1         text_2_props       238930         238889           100.000000\n",
-       "2              complex       238500         238448            99.815395\n",
-       "3               transe       238500         238448            99.815395\n",
-       "4             abstract       105964         105916            44.336910\n",
-       "5  abstract_first_sent       105964         105916            44.336910"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summArr = []\n",
-    "for key1 in masterEmbedDictMaster:\n",
-    "    cnt = countOverlap(masterEmbedDictMaster[key1], p279QnodesList)\n",
-    "    summArr.append([key1, len(masterEmbedDictMaster[key1]), cnt, cnt / len(p279QnodesList) * 100])\n",
-    "pd.DataFrame(summArr, columns=['embedding', 'total count', 'overlap count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-namibia",
-   "metadata": {},
-   "source": [
-    "# Concatenated Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fifth-associate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "embedDictMaster = {}\n",
-    "for key1 in ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']:\n",
-    "    embedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json'))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "egyptian-sentence",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props :  1024\n",
-      "text_2_props :  1024\n",
-      "complex :  200\n",
-      "transe :  200\n",
-      "abstract :  768\n",
-      "abstract_first_sent :  768\n"
-     ]
-    }
-   ],
-   "source": [
-    "def determineEmbeddingLengths(embedDictMaster):\n",
-    "    for key in embedDictMaster.keys():\n",
-    "        embed_size = len(next(iter(embedDictMaster[key].values())))\n",
-    "        print(key,\": \",embed_size)\n",
-    "determineEmbeddingLengths(embedDictMaster)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "removable-point",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Added 11 corrections\n",
-      "Added 11 corrections\n",
-      "Added 0 corrections\n",
-      "Added 0 corrections\n",
-      "Added 4 corrections\n",
-      "Added 4 corrections\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = deserializeEmbeddingDict(embedDictMaster[key1])\n",
-    "# Fill Coverage of embedding dictionaries\n",
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = fillCoverage(embedDictMaster[key1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "productive-indiana",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props 238941\n",
-      "text_2_props 238941\n",
-      "complex 238941\n",
-      "transe 238941\n",
-      "abstract 238941\n",
-      "abstract_first_sent 238941\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    print(key1, len(next(iter(embedDictMaster.values()))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mechanical-retro",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ready-financing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prime-hometown",
-   "metadata": {},
-   "source": [
-    "# Retrofitting sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "tight-civilization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "exciting-circle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase, weightAssignment=False):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            if weightAssignment:\n",
-    "                sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            else:\n",
-    "                sumOfSims = sum([1 for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "                \n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "hollywood-prisoner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    responseDict = {}\n",
-    "    responseDict['wordSimMissingSet'] = wordSimMissingSet\n",
-    "    responseDict['coveredPairs'] = len(wordSim353AnnotDF_New2)\n",
-    "    responseDict['totalPairs'] = len(wordSim353AnnotDF_New)\n",
-    "    \n",
-    "#     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    \n",
-    "    # Logic 1: Scale min,max value to 1,4 strictly\n",
-    "#     min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "#     min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "#     wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    \n",
-    "    # Logic 2: Scale abs value to 1,4 strictly\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * abs(p))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "    \n",
-    "#     print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "#     print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "#     print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    responseDict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['old_acc'] = accuracy_score(wordSim353AnnotDF_New['textOld'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    responseDict['new_acc'] = accuracy_score(wordSim353AnnotDF_New['textNew'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    \n",
-    "    responseDict['class_rep_old'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), output_dict=True)\n",
-    "    responseDict['class_rep_new'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), output_dict=True)\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    responseDict['cm_old'] = cm_old\n",
-    "    responseDict['cm_new'] = cm_new\n",
-    "    \n",
-    "    return responseDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "severe-explosion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDictMaster, embedDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "decreased-syndication",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "39218\n"
-     ]
-    }
-   ],
-   "source": [
-    "neighDictMaster['19k_childPar'] = fetchNeighbours(p279ChildPar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "rocky-criterion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['complex'] = complexEmb\n",
-    "embedDictMaster['transe'] = transeEmb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "apparent-sapphire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for key2 in embedDictMaster[key1].keys():\n",
-    "        embedDictMaster[key1][key2] = np.array(embedDictMaster[key1][key2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "precise-oxygen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embList = list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "identical-keyboard",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basisList = list(neighDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "aging-flavor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['19k_childPar'])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neighDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "amended-remove",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster, responsesDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "surgical-insurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7817a00dcf3c412b92a7c5ac75517168",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "results = []\n",
-    "NUM_ITERS = 10\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            groupResults = []\n",
-    "            for weightCase in [1,2]:\n",
-    "                embedDict = embedDictMaster[emb]\n",
-    "                if weightedNess:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                else:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                for iterNum in range(1,NUM_ITERS+1):\n",
-    "                    newEmbedDict = retrofit(embedDict, neighDictMaster[basis], weightCase, weightedNess)\n",
-    "        #             dists = determineDistances(embedDict, newEmbedDict)\n",
-    "                    responsesDict = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        #                 print(responsesDict.keys())\n",
-    "                    groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                    responsesDict['old_acc']*100, \\\n",
-    "                                    responsesDict['new_acc']*100, \\\n",
-    "                                    (responsesDict['new_acc'] - responsesDict['old_acc'])*100, \\\n",
-    "                                    responsesDict['coveredPairs'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                    ])\n",
-    "                    embedDict = newEmbedDict\n",
-    "\n",
-    "                newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                responsesDictMaster[caseName] = responsesDict\n",
-    "            for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "                results.append(gR+[rank])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "assigned-stations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'Rank'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "built-mumbai",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Embedding</th>\n",
-       "      <th>Basis</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Weightedness</th>\n",
-       "      <th>Iteration Num</th>\n",
-       "      <th>Old Acc</th>\n",
-       "      <th>New Acc</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>Pairs Covered</th>\n",
-       "      <th>Old I Precision</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Old U Precision</th>\n",
-       "      <th>Old U Recall</th>\n",
-       "      <th>Old U F1-Score</th>\n",
-       "      <th>New I Precision</th>\n",
-       "      <th>New I Recall</th>\n",
-       "      <th>New I F1-Score</th>\n",
-       "      <th>New U Precision</th>\n",
-       "      <th>New U Recall</th>\n",
-       "      <th>New U F1-Score</th>\n",
-       "      <th>Rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>3.488372</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>3.197674</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>2.906977</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>1.744186</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.491803</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.535714</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.116667</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.517241</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.545455</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-1.453488</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>0.377778</td>\n",
-       "      <td>0.165049</td>\n",
-       "      <td>0.229730</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-2.034884</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>40 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Embedding         Basis  Weight  Weightedness  Iteration Num    Old Acc  \\\n",
-       "0    complex  19k_childPar       1          True              1  60.755814   \n",
-       "20    transe  19k_childPar       1          True              1  62.500000   \n",
-       "1    complex  19k_childPar       1          True              2  64.244186   \n",
-       "11   complex  19k_childPar       2          True              2  61.918605   \n",
-       "10   complex  19k_childPar       2          True              1  60.755814   \n",
-       "2    complex  19k_childPar       1          True              3  67.151163   \n",
-       "4    complex  19k_childPar       1          True              5  67.151163   \n",
-       "36    transe  19k_childPar       2          True              7  62.500000   \n",
-       "30    transe  19k_childPar       2          True              1  62.500000   \n",
-       "22    transe  19k_childPar       1          True              3  64.825581   \n",
-       "38    transe  19k_childPar       2          True              9  63.081395   \n",
-       "33    transe  19k_childPar       2          True              4  62.790698   \n",
-       "18   complex  19k_childPar       2          True              9  63.081395   \n",
-       "37    transe  19k_childPar       2          True              8  63.081395   \n",
-       "32    transe  19k_childPar       2          True              3  62.790698   \n",
-       "23    transe  19k_childPar       1          True              4  65.406977   \n",
-       "39    transe  19k_childPar       2          True             10  63.372093   \n",
-       "15   complex  19k_childPar       2          True              6  63.662791   \n",
-       "14   complex  19k_childPar       2          True              5  63.662791   \n",
-       "13   complex  19k_childPar       2          True              4  63.662791   \n",
-       "12   complex  19k_childPar       2          True              3  63.662791   \n",
-       "17   complex  19k_childPar       2          True              8  63.372093   \n",
-       "16   complex  19k_childPar       2          True              7  63.662791   \n",
-       "35    transe  19k_childPar       2          True              6  62.790698   \n",
-       "34    transe  19k_childPar       2          True              5  63.081395   \n",
-       "31    transe  19k_childPar       2          True              2  63.081395   \n",
-       "28    transe  19k_childPar       1          True              9  60.755814   \n",
-       "19   complex  19k_childPar       2          True             10  63.081395   \n",
-       "26    transe  19k_childPar       1          True              7  62.209302   \n",
-       "29    transe  19k_childPar       1          True             10  60.465116   \n",
-       "8    complex  19k_childPar       1          True              9  65.116279   \n",
-       "3    complex  19k_childPar       1          True              4  67.732558   \n",
-       "6    complex  19k_childPar       1          True              7  66.569767   \n",
-       "7    complex  19k_childPar       1          True              8  65.988372   \n",
-       "21    transe  19k_childPar       1          True              2  65.697674   \n",
-       "27    transe  19k_childPar       1          True              8  61.627907   \n",
-       "25    transe  19k_childPar       1          True              6  63.372093   \n",
-       "5    complex  19k_childPar       1          True              6  67.732558   \n",
-       "9    complex  19k_childPar       1          True             10  64.534884   \n",
-       "24    transe  19k_childPar       1          True              5  65.406977   \n",
-       "\n",
-       "      New Acc  Increase  Pairs Covered  Old I Precision  ...  Old U Precision  \\\n",
-       "0   64.244186  3.488372            291         1.000000  ...         0.433121   \n",
-       "20  65.697674  3.197674            291         0.888889  ...         0.397059   \n",
-       "1   67.151163  2.906977            291         1.000000  ...         0.463415   \n",
-       "11  63.662791  1.744186            291         1.000000  ...         0.444444   \n",
-       "10  61.918605  1.162791            291         1.000000  ...         0.433121   \n",
-       "2   67.732558  0.581395            291         1.000000  ...         0.495050   \n",
-       "4   67.732558  0.581395            291         0.916667  ...         0.492958   \n",
-       "36  63.081395  0.581395            291         0.846154  ...         0.351852   \n",
-       "30  63.081395  0.581395            291         0.888889  ...         0.397059   \n",
-       "22  65.406977  0.581395            291         0.750000  ...         0.393939   \n",
-       "38  63.372093  0.290698            291         0.785714  ...         0.372549   \n",
-       "33  63.081395  0.290698            291         0.900000  ...         0.383333   \n",
-       "18  63.081395  0.000000            291         0.909091  ...         0.444444   \n",
-       "37  63.081395  0.000000            291         0.846154  ...         0.365385   \n",
-       "32  62.790698  0.000000            291         0.900000  ...         0.387097   \n",
-       "23  65.406977  0.000000            291         0.750000  ...         0.400000   \n",
-       "39  63.372093  0.000000            291         0.785714  ...         0.380000   \n",
-       "15  63.662791  0.000000            291         1.000000  ...         0.455224   \n",
-       "14  63.662791  0.000000            291         1.000000  ...         0.457143   \n",
-       "13  63.662791  0.000000            291         1.000000  ...         0.457746   \n",
-       "12  63.662791  0.000000            291         1.000000  ...         0.458904   \n",
-       "17  63.081395 -0.290698            291         0.909091  ...         0.448819   \n",
-       "16  63.372093 -0.290698            291         1.000000  ...         0.453846   \n",
-       "35  62.500000 -0.290698            291         0.846154  ...         0.363636   \n",
-       "34  62.790698 -0.290698            291         0.846154  ...         0.375000   \n",
-       "31  62.790698 -0.290698            291         0.900000  ...         0.400000   \n",
-       "28  60.465116 -0.290698            291         0.394737  ...         0.368421   \n",
-       "19  62.790698 -0.290698            291         0.909091  ...         0.444444   \n",
-       "26  61.627907 -0.581395            291         0.428571  ...         0.428571   \n",
-       "29  59.883721 -0.581395            291         0.365854  ...         0.388889   \n",
-       "8   64.534884 -0.581395            291         0.785714  ...         0.442308   \n",
-       "3   67.151163 -0.581395            291         0.909091  ...         0.511905   \n",
-       "6   65.988372 -0.581395            291         0.916667  ...         0.475410   \n",
-       "7   65.116279 -0.872093            291         0.846154  ...         0.464286   \n",
-       "21  64.825581 -0.872093            291         0.833333  ...         0.450000   \n",
-       "27  60.755814 -0.872093            291         0.416667  ...         0.400000   \n",
-       "25  62.209302 -1.162791            291         0.468750  ...         0.454545   \n",
-       "5   66.569767 -1.162791            291         0.916667  ...         0.507937   \n",
-       "9   63.081395 -1.453488            291         0.785714  ...         0.416667   \n",
-       "24  63.372093 -2.034884            291         0.652174  ...         0.444444   \n",
-       "\n",
-       "    Old U Recall  Old U F1-Score  New I Precision  New I Recall  \\\n",
-       "0       0.660194        0.523077         1.000000          0.40   \n",
-       "20      0.262136        0.315789         0.833333          0.50   \n",
-       "1       0.553398        0.504425         1.000000          0.50   \n",
-       "11      0.660194        0.531250         1.000000          0.45   \n",
-       "10      0.660194        0.523077         1.000000          0.40   \n",
-       "2       0.485437        0.490196         0.909091          0.50   \n",
-       "4       0.339806        0.402299         0.916667          0.55   \n",
-       "36      0.184466        0.242038         0.846154          0.55   \n",
-       "30      0.262136        0.315789         0.900000          0.45   \n",
-       "22      0.126214        0.191176         0.750000          0.75   \n",
-       "38      0.184466        0.246753         0.785714          0.55   \n",
-       "33      0.223301        0.282209         0.846154          0.55   \n",
-       "18      0.543689        0.489083         0.909091          0.50   \n",
-       "37      0.184466        0.245161         0.785714          0.55   \n",
-       "32      0.233010        0.290909         0.900000          0.45   \n",
-       "23      0.116505        0.180451         0.652174          0.75   \n",
-       "39      0.184466        0.248366         0.785714          0.55   \n",
-       "15      0.592233        0.514768         1.000000          0.45   \n",
-       "14      0.621359        0.526749         1.000000          0.45   \n",
-       "13      0.631068        0.530612         1.000000          0.45   \n",
-       "12      0.650485        0.538153         1.000000          0.45   \n",
-       "17      0.553398        0.495652         0.909091          0.50   \n",
-       "16      0.572816        0.506438         0.909091          0.50   \n",
-       "35      0.194175        0.253165         0.846154          0.55   \n",
-       "34      0.203883        0.264151         0.846154          0.55   \n",
-       "31      0.252427        0.309524         0.900000          0.45   \n",
-       "28      0.067961        0.114754         0.365854          0.75   \n",
-       "19      0.543689        0.489083         0.900000          0.45   \n",
-       "26      0.087379        0.145161         0.416667          0.75   \n",
-       "29      0.067961        0.115702         0.333333          0.75   \n",
-       "8       0.223301        0.296774         0.785714          0.55   \n",
-       "3       0.417476        0.459893         0.916667          0.55   \n",
-       "6       0.281553        0.353659         0.846154          0.55   \n",
-       "7       0.252427        0.327044         0.785714          0.55   \n",
-       "21      0.174757        0.251748         0.750000          0.60   \n",
-       "27      0.077670        0.130081         0.394737          0.75   \n",
-       "25      0.097087        0.160000         0.428571          0.75   \n",
-       "5       0.310680        0.385542         0.916667          0.55   \n",
-       "9       0.194175        0.264901         0.687500          0.55   \n",
-       "24      0.116505        0.184615         0.468750          0.75   \n",
-       "\n",
-       "    New I F1-Score  New U Precision  New U Recall  New U F1-Score  Rank  \n",
-       "0         0.571429         0.463415      0.553398        0.504425     2  \n",
-       "20        0.625000         0.450000      0.174757        0.251748     0  \n",
-       "1         0.666667         0.495050      0.485437        0.490196     4  \n",
-       "11        0.620690         0.458904      0.650485        0.538153    13  \n",
-       "10        0.571429         0.444444      0.660194        0.531250    12  \n",
-       "2         0.645161         0.511905      0.417476        0.459893     1  \n",
-       "4         0.687500         0.507937      0.310680        0.385542     5  \n",
-       "36        0.666667         0.365385      0.184466        0.245161     6  \n",
-       "30        0.600000         0.400000      0.252427        0.309524    17  \n",
-       "22        0.750000         0.400000      0.116505        0.180451     3  \n",
-       "38        0.647059         0.380000      0.184466        0.248366     8  \n",
-       "33        0.666667         0.375000      0.203883        0.264151    14  \n",
-       "18        0.645161         0.444444      0.543689        0.489083    19  \n",
-       "37        0.647059         0.372549      0.184466        0.246753     7  \n",
-       "32        0.600000         0.383333      0.223301        0.282209    12  \n",
-       "23        0.697674         0.444444      0.116505        0.184615     1  \n",
-       "39        0.647059         0.380000      0.184466        0.248366     9  \n",
-       "15        0.620690         0.453846      0.572816        0.506438    17  \n",
-       "14        0.620690         0.455224      0.592233        0.514768    16  \n",
-       "13        0.620690         0.457143      0.621359        0.526749    15  \n",
-       "12        0.620690         0.457746      0.631068        0.530612    14  \n",
-       "17        0.645161         0.444444      0.543689        0.489083    18  \n",
-       "16        0.645161         0.448819      0.553398        0.495652     9  \n",
-       "35        0.666667         0.351852      0.184466        0.242038     5  \n",
-       "34        0.666667         0.363636      0.194175        0.253165    15  \n",
-       "31        0.600000         0.387097      0.233010        0.290909    11  \n",
-       "28        0.491803         0.388889      0.067961        0.115702    13  \n",
-       "19        0.600000         0.444444      0.543689        0.489083    10  \n",
-       "26        0.535714         0.400000      0.077670        0.130081     4  \n",
-       "29        0.461538         0.411765      0.067961        0.116667    16  \n",
-       "8         0.647059         0.416667      0.194175        0.264901     0  \n",
-       "3         0.687500         0.492958      0.339806        0.402299     3  \n",
-       "6         0.666667         0.464286      0.252427        0.327044     7  \n",
-       "7         0.647059         0.442308      0.223301        0.296774     8  \n",
-       "21        0.666667         0.393939      0.126214        0.191176     2  \n",
-       "27        0.517241         0.368421      0.067961        0.114754    10  \n",
-       "25        0.545455         0.428571      0.087379        0.145161    19  \n",
-       "5         0.687500         0.475410      0.281553        0.353659     6  \n",
-       "9         0.611111         0.377778      0.165049        0.229730    11  \n",
-       "24        0.576923         0.454545      0.097087        0.160000    18  \n",
-       "\n",
-       "[40 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resultsDF.sort_values(by=['Increase'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "utility-globe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.to_csv('../data/retrofitting/masterRetro_Aug20_2021.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "crazy-scene",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv2",
-   "language": "python",
-   "name": "kgtkenv2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "288px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Master P279-Dataset Generation-checkpoint.ipynb b/.ipynb_checkpoints/Master P279-Dataset Generation-checkpoint.ipynb
deleted file mode 100644
index 585ab34..0000000
--- a/.ipynb_checkpoints/Master P279-Dataset Generation-checkpoint.ipynb	
+++ /dev/null
@@ -1,5117 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "individual-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import scipy.stats as stats\n",
-    "import json\n",
-    "from tqdm.notebook import tqdm\n",
-    "import gzip\n",
-    "from collections import defaultdict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "necessary-shopper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Filepaths\n",
-    "\n",
-    "# DWD Files\n",
-    "DERIVED_IS_COUNTS_FILE = '../source_dataset_files/wikidata-20210215-dwd-v2/dwd_isa_class_count.compact.tsv.gz'\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/descriptions.en.tsv.gz\"\n",
-    "P279_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/derived.P279.tsv.gz\"\n",
-    "P279STAR_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/derived.P279star.tsv.gz\"\n",
-    "CLASS_COUNTS_FILE = \"../data/supplementary_files/class-counts.tsv\"\n",
-    "CLASS_TRANSITIVE_P279_COUNTS_FILE = \"../data/supplementary_files/class-counts-P279star.tsv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "chubby-vehicle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_CHILD_PAR_INTERM_FILE = \"../data/basis/intermediate_files/P279_ChildPar.labDesc.csv\"\n",
-    "P279_CHILD_PAR_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_ChildPar.all-distilroberta-v1.csv\"\n",
-    "P279_CHILD_PAR_CLASSSIM_FILE = \"../data/basis/P279_ChildPar.classSim.csv\"\n",
-    "\n",
-    "P279_SIBLINGS_INTERM_FILE = \"../data/basis/intermediate_files/P279_Siblings.all_combinations.csv\"\n",
-    "P279_SIBLINGS_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_Siblings.all-distilroberta-v1.csv\"\n",
-    "P279_SIBLINGS_CLASSSIM_FILE = \"../data/basis/P279_Siblings.classSim.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "greek-young",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_DF = '../data/evaluation/wordsim353_with_r3.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "short-telling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PROBASE_SOURCE_DIR = \"../source_dataset_files/probase/\"\n",
-    "PROBASE_INTERM_FILE = \"../data/basis/intermediate_files/probase_WQnodes.csv\"\n",
-    "PROBASE_FINAL_FILE = '../data/basis/intermediate_files/probase_WQnodes_subset_and_sim.csv'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "secure-gibson",
-   "metadata": {},
-   "source": [
-    "# Utilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "allied-harmony",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71f276d6b5b642609801caf1fa94bb41",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/41845781 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def fetch_labels_to_qnodes_dict():\n",
-    "    labels_to_qnodes_dict = defaultdict(str)\n",
-    "    with gzip.open(LABELS_FILE) as lab_f:\n",
-    "        first_line = True\n",
-    "        for line in tqdm(lab_f, total=41845781):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split()\n",
-    "            str1 = line[3][1:-4]\n",
-    "            qnode = line[1]\n",
-    "            if str1 in labels_to_qnodes_dict:\n",
-    "                if qnode < labels_to_qnodes_dict[str1]:\n",
-    "                    labels_to_qnodes_dict[str1] = qnode\n",
-    "            else:\n",
-    "                labels_to_qnodes_dict[str1] = qnode\n",
-    "    return labels_to_qnodes_dict\n",
-    "labels_to_qnodes_dict = fetch_labels_to_qnodes_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "surgical-matrix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_worsim_labels_to_qnodes_dict():\n",
-    "    wordsim_DF = pd.read_csv(WORDSIM_DF)\n",
-    "    labels_to_qnodes_dict = {row['Word 1']: row['word1_kg_id'] for _, row in wordsim_DF.iterrows()}\n",
-    "    temp2 = {row['Word 2']: row['word2_kg_id'] for _, row in wordsim_DF.iterrows()}\n",
-    "    labels_to_qnodes_dict.update(temp2)\n",
-    "    return labels_to_qnodes_dict\n",
-    "\n",
-    "wordsim_labels_to_qnodes_dict = fetch_worsim_labels_to_qnodes_dict()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removable-number",
-   "metadata": {},
-   "source": [
-    "# Class Counts File Construction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "genuine-memorabilia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Took almost 1.5 hours to execute\n",
-    "# !zcat ../source_dataset_files/wikidata-20210215-dwd-v2/dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > ../data/supplementary_files/class-counts.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "pressed-influence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = defaultdict(int)\n",
-    "with gzip.open(P279STAR_EDGES_FILE, 'r') as f:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(f):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        class_counts[line[2]] += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "centered-school",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts_df = pd.DataFrame.from_dict(class_counts, orient='index',columns=['class_count'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "catholic-criminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts_df.reset_index().rename(columns={'index': '0'}).to_csv(CLASS_TRANSITIVE_P279_COUNTS_FILE, sep='\\t', header=None, index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lined-explosion",
-   "metadata": {},
-   "source": [
-    "# P279 Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "threatened-pacific",
-   "metadata": {},
-   "source": [
-    "## P279 ChildPar Dataset\n",
-    "\n",
-    "Use descriptions wherever available, if not use labels, if not skip rows\n",
-    "\n",
-    "**Old Version Details:**\n",
-    "\n",
-    "[Wikidata OS File (Wikidata 2021-02-15 DWD version)](https://drive.google.com/drive/folders/168j3OfdVGXMTKcs6VyH8rq_p0n6w0GGj?usp=sharing)\n",
-    "\n",
-    "There are 721983 rows in P279 dataset, 606996 unique number of nodes in this dataset, 511841 labels and 299844 descriptions\n",
-    "\n",
-    "There are 373463 rows in P279 dataset s.t. both nodes have either a label or a description.     There are 373463 rows where both nodes have labels.     There are 373463 rows where both nodes have descriptions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "polyphonic-county",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz -p \";P279;\" -o ../data/P279_dataset/wikidata-P279.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "silent-liver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv(P279_EDGES_FILE,sep='\\t')\n",
-    "p279DFNodesSet = set(p279DF.node1.to_list() + p279DF.node2.to_list())\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in p279DFNodesSet)]\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in p279DFNodesSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cubic-taxation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3077831 rows in P279 dataset, 2503943 unique number of nodes in this dataset, 2406580 labels and 1369514 descriptions\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(p279DF)} rows in P279 dataset, {len(p279DFNodesSet)} unique number of nodes in this dataset, {len(labelsDF)} labels and {len(descriptionsDF)} descriptions\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "damaged-driving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DFNew = p279DF\n",
-    "temp1 = p279DFNew.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')\n",
-    "temp1 = temp1[['id','label','node2','node2_label']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]\n",
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()\n",
-    "\n",
-    "p279DFNew = hierDF\n",
-    "temp1 = p279DFNew.set_index('node1').join(descriptionsDF.set_index('node1'), rsuffix='_desc')\n",
-    "temp1 = temp1[['id','label','node2','node1_label','node2_label', 'node2_desc']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(descriptionsDF.set_index('node1'), rsuffix='_desc2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node1_label', 'node2_label', 'node2_desc']].rename(columns={'node2_desc': 'node1_desc', 'node2':'node2_desc'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3['node1_desc'] = temp3['node1_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_desc'] = temp3['node2_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "dress-yellow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combineLabDesc(row, nodeNum):\n",
-    "    if type(row['node'+str(nodeNum)+'_desc']) == str and type(row['node'+str(nodeNum)+'_label']) == str :\n",
-    "        return row['node'+str(nodeNum)+'_label'] + ' ' + row['node'+str(nodeNum)+'_desc']\n",
-    "    else:\n",
-    "        return None\n",
-    "hierDF['node1_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(1,))\n",
-    "hierDF['node2_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(2,))\n",
-    "hierDF = hierDF[~hierDF['node1_labDesc'].isna()]\n",
-    "hierDF = hierDF[~hierDF['node2_labDesc'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "adaptive-measure",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 1898589 rows in P279 dataset s.t. both nodes have either a label or a description.     There are 1898589 rows where both nodes have labels.     There are 1898589 rows where both nodes have descriptions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(hierDF)} rows in P279 dataset s.t. both nodes have either a label or a description. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_label.isna()) & (~hierDF.node2_label.isna())])} rows where both nodes have labels. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_desc.isna()) & (~hierDF.node2_desc.isna())])} rows where both nodes have descriptions.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "disturbed-adaptation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     node2                                 id      node1 label  \\\n",
-       "1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "2  Q100026  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279   \n",
-       "3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279   \n",
-       "4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279   \n",
-       "5  Q100026   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279   \n",
-       "\n",
-       "                                          node2_desc              node1_label  \\\n",
-       "1  1944 fighter aircraft family by Oberst R. Lehmann                    YF-16   \n",
-       "2  1944 fighter aircraft family by Oberst R. Lehmann  F-16A/B Fighting Falcon   \n",
-       "3  1944 fighter aircraft family by Oberst R. Lehmann  F-16C/D Fighting Falcon   \n",
-       "4  1944 fighter aircraft family by Oberst R. Lehmann    F-16E/F Desert Falcon   \n",
-       "5  1944 fighter aircraft family by Oberst R. Lehmann               F-16 VISTA   \n",
-       "\n",
-       "  node2_label                                         node1_desc  \\\n",
-       "1        F-16     initial prototype of the F-16 fighter aircraft   \n",
-       "2        F-16        initial series of the F-16 fighter aircraft   \n",
-       "3        F-16      multirole series of the F-16 fighter aircraft   \n",
-       "4        F-16  export strike fighter series of the F-16 fight...   \n",
-       "5        F-16                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "1  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "2  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "3  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "4  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "5                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "5  F-16 1944 fighter aircraft family by Oberst R....  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-jurisdiction",
-   "metadata": {},
-   "source": [
-    "### Filter based on transitive P279 counts\n",
-    "\n",
-    "6769\n",
-    "(310053, 303284)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "broken-intermediate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "trained-parent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279Counts = pd.read_csv(CLASS_COUNTS_FILE, sep='\\t', header=None)\n",
-    "transitive_P279Counts = transitive_P279Counts.set_index(0)[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "great-engine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def extract_pairs_with_top_k_class_counts(transitive_P279Counts, k, hierDF):\n",
-    "    transitive_P279Counts = transitive_P279Counts[transitive_P279Counts >= k]\n",
-    "    transitive_P279CountsNeededSet = set(transitive_P279Counts.index.to_list())\n",
-    "    hierDF_transP279_filtered = hierDF[hierDF.node2.apply(lambda p: p in transitive_P279CountsNeededSet)]\n",
-    "#     print(\"No. of pairs with identical desc:\", (hierDF_transP279_filtered.node1_desc == hierDF_transP279_filtered.node2_desc).sum())\n",
-    "    hierDF_transP279_filtered1 = hierDF_transP279_filtered[hierDF_transP279_filtered.node1_desc != hierDF_transP279_filtered.node2_desc]\n",
-    "#     print(len(hierDF_transP279_filtered), len(hierDF_transP279_filtered1))\n",
-    "    hierDF_transP279_filtered1 = hierDF_transP279_filtered1.groupby(\"node2\").apply(lambda g: g if len(g) <= 500 else g.sample(500))\n",
-    "    return hierDF_transP279_filtered1, k, len(hierDF_transP279_filtered1), hierDF_transP279_filtered1.node2.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "worldwide-designation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 304654, 28511)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1, k, cnt, n_pars = extract_pairs_with_top_k_class_counts(transitive_P279Counts, 10, hierDF)\n",
-    "k, cnt, n_pars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "medical-hierarchy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# tempRes = []\n",
-    "# for k in range(10000,100000,10000):\n",
-    "#     _, k, cnt, n_pars = extract_pairs_with_top_k_class_counts(transitive_P279Counts, k, hierDF)\n",
-    "#     tempRes.append((k, cnt, n_pars))\n",
-    "# pd.DataFrame(tempRes, columns=['Thresh', 'No. of rows', 'No. of unique parents'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "peaceful-protocol",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    28511.000000\n",
-       "mean        10.685490\n",
-       "std         28.508339\n",
-       "min          1.000000\n",
-       "25%          2.000000\n",
-       "50%          4.000000\n",
-       "75%         10.000000\n",
-       "max        500.000000\n",
-       "Name: node2, dtype: float64"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1.node2.value_counts().describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "spiritual-ballot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1.to_csv(P279_CHILD_PAR_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "english-alfred",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv(P279_CHILD_PAR_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "id": "interpreted-institute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>Unnamed: 1</th>\n",
-       "      <th>node2.1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     node2  Unnamed: 1  node2.1                                 id      node1  \\\n",
-       "0  Q100026           1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "1  Q100026           2  Q100026  Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "2  Q100026           3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "3  Q100026           4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "4  Q100026           5  Q100026   Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "\n",
-       "  label                                         node2_desc  \\\n",
-       "0  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "1  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "2  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "3  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "4  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "\n",
-       "               node1_label node2_label  \\\n",
-       "0                    YF-16        F-16   \n",
-       "1  F-16A/B Fighting Falcon        F-16   \n",
-       "2  F-16C/D Fighting Falcon        F-16   \n",
-       "3    F-16E/F Desert Falcon        F-16   \n",
-       "4               F-16 VISTA        F-16   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \n",
-       "0  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....  "
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "committed-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:0')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=500)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "alpine-virus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "funny-asian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = hierDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "stunning-consolidation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_labDesc.isna().sum(), hierDF.node2_labDesc.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "warming-dinner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "149.29957628250122 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "103.25607371330261 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node1_labDesc, modelName)))\n",
-    "hierDF['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node2_labDesc, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "noticed-electric",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>Unnamed: 1</th>\n",
-       "      <th>node2.1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.0003189822, -0.31257084, 0.4071587, 0.4445...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.066473946, -0.17350475, 0.24638157, 0.4571...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.110435985, -0.09184393, 0.24768817, 0.2641...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.40679273, -0.026886985, 0.032887716, 0.641...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.18904628, 0.10738095, 0.3032565, 0.6507382...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index    node2  Unnamed: 1  node2.1                                 id  \\\n",
-       "0      0  Q100026           1  Q100026  Q17372279-P279-Q100026-beba8cd1-0   \n",
-       "1      1  Q100026           2  Q100026  Q17372377-P279-Q100026-fd42bd71-0   \n",
-       "2      2  Q100026           3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0   \n",
-       "3      3  Q100026           4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0   \n",
-       "4      4  Q100026           5  Q100026   Q2029940-P279-Q100026-ceba4380-0   \n",
-       "\n",
-       "       node1 label                                         node2_desc  \\\n",
-       "0  Q17372279  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "1  Q17372377  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "2  Q17372444  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "3  Q17372455  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "4   Q2029940  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "\n",
-       "               node1_label node2_label  \\\n",
-       "0                    YF-16        F-16   \n",
-       "1  F-16A/B Fighting Falcon        F-16   \n",
-       "2  F-16C/D Fighting Falcon        F-16   \n",
-       "3    F-16E/F Desert Falcon        F-16   \n",
-       "4               F-16 VISTA        F-16   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \\\n",
-       "0  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-0.0003189822, -0.31257084, 0.4071587, 0.4445...   \n",
-       "1  [-0.066473946, -0.17350475, 0.24638157, 0.4571...   \n",
-       "2  [-0.110435985, -0.09184393, 0.24768817, 0.2641...   \n",
-       "3  [-0.40679273, -0.026886985, 0.032887716, 0.641...   \n",
-       "4  [-0.18904628, 0.10738095, 0.3032565, 0.6507382...   \n",
-       "\n",
-       "                                           node2_emb  \n",
-       "0  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "1  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "2  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "3  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "4  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  "
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "surrounded-thinking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_emb.isna().sum(), hierDF.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "frozen-console",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF['similarity_value'] = hierDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "optimum-sewing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF[['id', 'node1', 'label', 'node2', 'similarity_value']].to_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "selective-nashville",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    304654.000000\n",
-       "mean          0.499092\n",
-       "std           0.186887\n",
-       "min          -0.130950\n",
-       "25%           0.371610\n",
-       "50%           0.499928\n",
-       "75%           0.626436\n",
-       "max           0.989182\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "interior-treat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba6879c669da4812919bee809747ece9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/747 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Alt scoring\n",
-    "# from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "# crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "# model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "# crossEncSimVals = model.predict(list(zip(hierDF.node1_labDesc.to_list(), hierDF.node2_labDesc.to_list())), show_progress_bar=True, batch_size=500)\n",
-    "# hierDF['similarity_value'] = crossEncSimVals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consolidated-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "secret-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confirmed-canberra",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "controlled-perfume",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "through-penalty",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "broke-tolerance",
-   "metadata": {},
-   "source": [
-    "## P279 Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "going-custom",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  id      node1 label    node2  \\\n",
-       "0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279  Q100026   \n",
-       "1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279  Q100026   \n",
-       "2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279  Q100026   \n",
-       "3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279  Q100026   \n",
-       "4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279  Q100026   \n",
-       "\n",
-       "   similarity_value  \n",
-       "0          0.592530  \n",
-       "1          0.660948  \n",
-       "2          0.634662  \n",
-       "3          0.552555  \n",
-       "4          0.504576  "
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "hierDF_transP279_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "normal-playback",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_left = hierDF_transP279_filtered.set_index('node2')\n",
-    "hierDF_transP279_filtered_right = hierDF_transP279_filtered.copy().set_index('node2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "interpreted-elimination",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.04 s, sys: 684 ms, total: 2.72 s\n",
-      "Wall time: 2.72 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "hierDF_transP279_filtered_sibs = hierDF_transP279_filtered_left.join(hierDF_transP279_filtered_right, rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "id": "consolidated-hampshire",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26426178"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "blessed-microwave",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        id      node1 label  similarity_value  \\\n",
-       "node2                                                                           \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "\n",
-       "                                  id_right node1_right label_right  \\\n",
-       "node2                                                                \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0   Q17372279        P279   \n",
-       "Q100026  Q17372377-P279-Q100026-fd42bd71-0   Q17372377        P279   \n",
-       "Q100026  Q17372444-P279-Q100026-ca0fc4bd-0   Q17372444        P279   \n",
-       "Q100026  Q17372455-P279-Q100026-c2b1bf36-0   Q17372455        P279   \n",
-       "Q100026   Q2029940-P279-Q100026-ceba4380-0    Q2029940        P279   \n",
-       "\n",
-       "         similarity_value_right  \n",
-       "node2                            \n",
-       "Q100026                0.592530  \n",
-       "Q100026                0.660948  \n",
-       "Q100026                0.634662  \n",
-       "Q100026                0.552555  \n",
-       "Q100026                0.504576  "
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "compliant-measure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.drop(columns=['label_right']).reset_index().rename(columns={'node2': 'par_node', 'node1_right': 'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "anticipated-occasion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  par_node                                 id      node1 label  \\\n",
-       "0  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "2  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "3  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "4  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "\n",
-       "   similarity_value                           id_right      node2  \\\n",
-       "0           0.59253  Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "1           0.59253  Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "2           0.59253  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "3           0.59253  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "4           0.59253   Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "\n",
-       "   similarity_value_right  \n",
-       "0                0.592530  \n",
-       "1                0.660948  \n",
-       "2                0.634662  \n",
-       "3                0.552555  \n",
-       "4                0.504576  "
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "steady-jacket",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26426178"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "italian-slovak",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1 != hierDF_transP279_filtered_sibs1.node2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "digital-oxygen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26121338"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "fewer-rebel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.to_csv(P279_SIBLINGS_INTERM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "failing-accent",
-   "metadata": {},
-   "source": [
-    "### Add labels + descs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "controlled-width",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "id": "prompt-grade",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs = pd.read_csv(P279_SIBLINGS_INTERM_FILE)\n",
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.iloc[sample_without_replacement(len(hierDF_transP279_filtered_sibs), 1000000, random_state=13)]\n",
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1.reset_index()\n",
-    "print(len(hierDF_transP279_filtered_sibs1))\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, compression='gzip', sep='\\t')\n",
-    "hierDF_transP279_filtered_set = set(hierDF_transP279_filtered_sibs1.node1.to_list() + hierDF_transP279_filtered_sibs1.node2.to_list() + hierDF_transP279_filtered_sibs1.par_node.to_list())\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "id": "american-porter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_label'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_label'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['par_label'] = hierDF_transP279_filtered_sibs1.par_node.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node1_desc'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_desc'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: descDict[p][1:-4] if p in descDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node1_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node1_label'] + ' ' + p['node1_desc'] + ' is ' + p['par_label'],axis=1)\n",
-    "hierDF_transP279_filtered_sibs1['node2_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node2_label'] + ' ' + p['node2_desc'] + ' is ' + p['par_label'],axis=1)\n",
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1_desc != hierDF_transP279_filtered_sibs1.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "official-separation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset reduced from 1000000 to 785418 by eliminating rows where node1_desc == node2_desc\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset reduced from {len(hierDF_transP279_filtered_sibs1)} to {len(hierDF_transP279_filtered_sibs2)} by eliminating rows where node1_desc == node2_desc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "id": "weighted-mirror",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>24998287</td>\n",
-       "      <td>Q89</td>\n",
-       "      <td>Q96627253-P279-Q89-51483924-0</td>\n",
-       "      <td>Q96627253</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.460166</td>\n",
-       "      <td>Q595003-P279-Q89-ddc59387-0</td>\n",
-       "      <td>Q595003</td>\n",
-       "      <td>0.553182</td>\n",
-       "      <td>Sevillana (manzana)</td>\n",
-       "      <td>Calville Blanc d\\'hiver</td>\n",
-       "      <td>apple</td>\n",
-       "      <td>apple cultivar</td>\n",
-       "      <td>apple cultivar</td>\n",
-       "      <td>Sevillana (manzana) apple cultivar is apple</td>\n",
-       "      <td>Calville Blanc d\\'hiver apple cultivar is apple</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6348351</td>\n",
-       "      <td>Q17589470</td>\n",
-       "      <td>Q56942-P279-Q17589470-d0932570-0</td>\n",
-       "      <td>Q56942</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.527086</td>\n",
-       "      <td>Q69974281-P279-Q17589470-ed5eb339-0</td>\n",
-       "      <td>Q69974281</td>\n",
-       "      <td>0.466616</td>\n",
-       "      <td>Wii U</td>\n",
-       "      <td>Gamatic 7600</td>\n",
-       "      <td>home video game console</td>\n",
-       "      <td>eight-generation home video game console by Ni...</td>\n",
-       "      <td>Pong console. The same of Otron Gamatic 7600</td>\n",
-       "      <td>Wii U eight-generation home video game console...</td>\n",
-       "      <td>Gamatic 7600 Pong console. The same of Otron G...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3244723</td>\n",
-       "      <td>Q13357858</td>\n",
-       "      <td>Q63845311-P279-Q13357858-c40a679c-0</td>\n",
-       "      <td>Q63845311</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.695520</td>\n",
-       "      <td>Q79132633-P279-Q13357858-4ccf52ce-0</td>\n",
-       "      <td>Q79132633</td>\n",
-       "      <td>0.679767</td>\n",
-       "      <td>2013 World Senior Badminton Championships O35</td>\n",
-       "      <td>Slovak Badminton Championships U17</td>\n",
-       "      <td>badminton tournament</td>\n",
-       "      <td>badminton championships</td>\n",
-       "      <td>badminton championships</td>\n",
-       "      <td>2013 World Senior Badminton Championships O35 ...</td>\n",
-       "      <td>Slovak Badminton Championships U17 badminton c...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5923963</td>\n",
-       "      <td>Q174736</td>\n",
-       "      <td>Q583164-P279-Q174736-262a2fe0-0</td>\n",
-       "      <td>Q583164</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.459580</td>\n",
-       "      <td>Q2692256-P279-Q174736-e1333ac9-0</td>\n",
-       "      <td>Q2692256</td>\n",
-       "      <td>0.592024</td>\n",
-       "      <td>Project 30bis</td>\n",
-       "      <td>Bagley-class destroyer</td>\n",
-       "      <td>destroyer</td>\n",
-       "      <td>1949 Soviet destroyer class</td>\n",
-       "      <td>class of U.S. destroyers</td>\n",
-       "      <td>Project 30bis 1949 Soviet destroyer class is d...</td>\n",
-       "      <td>Bagley-class destroyer class of U.S. destroyer...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10867070</td>\n",
-       "      <td>Q215627</td>\n",
-       "      <td>Q19840821-P279-Q215627-eb7ec03a-0</td>\n",
-       "      <td>Q19840821</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.201813</td>\n",
-       "      <td>Q40554819-P279-Q215627-95d720c4-0</td>\n",
-       "      <td>Q40554819</td>\n",
-       "      <td>0.104633</td>\n",
-       "      <td>Singaporeans</td>\n",
-       "      <td>Sicilians</td>\n",
-       "      <td>person</td>\n",
-       "      <td>citizens or residents of Singapore</td>\n",
-       "      <td>ethnic group indigenous to the island of Sicil...</td>\n",
-       "      <td>Singaporeans citizens or residents of Singapor...</td>\n",
-       "      <td>Sicilians ethnic group indigenous to the islan...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999995</th>\n",
-       "      <td>15478058</td>\n",
-       "      <td>Q3382119</td>\n",
-       "      <td>Q75086913-P279-Q3382119-a9ff163a-0</td>\n",
-       "      <td>Q75086913</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.769255</td>\n",
-       "      <td>Q75828417-P279-Q3382119-59b8950c-0</td>\n",
-       "      <td>Q75828417</td>\n",
-       "      <td>0.621310</td>\n",
-       "      <td>Sub-prefect of Nogent-le-Rotrou</td>\n",
-       "      <td>Sub-prefect of Pont-Audemer</td>\n",
-       "      <td>sub-prefect</td>\n",
-       "      <td>French official position</td>\n",
-       "      <td>former French official position (1800-1926)</td>\n",
-       "      <td>Sub-prefect of Nogent-le-Rotrou French officia...</td>\n",
-       "      <td>Sub-prefect of Pont-Audemer former French offi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999996</th>\n",
-       "      <td>23394433</td>\n",
-       "      <td>Q785745</td>\n",
-       "      <td>Q23022056-P279-Q785745-f48ca89b-0</td>\n",
-       "      <td>Q23022056</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.405016</td>\n",
-       "      <td>Q3511974-P279-Q785745-42da354d-0</td>\n",
-       "      <td>Q3511974</td>\n",
-       "      <td>0.306730</td>\n",
-       "      <td>Tasmanian Government Railways J class</td>\n",
-       "      <td>Alsace-Lorraine E 6</td>\n",
-       "      <td>tank locomotive</td>\n",
-       "      <td>class of 1 Australian 2-6-4-0T locomotive</td>\n",
-       "      <td>class of 13 German (later French) metre-gauge ...</td>\n",
-       "      <td>Tasmanian Government Railways J class class of...</td>\n",
-       "      <td>Alsace-Lorraine E 6 class of 13 German (later ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999997</th>\n",
-       "      <td>19035228</td>\n",
-       "      <td>Q483373</td>\n",
-       "      <td>Q682971-P279-Q483373-6ff4adf5-0</td>\n",
-       "      <td>Q682971</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.314112</td>\n",
-       "      <td>Q4598091-P279-Q483373-fd15b986-0</td>\n",
-       "      <td>Q4598091</td>\n",
-       "      <td>0.213652</td>\n",
-       "      <td>Rhaetian Railway ABe 4/16</td>\n",
-       "      <td>CTA 2000 series</td>\n",
-       "      <td>electric multiple unit</td>\n",
-       "      <td>multiple unit</td>\n",
-       "      <td>class of Chicago Transit Authority cars</td>\n",
-       "      <td>Rhaetian Railway ABe 4/16 multiple unit is ele...</td>\n",
-       "      <td>CTA 2000 series class of Chicago Transit Autho...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999998</th>\n",
-       "      <td>20618370</td>\n",
-       "      <td>Q625151</td>\n",
-       "      <td>Q841350-P279-Q625151-bd2c0728-0</td>\n",
-       "      <td>Q841350</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.459560</td>\n",
-       "      <td>Q463577-P279-Q625151-4cfe245b-0</td>\n",
-       "      <td>Q463577</td>\n",
-       "      <td>0.466203</td>\n",
-       "      <td>OSE class 120</td>\n",
-       "      <td>BLS Re 4/4</td>\n",
-       "      <td>electric locomotive</td>\n",
-       "      <td>class of Greek electric locomotives</td>\n",
-       "      <td>class of 35 Swiss electric locomoties</td>\n",
-       "      <td>OSE class 120 class of Greek electric locomoti...</td>\n",
-       "      <td>BLS Re 4/4 class of 35 Swiss electric locomoti...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999999</th>\n",
-       "      <td>2023217</td>\n",
-       "      <td>Q11513337</td>\n",
-       "      <td>Q13382122-P279-Q11513337-2d44cd4f-0</td>\n",
-       "      <td>Q13382122</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.559249</td>\n",
-       "      <td>Q13854733-P279-Q11513337-8c2aca91-0</td>\n",
-       "      <td>Q13854733</td>\n",
-       "      <td>0.626328</td>\n",
-       "      <td>high jumper</td>\n",
-       "      <td>steeplechase runner</td>\n",
-       "      <td>athletics competitor</td>\n",
-       "      <td>sportsperson taking part in high jump competit...</td>\n",
-       "      <td>sportsperson taking part in steeplechasing com...</td>\n",
-       "      <td>high jumper sportsperson taking part in high j...</td>\n",
-       "      <td>steeplechase runner sportsperson taking part i...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000000 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           index   par_node                                   id      node1  \\\n",
-       "0       24998287        Q89        Q96627253-P279-Q89-51483924-0  Q96627253   \n",
-       "1        6348351  Q17589470     Q56942-P279-Q17589470-d0932570-0     Q56942   \n",
-       "2        3244723  Q13357858  Q63845311-P279-Q13357858-c40a679c-0  Q63845311   \n",
-       "3        5923963    Q174736      Q583164-P279-Q174736-262a2fe0-0    Q583164   \n",
-       "4       10867070    Q215627    Q19840821-P279-Q215627-eb7ec03a-0  Q19840821   \n",
-       "...          ...        ...                                  ...        ...   \n",
-       "999995  15478058   Q3382119   Q75086913-P279-Q3382119-a9ff163a-0  Q75086913   \n",
-       "999996  23394433    Q785745    Q23022056-P279-Q785745-f48ca89b-0  Q23022056   \n",
-       "999997  19035228    Q483373      Q682971-P279-Q483373-6ff4adf5-0    Q682971   \n",
-       "999998  20618370    Q625151      Q841350-P279-Q625151-bd2c0728-0    Q841350   \n",
-       "999999   2023217  Q11513337  Q13382122-P279-Q11513337-2d44cd4f-0  Q13382122   \n",
-       "\n",
-       "       label  similarity_value                             id_right  \\\n",
-       "0       P279          0.460166          Q595003-P279-Q89-ddc59387-0   \n",
-       "1       P279          0.527086  Q69974281-P279-Q17589470-ed5eb339-0   \n",
-       "2       P279          0.695520  Q79132633-P279-Q13357858-4ccf52ce-0   \n",
-       "3       P279          0.459580     Q2692256-P279-Q174736-e1333ac9-0   \n",
-       "4       P279          0.201813    Q40554819-P279-Q215627-95d720c4-0   \n",
-       "...      ...               ...                                  ...   \n",
-       "999995  P279          0.769255   Q75828417-P279-Q3382119-59b8950c-0   \n",
-       "999996  P279          0.405016     Q3511974-P279-Q785745-42da354d-0   \n",
-       "999997  P279          0.314112     Q4598091-P279-Q483373-fd15b986-0   \n",
-       "999998  P279          0.459560      Q463577-P279-Q625151-4cfe245b-0   \n",
-       "999999  P279          0.559249  Q13854733-P279-Q11513337-8c2aca91-0   \n",
-       "\n",
-       "            node2  similarity_value_right  \\\n",
-       "0         Q595003                0.553182   \n",
-       "1       Q69974281                0.466616   \n",
-       "2       Q79132633                0.679767   \n",
-       "3        Q2692256                0.592024   \n",
-       "4       Q40554819                0.104633   \n",
-       "...           ...                     ...   \n",
-       "999995  Q75828417                0.621310   \n",
-       "999996   Q3511974                0.306730   \n",
-       "999997   Q4598091                0.213652   \n",
-       "999998    Q463577                0.466203   \n",
-       "999999  Q13854733                0.626328   \n",
-       "\n",
-       "                                          node1_label  \\\n",
-       "0                                 Sevillana (manzana)   \n",
-       "1                                               Wii U   \n",
-       "2       2013 World Senior Badminton Championships O35   \n",
-       "3                                       Project 30bis   \n",
-       "4                                        Singaporeans   \n",
-       "...                                               ...   \n",
-       "999995                Sub-prefect of Nogent-le-Rotrou   \n",
-       "999996          Tasmanian Government Railways J class   \n",
-       "999997                      Rhaetian Railway ABe 4/16   \n",
-       "999998                                  OSE class 120   \n",
-       "999999                                    high jumper   \n",
-       "\n",
-       "                               node2_label                par_label  \\\n",
-       "0                  Calville Blanc d\\'hiver                    apple   \n",
-       "1                             Gamatic 7600  home video game console   \n",
-       "2       Slovak Badminton Championships U17     badminton tournament   \n",
-       "3                   Bagley-class destroyer                destroyer   \n",
-       "4                                Sicilians                   person   \n",
-       "...                                    ...                      ...   \n",
-       "999995         Sub-prefect of Pont-Audemer              sub-prefect   \n",
-       "999996                 Alsace-Lorraine E 6          tank locomotive   \n",
-       "999997                     CTA 2000 series   electric multiple unit   \n",
-       "999998                          BLS Re 4/4      electric locomotive   \n",
-       "999999                 steeplechase runner     athletics competitor   \n",
-       "\n",
-       "                                               node1_desc  \\\n",
-       "0                                          apple cultivar   \n",
-       "1       eight-generation home video game console by Ni...   \n",
-       "2                                 badminton championships   \n",
-       "3                             1949 Soviet destroyer class   \n",
-       "4                      citizens or residents of Singapore   \n",
-       "...                                                   ...   \n",
-       "999995                           French official position   \n",
-       "999996          class of 1 Australian 2-6-4-0T locomotive   \n",
-       "999997                                      multiple unit   \n",
-       "999998                class of Greek electric locomotives   \n",
-       "999999  sportsperson taking part in high jump competit...   \n",
-       "\n",
-       "                                               node2_desc  \\\n",
-       "0                                          apple cultivar   \n",
-       "1            Pong console. The same of Otron Gamatic 7600   \n",
-       "2                                 badminton championships   \n",
-       "3                                class of U.S. destroyers   \n",
-       "4       ethnic group indigenous to the island of Sicil...   \n",
-       "...                                                   ...   \n",
-       "999995        former French official position (1800-1926)   \n",
-       "999996  class of 13 German (later French) metre-gauge ...   \n",
-       "999997            class of Chicago Transit Authority cars   \n",
-       "999998              class of 35 Swiss electric locomoties   \n",
-       "999999  sportsperson taking part in steeplechasing com...   \n",
-       "\n",
-       "                                               node1_sent  \\\n",
-       "0             Sevillana (manzana) apple cultivar is apple   \n",
-       "1       Wii U eight-generation home video game console...   \n",
-       "2       2013 World Senior Badminton Championships O35 ...   \n",
-       "3       Project 30bis 1949 Soviet destroyer class is d...   \n",
-       "4       Singaporeans citizens or residents of Singapor...   \n",
-       "...                                                   ...   \n",
-       "999995  Sub-prefect of Nogent-le-Rotrou French officia...   \n",
-       "999996  Tasmanian Government Railways J class class of...   \n",
-       "999997  Rhaetian Railway ABe 4/16 multiple unit is ele...   \n",
-       "999998  OSE class 120 class of Greek electric locomoti...   \n",
-       "999999  high jumper sportsperson taking part in high j...   \n",
-       "\n",
-       "                                               node2_sent  \n",
-       "0         Calville Blanc d\\'hiver apple cultivar is apple  \n",
-       "1       Gamatic 7600 Pong console. The same of Otron G...  \n",
-       "2       Slovak Badminton Championships U17 badminton c...  \n",
-       "3       Bagley-class destroyer class of U.S. destroyer...  \n",
-       "4       Sicilians ethnic group indigenous to the islan...  \n",
-       "...                                                   ...  \n",
-       "999995  Sub-prefect of Pont-Audemer former French offi...  \n",
-       "999996  Alsace-Lorraine E 6 class of 13 German (later ...  \n",
-       "999997  CTA 2000 series class of Chicago Transit Autho...  \n",
-       "999998  BLS Re 4/4 class of 35 Swiss electric locomoti...  \n",
-       "999999  steeplechase runner sportsperson taking part i...  \n",
-       "\n",
-       "[1000000 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "id": "third-canon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff153c8a9ba8458a8c4edf70cee2648d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/786 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "693.1311120986938 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4beaeda275a64f0b867ab8f25b138435",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/786 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "688.8171112537384 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "count    785418.000000\n",
-       "mean          0.512485\n",
-       "std           0.160710\n",
-       "min          -0.145408\n",
-       "25%           0.399946\n",
-       "50%           0.515756\n",
-       "75%           0.632409\n",
-       "max           0.997993\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:0')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs2.reset_index()\n",
-    "hierDF_transP279_filtered_sibs2['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node1_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs2['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node2_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs2['similarity_value'] = hierDF_transP279_filtered_sibs2.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)\n",
-    "hierDF_transP279_filtered_sibs2['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "id": "invalid-yahoo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2[['id', 'node1', 'label', 'node2', 'par_node', 'similarity_value']].to_csv(P279_SIBLINGS_DISTILBERT_COSSIM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "infrared-success",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a35cdeb3c28e445dbfb84b318876a709",
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fea59ce42344dfaa116390c720ade24",
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4f09ee455fd4edcb3d7cde46fa6b141",
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ed068be329844b696662aaa852d938c",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84eafe207915471b804c37f346d69474",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56aa42aa7cbb4980992bbe5feb3078db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Alt scoring\n",
-    "# from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "# crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "# model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "# crossEncSimVals = model.predict(list(zip(hierDF_transP279_filtered_sibs2.node1_sent.to_list(), hierDF_transP279_filtered_sibs2.node2_sent.to_list())), show_progress_bar=True, batch_size=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "standard-evaluation",
-   "metadata": {},
-   "source": [
-    "# Probase Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "organized-sound",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>size</td>\n",
-       "      <td>33222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>revenue</td>\n",
-       "      <td>33185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>state</td>\n",
-       "      <td>california</td>\n",
-       "      <td>18062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>supplement</td>\n",
-       "      <td>msm glucosamine sulfate</td>\n",
-       "      <td>15942</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               node1_label              node2_label  no_of_relations\n",
-       "0                   factor                      age            35167\n",
-       "1  free rich company datum                     size            33222\n",
-       "2  free rich company datum                  revenue            33185\n",
-       "3                    state               california            18062\n",
-       "4               supplement  msm glucosamine sulfate            15942"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF = pd.read_csv(PROBASE_SOURCE_DIR + 'data-concept/data-concept-instance-relations.txt',header=None, sep='\\t')\n",
-    "probDF.columns=['node1_label', 'node2_label', 'no_of_relations']\n",
-    "probDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "alternative-eagle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF['node1'] = probDF.node1_label.apply(lambda p: labels_to_qnodes_dict[p] if p in labels_to_qnodes_dict else None)\n",
-    "probDF['node2'] = probDF.node2_label.apply(lambda p: labels_to_qnodes_dict[p] if p in labels_to_qnodes_dict else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "spare-terror",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "acc4b8e178af4e5ebb4358c8d92fb7b4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/33377320 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def fix_qnodes_to_wsim(row):\n",
-    "    if row.node1_label in wordsim_labels_to_qnodes_dict:\n",
-    "        row['node1'] = wordsim_labels_to_qnodes_dict[row.node1_label]\n",
-    "    if row.node2_label in wordsim_labels_to_qnodes_dict:\n",
-    "        row['node2'] = wordsim_labels_to_qnodes_dict[row.node2_label]\n",
-    "    return row\n",
-    "tqdm.pandas()\n",
-    "probDF1 = probDF.progress_apply(fix_qnodes_to_wsim, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "attached-briefing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>Q103858669</td>\n",
-       "      <td>Q100343219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>size</td>\n",
-       "      <td>33222</td>\n",
-       "      <td>None</td>\n",
-       "      <td>Q322481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>revenue</td>\n",
-       "      <td>33185</td>\n",
-       "      <td>None</td>\n",
-       "      <td>Q850210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>state</td>\n",
-       "      <td>california</td>\n",
-       "      <td>18062</td>\n",
-       "      <td>P1310</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>supplement</td>\n",
-       "      <td>msm glucosamine sulfate</td>\n",
-       "      <td>15942</td>\n",
-       "      <td>Q2915731</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377315</th>\n",
-       "      <td>popular legacy datum structure</td>\n",
-       "      <td>binary search tree</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377316</th>\n",
-       "      <td>norwegian food</td>\n",
-       "      <td>lefse</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377317</th>\n",
-       "      <td>freeze skill</td>\n",
-       "      <td>new fot</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377318</th>\n",
-       "      <td>enhanced enforcement initiative</td>\n",
-       "      <td>monthly impact inspections of problem mine</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377319</th>\n",
-       "      <td>enhanced enforcement initiative</td>\n",
-       "      <td>pattern of violation notification</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>33377320 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              node1_label  \\\n",
-       "0                                  factor   \n",
-       "1                 free rich company datum   \n",
-       "2                 free rich company datum   \n",
-       "3                                   state   \n",
-       "4                              supplement   \n",
-       "...                                   ...   \n",
-       "33377315   popular legacy datum structure   \n",
-       "33377316                   norwegian food   \n",
-       "33377317                     freeze skill   \n",
-       "33377318  enhanced enforcement initiative   \n",
-       "33377319  enhanced enforcement initiative   \n",
-       "\n",
-       "                                         node2_label  no_of_relations  \\\n",
-       "0                                                age            35167   \n",
-       "1                                               size            33222   \n",
-       "2                                            revenue            33185   \n",
-       "3                                         california            18062   \n",
-       "4                            msm glucosamine sulfate            15942   \n",
-       "...                                              ...              ...   \n",
-       "33377315                          binary search tree                1   \n",
-       "33377316                                       lefse                1   \n",
-       "33377317                                     new fot                1   \n",
-       "33377318  monthly impact inspections of problem mine                1   \n",
-       "33377319           pattern of violation notification                1   \n",
-       "\n",
-       "               node1       node2  \n",
-       "0         Q103858669  Q100343219  \n",
-       "1               None     Q322481  \n",
-       "2               None     Q850210  \n",
-       "3              P1310        None  \n",
-       "4           Q2915731        None  \n",
-       "...              ...         ...  \n",
-       "33377315        None        None  \n",
-       "33377316        None        None  \n",
-       "33377317        None        None  \n",
-       "33377318        None        None  \n",
-       "33377319        None        None  \n",
-       "\n",
-       "[33377320 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "empty-manner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33377320"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(probDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "peripheral-atmosphere",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "955210"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "((~probDF1.node1.isna()) & (~probDF1.node2.isna())).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "intellectual-mitchell",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "factor                         364111\n",
-       "feature                        203549\n",
-       "issue                          201986\n",
-       "product                        172106\n",
-       "item                           158829\n",
-       "                                ...  \n",
-       "non opec supplier                   1\n",
-       "proactive industry group            1\n",
-       "car s important information         1\n",
-       "mesofronts                          1\n",
-       "bucket related offer                1\n",
-       "Name: node1_label, Length: 5376524, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF1.node1_label.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "outstanding-shadow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF1.to_csv(PROBASE_INTERM_FILE,index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "alert-announcement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes = pd.read_csv(PROBASE_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "suspected-freight",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes[(~probDF_Qnodes_DF_WQnodes.node1.isna()) & (~probDF_Qnodes_DF_WQnodes.node2.isna())]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "hazardous-healing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    955210.000000\n",
-       "mean         10.028577\n",
-       "std          85.410631\n",
-       "min           1.000000\n",
-       "25%           1.000000\n",
-       "50%           2.000000\n",
-       "75%           5.000000\n",
-       "max       35167.000000\n",
-       "Name: no_of_relations, dtype: float64"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "upper-guitar",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-36-38e5ab73861c>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  probDF_Qnodes_DF_WQnodes1_subset['similarity_value'] = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].apply(np.log10) / np.log10(maxNR)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "count    955210.000000\n",
-       "mean          0.091717\n",
-       "std           0.112667\n",
-       "min           0.000000\n",
-       "25%           0.000000\n",
-       "50%           0.066217\n",
-       "75%           0.153750\n",
-       "max           1.000000\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "maxNR = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].max()\n",
-    "probDF_Qnodes_DF_WQnodes1_subset['similarity_value'] = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].apply(np.log10) / np.log10(maxNR)\n",
-    "probDF_Qnodes_DF_WQnodes1_subset.similarity_value.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "attached-medication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.to_csv(PROBASE_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "exciting-capitol",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "neither-consumer",
-   "metadata": {},
-   "source": [
-    "# Class Similarity Datasets\n",
-    "\n",
-    "`zcat derived.dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > class-counts.tsv`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "short-heritage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['similarity_value'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['similarity_value'] = None\n",
-    "    return row"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "interested-boxing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childPar_class = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "alternate-trance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e5667a15f08d470ca3907d5200ca13e7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/304654 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-205-cc4159d2906b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mP279childPar_class_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mP279childPar_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetchClassSim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    808\u001b[0m                 \u001b[0;31m# on the df using our wrapper (which provides bar updating)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    811\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7763\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7764\u001b[0m         )\n\u001b[0;32m-> 7765\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    803\u001b[0m                     \u001b[0;31m# take a fast or slow code path; so stop when t.total==t.n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m                 \u001b[0;31m# Apply the provided function (in **kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-201-4df33f71c8eb>\u001b[0m in \u001b[0;36mfetchClassSim\u001b[0;34m(row, similarity_type)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfetchClassSim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimilarity_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://kgtk.isi.edu/similarity_api?q1=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"&q2=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"&embedding_type=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msimilarity_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'similarity_value'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'similarity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    540\u001b[0m         }\n\u001b[1;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sock\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_default_certs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         self.sock = ssl_wrap_socket(\n\u001b[0m\u001b[1;32m    412\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m             \u001b[0mkeyfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/util/ssl_.py\u001b[0m in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mca_certs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mca_cert_dir\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mca_cert_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m             \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_verify_locations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mca_certs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mca_cert_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mca_cert_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "tqdm.pandas()\n",
-    "P279childPar_class_new = P279childPar_class.progress_apply(fetchClassSim, axis=1, args=('class',))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adolescent-nurse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childPar_class_new.to_csv(P279_CHILD_PAR_CLASSSIM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "warming-vertical",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "id": "imported-mounting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0f3c043918e4634a2dc5bdc7f767c7a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/304654 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-202-93d379bb189a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mP279childPar_class_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetchClassSim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'class'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP279childPar_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP279childPar_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1055\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    931\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    432\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "P279childPar_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row, 'class') for _, row in tqdm(P279childPar_class.iterrows(), total=len(P279childPar_class)))\n",
-    "                                                                                                                                                                                                                                                                                  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spread-snapshot",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "supposed-newport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "reliable-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2[['node1', 'node2']].rename(columns={'node1':'q1', 'node2':'q2'}).to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols_FOR_CLASS.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "fifty-garden",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21f6e6799b7d425fa5c2d3d79d6ae2dc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "215bbc5968f94456a3a50e8d182f06d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "class_dict = {}\n",
-    "hierDF_transP279_filtered = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "p279QnodesList = set(hierDF_transP279_filtered.node1.to_list() + hierDF_transP279_filtered.node2.to_list())\n",
-    "with gzip.open(DERIVED_IS_COUNTS_FILE) as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            class_dict[line[0]] = line[2].strip()\n",
-    "all_class_count_dict = {}\n",
-    "with open(CLASS_COUNTS_FILE) as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            all_class_count_dict[line[0]] = line[1].strip()\n",
-    "\n",
-    "\n",
-    "class SemanticSimilarity(object):\n",
-    "    def __init__(self):\n",
-    "#         self.config = config\n",
-    "#         self.embeddings_type = ['class']\n",
-    "        self.N = float(52546967)\n",
-    "        self.all_class_count_dict = all_class_count_dict\n",
-    "        \n",
-    "    def get_qnode_details(self, qnodes: list):\n",
-    "        global class_dict\n",
-    "        qnodes_dict = {}\n",
-    "        qnodes_dict[qnodes[0]] = class_dict.get(qnodes[0],'')\n",
-    "        qnodes_dict[qnodes[1]] = class_dict.get(qnodes[1],'')\n",
-    "        return qnodes_dict\n",
-    "        \n",
-    "    def compute_class_similarity(self, q1, q2):\n",
-    "        qnodes_dict = self.get_qnode_details([q1, q2])\n",
-    "#         print(qnodes_dict)\n",
-    "        feature_dict, feature_count_dict = self.build_qnode_feature_dict(qnodes_dict)\n",
-    "#         print(feature_dict, feature_count_dict)\n",
-    "        normalized_classes_idf = self.normalize_idf_classes(feature_dict, feature_count_dict)\n",
-    "        if q1 in feature_dict and q2 in feature_dict:\n",
-    "            q1_cl = set(feature_dict[q1])\n",
-    "            q2_cl = set(feature_dict[q2])\n",
-    "            q1_q2_intersection = q1_cl.intersection(q2_cl)\n",
-    "\n",
-    "            _similarity = sum([normalized_classes_idf.get(c) for c in q1_q2_intersection])\n",
-    "            return {\n",
-    "                'similarity': _similarity\n",
-    "            }\n",
-    "        return {\n",
-    "            'similarity': None\n",
-    "        }\n",
-    "\n",
-    "    def build_qnode_feature_dict(self, qnodes_dict: dict) -> (dict, dict):\n",
-    "        feature_dict = {}\n",
-    "        feature_count_dict = {}\n",
-    "\n",
-    "        for qnode in qnodes_dict:\n",
-    "            feature_val = []\n",
-    "            \n",
-    "            if '|' in qnodes_dict[qnode]:\n",
-    "                cl = qnodes_dict[qnode].split(\"|\")\n",
-    "\n",
-    "                for c in cl:\n",
-    "                    vals = c.split(\":\")\n",
-    "                    feature_val.append(vals[0])\n",
-    "                    feature_count_dict[vals[0]] = float(vals[1])\n",
-    "            if qnode not in feature_val:\n",
-    "                feature_val.append(qnode)\n",
-    "            if qnode not in feature_count_dict:\n",
-    "                feature_count_dict[qnode] = float(self.all_class_count_dict.get(qnode, 1.0))\n",
-    "            feature_dict[qnode] = feature_val\n",
-    "\n",
-    "        return feature_dict, feature_count_dict\n",
-    "\n",
-    "    def normalize_idf_classes(self, feature_dict, feature_count_dict):\n",
-    "\n",
-    "        classes_count = {}\n",
-    "        for qnode in feature_dict:\n",
-    "\n",
-    "            classes = feature_dict[qnode]\n",
-    "            for c in classes:\n",
-    "                if c not in classes_count:\n",
-    "                    classes_count[c] = 0\n",
-    "                classes_count[c] += 1\n",
-    "\n",
-    "        classes_idf = self.calculate_idf_features(feature_count_dict)\n",
-    "\n",
-    "        # multiply class count with idf\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_count[c] * classes_idf[c]\n",
-    "\n",
-    "        # normalize the idf scores so that they sum to 1\n",
-    "        classes_idf_sum = sum([classes_idf[x] for x in classes_idf])\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_idf[c] / classes_idf_sum\n",
-    "\n",
-    "        return classes_idf\n",
-    "\n",
-    "    def calculate_idf_features(self, feature_count_dict):\n",
-    "        _ = {}\n",
-    "        for c in feature_count_dict:\n",
-    "            _[c] = math.log(self.N / feature_count_dict[c])\n",
-    "        return _"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "streaming-match",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flying-perception",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "# import sys\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "#     try:\n",
-    "#         row['classSim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "#     except Exception as exc:\n",
-    "#         print(exc)\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     row['Resp_code'] = resp\n",
-    "#     return row\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "empirical-suspect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "helpful-medline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "activated-excuse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "baking-input",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cognitive-decade",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "pacific-warehouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outdoor-sense",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_3M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "handy-coral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_10M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wired-treatment",
-   "metadata": {},
-   "source": [
-    "# JC Similarity Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "textile-productivity",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "aggregate-tiger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "cardiac-fence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "978c7892341342029d881b0bf31a6f66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/303284 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "could not convert string to float: ''\n",
-      "Resp not found for Q102108504, Q100171002\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "def fetchJCSim(row):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "    try:\n",
-    "        row['similarity_value'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['similarity_value'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "attached-novelty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Unnamed: 0                                      0\n",
-       " id              Q17372279-P279-Q100026-beba8cd1-0\n",
-       " node1                                   Q17372279\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.666525\n",
-       " classSim                                 0.835782\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 0, dtype: object,\n",
-       " Unnamed: 0                                      1\n",
-       " id              Q17372377-P279-Q100026-fd42bd71-0\n",
-       " node1                                   Q17372377\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.700515\n",
-       " classSim                                  0.94674\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 1, dtype: object,\n",
-       " Unnamed: 0                                      2\n",
-       " id              Q17372444-P279-Q100026-ca0fc4bd-0\n",
-       " node1                                   Q17372444\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.683245\n",
-       " classSim                                 0.946647\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 2, dtype: object,\n",
-       " Unnamed: 0                                      3\n",
-       " id              Q17372455-P279-Q100026-c2b1bf36-0\n",
-       " node1                                   Q17372455\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.689516\n",
-       " classSim                                 0.943256\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 3, dtype: object,\n",
-       " Unnamed: 0                                     4\n",
-       " id              Q2029940-P279-Q100026-ceba4380-0\n",
-       " node1                                   Q2029940\n",
-       " label                                       P279\n",
-       " node2                                    Q100026\n",
-       " bert2SentSim                            0.603161\n",
-       " classSim                                0.809487\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 4, dtype: object,\n",
-       " Unnamed: 0                                    5\n",
-       " id              Q209480-P279-Q100026-a218e96b-0\n",
-       " node1                                   Q209480\n",
-       " label                                      P279\n",
-       " node2                                   Q100026\n",
-       " bert2SentSim                            0.61352\n",
-       " classSim                               0.975716\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 5, dtype: object,\n",
-       " Unnamed: 0                                      6\n",
-       " id              Q77929250-P279-Q100026-964ec021-0\n",
-       " node1                                   Q77929250\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                              0.76767\n",
-       " classSim                                 0.961308\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 6, dtype: object,\n",
-       " Unnamed: 0                                      7\n",
-       " id              Q1087043-P279-Q1000371-945e7797-0\n",
-       " node1                                    Q1087043\n",
-       " label                                        P279\n",
-       " node2                                    Q1000371\n",
-       " bert2SentSim                             0.699843\n",
-       " classSim                                 0.671266\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 7, dtype: object,\n",
-       " Unnamed: 0                                    8\n",
-       " id              Q384656-P279-Q100047-6147b9a8-0\n",
-       " node1                                   Q384656\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 8, dtype: object,\n",
-       " Unnamed: 0                                    9\n",
-       " id              Q471089-P279-Q100047-473b504b-0\n",
-       " node1                                   Q471089\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.679593\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 9, dtype: object,\n",
-       " Unnamed: 0                                   10\n",
-       " id              Q471094-P279-Q100047-14b1b1df-0\n",
-       " node1                                   Q471094\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 10, dtype: object,\n",
-       " Unnamed: 0                                   11\n",
-       " id              Q471102-P279-Q100047-886a847c-0\n",
-       " node1                                   Q471102\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 11, dtype: object,\n",
-       " Unnamed: 0                                   12\n",
-       " id              Q471127-P279-Q100047-6738fc46-0\n",
-       " node1                                   Q471127\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 12, dtype: object,\n",
-       " Unnamed: 0                                   13\n",
-       " id              Q471144-P279-Q100047-766e1fa3-0\n",
-       " node1                                   Q471144\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.811462\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 13, dtype: object,\n",
-       " Unnamed: 0                                   14\n",
-       " id              Q471158-P279-Q100047-7955aa40-0\n",
-       " node1                                   Q471158\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 14, dtype: object,\n",
-       " Unnamed: 0                                         15\n",
-       " id              Q100052562-P279-Q100052008-1a4fa432-0\n",
-       " node1                                      Q100052562\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.515993\n",
-       " classSim                                     0.913344\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 15, dtype: object,\n",
-       " Unnamed: 0                                         16\n",
-       " id              Q100052791-P279-Q100052008-1d15b1ae-0\n",
-       " node1                                      Q100052791\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.478483\n",
-       " classSim                                     0.907455\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 16, dtype: object,\n",
-       " Unnamed: 0                                         17\n",
-       " id              Q100119502-P279-Q100052008-d2c330a4-0\n",
-       " node1                                      Q100119502\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                  0.51221\n",
-       " classSim                                     0.913229\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 17, dtype: object,\n",
-       " Unnamed: 0                                        18\n",
-       " id              Q88594442-P279-Q100052008-badba21b-0\n",
-       " node1                                      Q88594442\n",
-       " label                                           P279\n",
-       " node2                                     Q100052008\n",
-       " bert2SentSim                                0.424672\n",
-       " classSim                                     0.91916\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 18, dtype: object,\n",
-       " Unnamed: 0                                     19\n",
-       " id              Q1133024-P279-Q1000660-6d8a1a17-0\n",
-       " node1                                    Q1133024\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.556017\n",
-       " classSim                                  0.96031\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 19, dtype: object,\n",
-       " Unnamed: 0                                     20\n",
-       " id              Q1231309-P279-Q1000660-656bad41-0\n",
-       " node1                                    Q1231309\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.473025\n",
-       " classSim                                 0.987153\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 20, dtype: object,\n",
-       " Unnamed: 0                                     21\n",
-       " id              Q1892554-P279-Q1000660-b60addd4-0\n",
-       " node1                                    Q1892554\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.710084\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 21, dtype: object,\n",
-       " Unnamed: 0                                     22\n",
-       " id              Q2835946-P279-Q1000660-ff0a5cf9-0\n",
-       " node1                                    Q2835946\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                              0.69158\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 22, dtype: object,\n",
-       " Unnamed: 0                                     23\n",
-       " id              Q3413373-P279-Q1000660-9d565f5e-0\n",
-       " node1                                    Q3413373\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.676028\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 23, dtype: object,\n",
-       " Unnamed: 0                                     24\n",
-       " id              Q5611316-P279-Q1000660-6b051008-0\n",
-       " node1                                    Q5611316\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.652025\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 24, dtype: object,\n",
-       " Unnamed: 0                                    25\n",
-       " id              Q649977-P279-Q1000660-cc4475c9-0\n",
-       " node1                                    Q649977\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.548393\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 25, dtype: object,\n",
-       " Unnamed: 0                                     26\n",
-       " id              Q7269432-P279-Q1000660-8976e0ff-0\n",
-       " node1                                    Q7269432\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.439381\n",
-       " classSim                                 0.989483\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 26, dtype: object,\n",
-       " Unnamed: 0                                    27\n",
-       " id              Q844980-P279-Q1000660-5f020e98-0\n",
-       " node1                                    Q844980\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.678084\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 27, dtype: object,\n",
-       " Unnamed: 0                                     28\n",
-       " id              Q1051606-P279-Q1000809-995e5221-0\n",
-       " node1                                    Q1051606\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.740599\n",
-       " classSim                                 0.912943\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 28, dtype: object,\n",
-       " Unnamed: 0                                     29\n",
-       " id              Q4984171-P279-Q1000809-95749f41-0\n",
-       " node1                                    Q4984171\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.808338\n",
-       " classSim                                 0.957835\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 29, dtype: object,\n",
-       " Unnamed: 0                                     30\n",
-       " id              Q2047562-P279-Q1001041-45feee81-0\n",
-       " node1                                    Q2047562\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.359032\n",
-       " classSim                                 0.707243\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 30, dtype: object,\n",
-       " Unnamed: 0                                     31\n",
-       " id              Q8069073-P279-Q1001041-3f13f5bb-0\n",
-       " node1                                    Q8069073\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.769952\n",
-       " classSim                                 0.849692\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 31, dtype: object,\n",
-       " Unnamed: 0                                     32\n",
-       " id              Q1070955-P279-Q1001051-5df03dac-0\n",
-       " node1                                    Q1070955\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.800508\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 32, dtype: object,\n",
-       " Unnamed: 0                                     33\n",
-       " id              Q1153367-P279-Q1001051-34783d55-0\n",
-       " node1                                    Q1153367\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.837581\n",
-       " classSim                                 0.876525\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 33, dtype: object,\n",
-       " Unnamed: 0                                     34\n",
-       " id              Q1958065-P279-Q1001051-d6eb5968-0\n",
-       " node1                                    Q1958065\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.848892\n",
-       " classSim                                 0.707989\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 34, dtype: object,\n",
-       " Unnamed: 0                                    35\n",
-       " id              Q209680-P279-Q1001051-072eeb1b-0\n",
-       " node1                                    Q209680\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.656872\n",
-       " classSim                                0.724838\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 35, dtype: object,\n",
-       " Unnamed: 0                                     36\n",
-       " id              Q2986204-P279-Q1001051-7b21695a-0\n",
-       " node1                                    Q2986204\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.816212\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 36, dtype: object,\n",
-       " Unnamed: 0                                    37\n",
-       " id              Q385530-P279-Q1001051-17db3704-0\n",
-       " node1                                    Q385530\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.75061\n",
-       " classSim                                0.780191\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 37, dtype: object,\n",
-       " Unnamed: 0                                      38\n",
-       " id              Q41795401-P279-Q1001051-5747dcc4-0\n",
-       " node1                                    Q41795401\n",
-       " label                                         P279\n",
-       " node2                                     Q1001051\n",
-       " bert2SentSim                              0.787191\n",
-       " classSim                                   0.88381\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 38, dtype: object,\n",
-       " Unnamed: 0                                    39\n",
-       " id              Q612405-P279-Q1001051-bb6fa17c-0\n",
-       " node1                                    Q612405\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.72366\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 39, dtype: object,\n",
-       " Unnamed: 0                                    40\n",
-       " id              Q668439-P279-Q1001051-bbba9ea3-0\n",
-       " node1                                    Q668439\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.698926\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 40, dtype: object,\n",
-       " Unnamed: 0                                     41\n",
-       " id              Q7133779-P279-Q1001051-4fc71627-0\n",
-       " node1                                    Q7133779\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.576617\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 41, dtype: object,\n",
-       " Unnamed: 0                                     42\n",
-       " id              Q7630946-P279-Q1001051-2e78f5b5-0\n",
-       " node1                                    Q7630946\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.632298\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 42, dtype: object,\n",
-       " Unnamed: 0                                     43\n",
-       " id              Q1056352-P279-Q1001059-c1976180-0\n",
-       " node1                                    Q1056352\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.717131\n",
-       " classSim                                 0.886053\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 43, dtype: object,\n",
-       " Unnamed: 0                                     44\n",
-       " id              Q2123051-P279-Q1001059-18b4564b-0\n",
-       " node1                                    Q2123051\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.606046\n",
-       " classSim                                 0.465094\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 44, dtype: object,\n",
-       " Unnamed: 0                                    45\n",
-       " id              Q444278-P279-Q1001059-0c74af28-0\n",
-       " node1                                    Q444278\n",
-       " label                                       P279\n",
-       " node2                                   Q1001059\n",
-       " bert2SentSim                            0.481163\n",
-       " classSim                                0.881922\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 45, dtype: object,\n",
-       " Unnamed: 0                                     46\n",
-       " id              Q7240696-P279-Q1001059-31dfa177-0\n",
-       " node1                                    Q7240696\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.752948\n",
-       " classSim                                 0.881922\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 46, dtype: object,\n",
-       " Unnamed: 0                                      47\n",
-       " id              Q85776190-P279-Q1001059-b7a4aad6-0\n",
-       " node1                                    Q85776190\n",
-       " label                                         P279\n",
-       " node2                                     Q1001059\n",
-       " bert2SentSim                              0.747289\n",
-       " classSim                                   0.53963\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 47, dtype: object,\n",
-       " Unnamed: 0                                         48\n",
-       " id              Q100052008-P279-Q100116222-d1597eca-0\n",
-       " node1                                      Q100052008\n",
-       " label                                            P279\n",
-       " node2                                      Q100116222\n",
-       " bert2SentSim                                 0.725062\n",
-       " classSim                                     0.909364\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 48, dtype: object,\n",
-       " Unnamed: 0                                        49\n",
-       " id              Q27303706-P279-Q100116222-526530c6-0\n",
-       " node1                                      Q27303706\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.537653\n",
-       " classSim                                    0.917887\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 49, dtype: object,\n",
-       " Unnamed: 0                                        50\n",
-       " id              Q99565932-P279-Q100116222-139f958c-0\n",
-       " node1                                      Q99565932\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.691907\n",
-       " classSim                                    0.892978\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 50, dtype: object,\n",
-       " Unnamed: 0                                     51\n",
-       " id              Q1416523-P279-Q1001329-2b6d0c1e-0\n",
-       " node1                                    Q1416523\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.160931\n",
-       " classSim                                 0.871646\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 51, dtype: object,\n",
-       " Unnamed: 0                                     52\n",
-       " id              Q2963918-P279-Q1001329-c85a352f-0\n",
-       " node1                                    Q2963918\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.469497\n",
-       " classSim                                 0.878703\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 52, dtype: object,\n",
-       " Unnamed: 0                                      53\n",
-       " id              Q56756850-P279-Q1001329-6cb334b1-0\n",
-       " node1                                    Q56756850\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.412103\n",
-       " classSim                                  0.363595\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 53, dtype: object,\n",
-       " Unnamed: 0                                      54\n",
-       " id              Q60782200-P279-Q1001329-0b4da903-0\n",
-       " node1                                    Q60782200\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.239958\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 54, dtype: object,\n",
-       " Unnamed: 0                                      55\n",
-       " id              Q78841106-P279-Q1001329-d474c822-0\n",
-       " node1                                    Q78841106\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.496099\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 55, dtype: object,\n",
-       " Unnamed: 0                                    56\n",
-       " id              Q798863-P279-Q1001329-c284c2ec-0\n",
-       " node1                                    Q798863\n",
-       " label                                       P279\n",
-       " node2                                   Q1001329\n",
-       " bert2SentSim                            0.597623\n",
-       " classSim                                0.871646\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 56, dtype: object,\n",
-       " Unnamed: 0                                      57\n",
-       " id              Q96337569-P279-Q1001329-b8b30fb9-0\n",
-       " node1                                    Q96337569\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.210435\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 57, dtype: object,\n",
-       " Unnamed: 0                                       58\n",
-       " id              Q3403683-P279-Q100135295-fd0a0a92-0\n",
-       " node1                                      Q3403683\n",
-       " label                                          P279\n",
-       " node2                                    Q100135295\n",
-       " bert2SentSim                               0.371629\n",
-       " classSim                                   0.811291\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 58, dtype: object,\n",
-       " Unnamed: 0                                         59\n",
-       " id              Q100095237-P279-Q100136951-a7f6f753-0\n",
-       " node1                                      Q100095237\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.909817\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 59, dtype: object,\n",
-       " Unnamed: 0                                         60\n",
-       " id              Q100136066-P279-Q100136951-1e2368cd-0\n",
-       " node1                                      Q100136066\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.843155\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 60, dtype: object,\n",
-       " Unnamed: 0                                      61\n",
-       " id              Q235041-P279-Q100136951-07d30679-0\n",
-       " node1                                      Q235041\n",
-       " label                                         P279\n",
-       " node2                                   Q100136951\n",
-       " bert2SentSim                              0.607752\n",
-       " classSim                                  0.719618\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 61, dtype: object,\n",
-       " Unnamed: 0                                      62\n",
-       " id              Q22006653-P279-Q1001378-98eddb62-0\n",
-       " node1                                    Q22006653\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.366579\n",
-       " classSim                                    0.5263\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 62, dtype: object,\n",
-       " Unnamed: 0                                     63\n",
-       " id              Q2895476-P279-Q1001378-9d15228e-0\n",
-       " node1                                    Q2895476\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.682378\n",
-       " classSim                                  0.75066\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 63, dtype: object,\n",
-       " Unnamed: 0                                     64\n",
-       " id              Q2918907-P279-Q1001378-39c09697-0\n",
-       " node1                                    Q2918907\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.813292\n",
-       " classSim                                 0.775328\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 64, dtype: object,\n",
-       " Unnamed: 0                                     65\n",
-       " id              Q3516833-P279-Q1001378-30384a7c-0\n",
-       " node1                                    Q3516833\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.684669\n",
-       " classSim                                 0.916831\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 65, dtype: object,\n",
-       " Unnamed: 0                                     66\n",
-       " id              Q4129855-P279-Q1001378-3ad464f7-0\n",
-       " node1                                    Q4129855\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.740775\n",
-       " classSim                                 0.905545\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 66, dtype: object,\n",
-       " Unnamed: 0                                     67\n",
-       " id              Q5197004-P279-Q1001378-513f9939-0\n",
-       " node1                                    Q5197004\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.642596\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 67, dtype: object,\n",
-       " Unnamed: 0                                     68\n",
-       " id              Q7237336-P279-Q1001378-55e73e1a-0\n",
-       " node1                                    Q7237336\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                              0.58928\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 68, dtype: object,\n",
-       " Unnamed: 0                                    69\n",
-       " id              Q738160-P279-Q1001378-b69af0cb-0\n",
-       " node1                                    Q738160\n",
-       " label                                       P279\n",
-       " node2                                   Q1001378\n",
-       " bert2SentSim                            0.813292\n",
-       " classSim                                0.892815\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 69, dtype: object,\n",
-       " Unnamed: 0                                  70\n",
-       " id              Q8317-P279-Q1001378-a56c669f-0\n",
-       " node1                                    Q8317\n",
-       " label                                     P279\n",
-       " node2                                 Q1001378\n",
-       " bert2SentSim                          0.636886\n",
-       " classSim                              0.541961\n",
-       " Resp_code                     <Response [200]>\n",
-       " Name: 70, dtype: object,\n",
-       " Unnamed: 0                                      71\n",
-       " id              Q86908314-P279-Q1001378-7a81e91b-0\n",
-       " node1                                    Q86908314\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.421366\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 71, dtype: object,\n",
-       " Unnamed: 0                                      72\n",
-       " id              Q86910224-P279-Q1001378-b427331c-0\n",
-       " node1                                    Q86910224\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.584772\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 72, dtype: object,\n",
-       " Unnamed: 0                                         73\n",
-       " id              Q100148346-P279-Q100148352-003654ac-0\n",
-       " node1                                      Q100148346\n",
-       " label                                            P279\n",
-       " node2                                      Q100148352\n",
-       " bert2SentSim                                 0.872339\n",
-       " classSim                                     0.951283\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 73, dtype: object,\n",
-       " Unnamed: 0                                        74\n",
-       " id              Q27147769-P279-Q100148352-8254b7c4-0\n",
-       " node1                                      Q27147769\n",
-       " label                                           P279\n",
-       " node2                                     Q100148352\n",
-       " bert2SentSim                                0.886007\n",
-       " classSim                                    0.924902\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 74, dtype: object,\n",
-       " Unnamed: 0                                     75\n",
-       " id              Q14747611-P279-Q100159-567e45a7-0\n",
-       " node1                                   Q14747611\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                              0.50114\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 75, dtype: object,\n",
-       " Unnamed: 0                                     76\n",
-       " id              Q17089549-P279-Q100159-3247e0d4-0\n",
-       " node1                                   Q17089549\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.705335\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 76, dtype: object,\n",
-       " Unnamed: 0                                     77\n",
-       " id              Q18923049-P279-Q100159-04db261f-0\n",
-       " node1                                   Q18923049\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.861815\n",
-       " classSim                                 0.719858\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 77, dtype: object,\n",
-       " Unnamed: 0                                    78\n",
-       " id              Q2056084-P279-Q100159-94d1b7b8-0\n",
-       " node1                                   Q2056084\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.782052\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 78, dtype: object,\n",
-       " Unnamed: 0                                    79\n",
-       " id              Q5097845-P279-Q100159-c2bd0b24-0\n",
-       " node1                                   Q5097845\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.551256\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 79, dtype: object,\n",
-       " Unnamed: 0                                    80\n",
-       " id              Q6061403-P279-Q100159-d5101564-0\n",
-       " node1                                   Q6061403\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.565718\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 80, dtype: object,\n",
-       " Unnamed: 0                                   81\n",
-       " id              Q652653-P279-Q100159-bd6c5d3a-0\n",
-       " node1                                   Q652653\n",
-       " label                                      P279\n",
-       " node2                                   Q100159\n",
-       " bert2SentSim                           0.501037\n",
-       " classSim                               0.837687\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 81, dtype: object,\n",
-       " Unnamed: 0                                     82\n",
-       " id              Q66293226-P279-Q100159-ad4b8016-0\n",
-       " node1                                   Q66293226\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                               0.8089\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 82, dtype: object,\n",
-       " Unnamed: 0                                         83\n",
-       " id              Q100166380-P279-Q100166391-4684ea65-0\n",
-       " node1                                      Q100166380\n",
-       " label                                            P279\n",
-       " node2                                      Q100166391\n",
-       " bert2SentSim                                 0.752609\n",
-       " classSim                                     0.799499\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 83, dtype: object,\n",
-       " Unnamed: 0                                      84\n",
-       " id              Q244326-P279-Q100166391-35359099-0\n",
-       " node1                                      Q244326\n",
-       " label                                         P279\n",
-       " node2                                   Q100166391\n",
-       " bert2SentSim                              0.522545\n",
-       " classSim                                  0.470975\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 84, dtype: object,\n",
-       " Unnamed: 0                                     85\n",
-       " id              Q40551-P279-Q100166391-7242baf9-0\n",
-       " node1                                      Q40551\n",
-       " label                                        P279\n",
-       " node2                                  Q100166391\n",
-       " bert2SentSim                              0.74986\n",
-       " classSim                                 0.781795\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 85, dtype: object,\n",
-       " Unnamed: 0                                         86\n",
-       " id              Q102108504-P279-Q100171002-7ccf3934-0\n",
-       " node1                                      Q102108504\n",
-       " label                                            P279\n",
-       " node2                                      Q100171002\n",
-       " bert2SentSim                                 0.680867\n",
-       " classSim                                         None\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 86, dtype: object,\n",
-       " Unnamed: 0                                       87\n",
-       " id              Q1150647-P279-Q100171002-fb1cad26-0\n",
-       " node1                                      Q1150647\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.664551\n",
-       " classSim                                   0.795416\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 87, dtype: object,\n",
-       " Unnamed: 0                                       88\n",
-       " id              Q1503443-P279-Q100171002-8282ae74-0\n",
-       " node1                                      Q1503443\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.748802\n",
-       " classSim                                   0.822611\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 88, dtype: object,\n",
-       " Unnamed: 0                                       89\n",
-       " id              Q2616305-P279-Q100171002-49dc8346-0\n",
-       " node1                                      Q2616305\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.646817\n",
-       " classSim                                   0.887266\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 89, dtype: object,\n",
-       " Unnamed: 0                                        90\n",
-       " id              Q24050179-P279-Q100195948-90966900-0\n",
-       " node1                                      Q24050179\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.652214\n",
-       " classSim                                    0.698738\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 90, dtype: object,\n",
-       " Unnamed: 0                                        91\n",
-       " id              Q29479539-P279-Q100195948-e222789a-0\n",
-       " node1                                      Q29479539\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.689148\n",
-       " classSim                                    0.681145\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 91, dtype: object,\n",
-       " Unnamed: 0                                      92\n",
-       " id              Q308859-P279-Q100195948-a5841742-0\n",
-       " node1                                      Q308859\n",
-       " label                                         P279\n",
-       " node2                                   Q100195948\n",
-       " bert2SentSim                              0.750328\n",
-       " classSim                                  0.279686\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 92, dtype: object,\n",
-       " Unnamed: 0                                         93\n",
-       " id              Q100251305-P279-Q100251275-02ddd08e-0\n",
-       " node1                                      Q100251305\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.486286\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 93, dtype: object,\n",
-       " Unnamed: 0                                         94\n",
-       " id              Q100251705-P279-Q100251275-fa0c89c7-0\n",
-       " node1                                      Q100251705\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.370606\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 94, dtype: object,\n",
-       " Unnamed: 0                                        95\n",
-       " id              Q16007968-P279-Q100251275-318d5598-0\n",
-       " node1                                      Q16007968\n",
-       " label                                           P279\n",
-       " node2                                     Q100251275\n",
-       " bert2SentSim                                0.478886\n",
-       " classSim                                      0.9303\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 95, dtype: object,\n",
-       " Unnamed: 0                                       96\n",
-       " id              Q3310017-P279-Q100251275-772909ea-0\n",
-       " node1                                      Q3310017\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                                0.57202\n",
-       " classSim                                   0.929488\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 96, dtype: object,\n",
-       " Unnamed: 0                                       97\n",
-       " id              Q4855428-P279-Q100251275-fbf2100a-0\n",
-       " node1                                      Q4855428\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                               0.545835\n",
-       " classSim                                   0.931676\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 97, dtype: object,\n",
-       " Unnamed: 0                                      98\n",
-       " id              Q862086-P279-Q100251275-faf57342-0\n",
-       " node1                                      Q862086\n",
-       " label                                         P279\n",
-       " node2                                   Q100251275\n",
-       " bert2SentSim                              0.630551\n",
-       " classSim                                  0.948207\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 98, dtype: object,\n",
-       " Unnamed: 0                                        99\n",
-       " id              Q15209696-P279-Q100257358-c4b60f40-0\n",
-       " node1                                      Q15209696\n",
-       " label                                           P279\n",
-       " node2                                     Q100257358\n",
-       " bert2SentSim                                0.484455\n",
-       " classSim                                    0.610338\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 99, dtype: object]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "worth-croatia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "changing-burning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "portuguese-practitioner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "breathing-bicycle",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "historic-maria",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spanish-killer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_3M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collectible-modern",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_10M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "removable-watershed",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fossil-psychiatry",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "rapid-drink",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "liquid-symphony",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intellectual-rainbow",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integrated-intention",
-   "metadata": {},
-   "source": [
-    "# Wiki-CS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "functioning-scholarship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unique-weekend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs-20200504.tsv',sep='\\t')\n",
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])\n",
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')\n",
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "located-swaziland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sharing-scientist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]\n",
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fewer-place",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})\n",
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label']].to_csv('../data/wikidata-cs_categorized.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "better-planet",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "enhanced-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs_categorized.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "complex-character",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "exciting-anderson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I_set_size = len(wikiDF[wikiDF.category == 'I'])\n",
-    "M_sampled_set = wikiDF[wikiDF.category == 'M'].sample(I_set_size, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "thrown-graham",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import combinations\n",
-    "\n",
-    "def find_relation_mapping(mainset, subset, needed_size):\n",
-    "    nodes = set([row['word1_kg_id'] for _,row in subset.iterrows()] \n",
-    "            + [row['word2_kg_id'] for _,row in subset.iterrows()])\n",
-    "    node_label_mappings = {row['word1_kg_id']: row['node1;label'] for _, row in subset.iterrows()}.update(\n",
-    "                                {row['word2_kg_id']: row['node2;label'] for _, row in subset.iterrows()}\n",
-    "                        )\n",
-    "    mainset_pairs = set([(row['word1_kg_id'], row['word2_kg_id']) for _,row in mainset.iterrows()])\n",
-    "\n",
-    "    all_pairs = set([pair for pair in list(combinations(list(nodes), 2))[10*needed_size]])\n",
-    "    \n",
-    "    removed_pairs = set()\n",
-    "    \n",
-    "    first_line = True\n",
-    "    with gzip.open('../data/wikidata-20210215-dwd/claims.tsv.gz', 'r') as claims_file:\n",
-    "        for line in tqdm(claims_file,total=98482585):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            curr_pair = (line[1], line[3])\n",
-    "            if curr_pair in all_pairs:\n",
-    "                removed_pairs.add(curr_pair)\n",
-    "    newDF = []\n",
-    "    for pair in all_pairs - removed_pairs - mainset_pairs:\n",
-    "        try:\n",
-    "            if pair[0] is None or pair[1] is None:\n",
-    "                continue\n",
-    "            newDF.append(pair[0], pair[1], 'U', node_label_mappings[pair[0]], node_label_mappings[pair[1]])\n",
-    "        except:\n",
-    "            print(pair)\n",
-    "    return pd.DataFrame(newDF, columns=subset.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "immune-jesus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3df524d9d4fd47e6864fe57299e1f8e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/98482585 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q1070669\n",
-      "Q20755059\n",
-      "CPU times: user 13min 20s, sys: 1min 24s, total: 14min 44s\n",
-      "Wall time: 14min 40s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "U_sampled_set = find_relation_mapping(wikiDF, wikiDF[wikiDF.category == 'M'], I_set_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "transparent-weather",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(U_sampled_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "graduate-guarantee",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "amended-support",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-knowing",
-   "metadata": {},
-   "source": [
-    "# Check Diff in versions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "under-simon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "275927\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "def get_all_nodes():\n",
-    "    p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "    wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "    concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "excess-killer",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "files_set_1 = ['../data/wikidata-20210215-dwd/descriptions.en.tsv.gz',\n",
-    "              '../data/wikidata-20210215-dwd/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "files_set_2 = [DESCRIPTIONS_FILE,\n",
-    "              '../../wd-correctness/gdrive-kgtk-dump-2020-12-07/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "\n",
-    "all_nodes_set = set(allNodes)\n",
-    "for file1, file2 in zip(files_set_1, files_set_2):\n",
-    "    file1_dict, file2_dict = {}, {}\n",
-    "    with gzip.open(file1) as file1:\n",
-    "        for line in tqdm(file1):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file1_dict[line[1]] = line[3][1:-4]\n",
-    "    with gzip.open(file2) as file2:\n",
-    "        for line in tqdm(file2):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file2_dict[line[1]] = line[3][1:-4]\n",
-    "    for elem in file1_dict.keys():\n",
-    "        try:\n",
-    "            if file1_dict[elem] != file2_dict[elem]:\n",
-    "                print(elem, file1_dict[elem], file2_dict[elem])\n",
-    "        except:\n",
-    "            continue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "integrated-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "romantic-rebecca",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "needed-convergence",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
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- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
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-    "name": "ipython",
-    "version": 3
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diff --git a/.ipynb_checkpoints/PyKeen Experiments-checkpoint.ipynb b/.ipynb_checkpoints/PyKeen Experiments-checkpoint.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/.ipynb_checkpoints/PyKeen Experiments-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC Reorganized-checkpoint.ipynb b/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC Reorganized-checkpoint.ipynb
deleted file mode 100644
index 9d333f4..0000000
--- a/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC Reorganized-checkpoint.ipynb	
+++ /dev/null
@@ -1,9853 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "strange-saturn",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "random-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "incorporated-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'id', 'node1_label', 'label', 'node2_label', 'sent',\n",
-       "       'node1_emb', 'node2_emb', 'bert2SentSim', 'abstract',\n",
-       "       'abstract_firstSent', 'BERT_abstract_emb',\n",
-       "       'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'textEmb_7props_left',\n",
-       "       'textEmb_7props_right', 'textEmb_2props_left', 'textEmb_2props_right',\n",
-       "       'textEmb_7props_cosSim', 'textEmb_2props_cosSim',\n",
-       "       'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.772184\n",
-       "std          0.122326\n",
-       "min          0.120480\n",
-       "25%          0.684141\n",
-       "50%          0.793718\n",
-       "75%          0.874284\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "under-rabbit",
-   "metadata": {},
-   "source": [
-    "# Embeddings Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "annoying-intake",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Complex, Transe Embeddings Datasets generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "tamil-activity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "immune-nashville",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "residential-cleaners",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "current-great",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id  \n",
-       "0        Q454  \n",
-       "1   Q13648784  \n",
-       "2     Q178022  \n",
-       "3   Q21004260  \n",
-       "4      Q49112  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "valued-progressive",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# wordSimSet = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "different-vancouver",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list() + p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list() + wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-lewis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19166"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "collected-regular",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1499717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4765290\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16000518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6423382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2177259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q100448831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2857578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13577338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q96158854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7892\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7675683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2380954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21083881\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q211521\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16023742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q97627995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16721350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q32979618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4553362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q98232491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66194218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5372\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25481995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61466331\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6983403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84105477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30107768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56753514\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91927989\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12047900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11698973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q517386\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17097928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19838691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q101541623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17376918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23013268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66363580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55693905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68131879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85803433\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21406831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29957548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78710574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6462051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1042920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7925\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28324850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2849391\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "elect-gothic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(compEmbeddings.items(), columns=['node', 'complex_embedding']).to_csv('../data/wordsim353_complex_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "combined-theme",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(transeEmbeddings.items(), columns=['node', 'transe_embedding']).to_csv('../data/wordsim353_transe_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-extraction",
-   "metadata": {},
-   "source": [
-    "# Basis Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hairy-tuition",
-   "metadata": {},
-   "source": [
-    "## Probase Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "opened-quarter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = pd.read_csv('../data/probase/probase_WQnodes_subset_and_sim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "major-peter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1_qnode</th>\n",
-       "      <th>node2_qnode</th>\n",
-       "      <th>n1_final_qnode</th>\n",
-       "      <th>n2_final_qnode</th>\n",
-       "      <th>sim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>gender</td>\n",
-       "      <td>14230</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>0.913568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>temperature</td>\n",
-       "      <td>13660</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>P2076</td>\n",
-       "      <td>0.909663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>7</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>copper</td>\n",
-       "      <td>11142</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>Q15830500</td>\n",
-       "      <td>0.890199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>variable</td>\n",
-       "      <td>age</td>\n",
-       "      <td>9375</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q10954303</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>0.873703</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0 node1_label  node2_label  no_of_relations node1_qnode  \\\n",
-       "0           0      factor          age            35167         NaN   \n",
-       "1           5      factor       gender            14230         NaN   \n",
-       "2           6      factor  temperature            13660         NaN   \n",
-       "3           7       metal       copper            11142      Q11426   \n",
-       "4           9    variable          age             9375         NaN   \n",
-       "\n",
-       "  node2_qnode n1_final_qnode n2_final_qnode       sim  \n",
-       "0         NaN      Q15061738     Q100343219  1.000000  \n",
-       "1      Q48277      Q15061738         Q48277  0.913568  \n",
-       "2         NaN      Q15061738          P2076  0.909663  \n",
-       "3         NaN         Q11426      Q15830500  0.890199  \n",
-       "4         NaN      Q10954303     Q100343219  0.873703  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "photographic-character",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes1_subset.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'bert2SentSim'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "russian-proceeding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'] = 0.5 + 0.5 * probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "painted-selection",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-portsmouth",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## 19k+19k dataset with class similarity scores generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "sublime-girlfriend",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "classSim = []\n",
-    "\n",
-    "for _, row in tqdm(p279WordSimSeededDF_wabs_text.iterrows()):\n",
-    "    resp = requests.get(\"https://dsbox02.isi.edu:8888/qnode-similarity?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "    row['bert2SentSim'] = float(resp) if resp else -1\n",
-    "    classSim.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "optional-bracelet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff86112887254e7f950c4586072a22f5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "for _, row in tqdm(p279Seeded_SiblingsDF3_wabs_text.iterrows()):\n",
-    "    resp = requests.get(\"https://dsbox02.isi.edu:8888/qnode-similarity?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "    row['bert2SentSim'] = float(resp) if resp else -1\n",
-    "    classSim.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "interior-yield",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "classSimT = []\n",
-    "for class1 in classSim:\n",
-    "    classSimT.append(pd.DataFrame(class1).transpose())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "single-ethiopia",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "classSimDF = pd.concat(classSimT, ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "stuck-formation",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "classSimDF['bert2SentSim'] = classSimDF.bert2SentSim.astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "enclosed-plenty",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "classSimDF.loc[classSimDF['bert2SentSim'] == -1, ['bert2SentSim']] = 0.621981"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "public-thesis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    38790.000000\n",
-       "mean         0.621981\n",
-       "std          0.248664\n",
-       "min         -0.035900\n",
-       "25%          0.426818\n",
-       "50%          0.720367\n",
-       "75%          0.840006\n",
-       "max          0.985841\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classSimDF[classSimDF['bert2SentSim'] != -1]['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "becoming-thomson",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "max1 = classSimDF['bert2SentSim'].max()\n",
-    "min1 = classSimDF['bert2SentSim'].min()\n",
-    "classSimDF['bert2SentSim'] = classSimDF['bert2SentSim'].apply(lambda p: (p - min1) / (max1 - min1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "coastal-edition",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.840243</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.672269</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.613978</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.50806</td>\n",
-       "      <td>0.455747</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798362</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.77003</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.320911</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.840243  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.672269  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.613978  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798362  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.320911  ...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \\\n",
-       "0              0.823419                 -1.0                           -1.0   \n",
-       "1              0.734428                 -1.0                           -1.0   \n",
-       "2              0.707611              0.50806                       0.455747   \n",
-       "3              0.847591             0.855959                        0.77003   \n",
-       "4              0.780342             0.752693                       0.515319   \n",
-       "\n",
-       "  parent par_label node1_sent node2_sent comb_sent  \n",
-       "0    NaN       NaN        NaN        NaN       NaN  \n",
-       "1    NaN       NaN        NaN        NaN       NaN  \n",
-       "2    NaN       NaN        NaN        NaN       NaN  \n",
-       "3    NaN       NaN        NaN        NaN       NaN  \n",
-       "4    NaN       NaN        NaN        NaN       NaN  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "id": "governing-detector",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "classSimDF[:19454].to_csv('../data/P279_ChildPar_19k_WEmbAndClassSim.csv', index=None)\n",
-    "classSimDF[19454:].to_csv('../data/P279_Siblings_19k_WEmbAndClassSim.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compact-bridges",
-   "metadata": {},
-   "source": [
-    "## 19k+19k dataset with class similarity scores fetch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "nominated-blogger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimTargetChildPar = pd.read_csv('../data/P279_ChildPar_19k_WEmbAndClassSim.csv')\n",
-    "classSimTargetSiblings = pd.read_csv('../data/P279_Siblings_19k_WEmbAndClassSim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "korean-guitar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(classSimTargetSiblings['bert2SentSim'] == 0.5).sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hundred-virus",
-   "metadata": {},
-   "source": [
-    "# Ground Truth Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "limited-hormone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDF = pd.read_csv('../data/pedersen2007measures_table1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "alien-happiness",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Term1</th>\n",
-       "      <th>Term1_kg_id</th>\n",
-       "      <th>Term1_kg_label</th>\n",
-       "      <th>Term1_description</th>\n",
-       "      <th>Term2</th>\n",
-       "      <th>Term2_kg_id</th>\n",
-       "      <th>Term2_kg_label</th>\n",
-       "      <th>Term2_description</th>\n",
-       "      <th>Physician</th>\n",
-       "      <th>Coder</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Renal failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>Kidney failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Heart</td>\n",
-       "      <td>Q1072</td>\n",
-       "      <td>heart</td>\n",
-       "      <td>organ for the circulation of blood in animal c...</td>\n",
-       "      <td>Myocardium</td>\n",
-       "      <td>Q84133</td>\n",
-       "      <td>myocardium</td>\n",
-       "      <td>middle layer of the heart wall, which consists...</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Stroke</td>\n",
-       "      <td>Q12202</td>\n",
-       "      <td>stroke</td>\n",
-       "      <td>problem with the arteries supplying blood to t...</td>\n",
-       "      <td>Infarct</td>\n",
-       "      <td>Q207550</td>\n",
-       "      <td>infarction</td>\n",
-       "      <td>tissue death caused by a local lack of oxygen,...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Abortion</td>\n",
-       "      <td>Q8452</td>\n",
-       "      <td>abortion</td>\n",
-       "      <td>intentional ending of a pregnancy</td>\n",
-       "      <td>Miscarriage</td>\n",
-       "      <td>Q28693</td>\n",
-       "      <td>miscarriage</td>\n",
-       "      <td>natural death of an embryo or fetus before it ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Delusion</td>\n",
-       "      <td>Q189643</td>\n",
-       "      <td>delusion</td>\n",
-       "      <td>firm and fixed belief based on inadequate grou...</td>\n",
-       "      <td>Schizophrenia</td>\n",
-       "      <td>Q41112</td>\n",
-       "      <td>schizophrenia</td>\n",
-       "      <td>psychotic disorder characterized by emotional ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Term1 Term1_kg_id  Term1_kg_label  \\\n",
-       "0  Renal failure     Q476921  kidney failure   \n",
-       "1          Heart       Q1072           heart   \n",
-       "2         Stroke      Q12202          stroke   \n",
-       "3       Abortion       Q8452        abortion   \n",
-       "4       Delusion     Q189643        delusion   \n",
-       "\n",
-       "                                   Term1_description           Term2  \\\n",
-       "0  disease where the kidneys fail to adequately f...  Kidney failure   \n",
-       "1  organ for the circulation of blood in animal c...      Myocardium   \n",
-       "2  problem with the arteries supplying blood to t...         Infarct   \n",
-       "3                  intentional ending of a pregnancy     Miscarriage   \n",
-       "4  firm and fixed belief based on inadequate grou...   Schizophrenia   \n",
-       "\n",
-       "  Term2_kg_id  Term2_kg_label  \\\n",
-       "0     Q476921  kidney failure   \n",
-       "1      Q84133      myocardium   \n",
-       "2     Q207550      infarction   \n",
-       "3      Q28693     miscarriage   \n",
-       "4      Q41112   schizophrenia   \n",
-       "\n",
-       "                                   Term2_description  Physician  Coder  \n",
-       "0  disease where the kidneys fail to adequately f...        4.0    4.0  \n",
-       "1  middle layer of the heart wall, which consists...        3.3    3.0  \n",
-       "2  tissue death caused by a local lack of oxygen,...        3.0    2.8  \n",
-       "3  natural death of an embryo or fetus before it ...        3.0    3.3  \n",
-       "4  psychotic disorder characterized by emotional ...        3.0    2.2  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bioDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parental-event",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDFNodesSet = set(bioDF.Term1_kg_id.to_list() + bioDF.Term2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "wicked-proportion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childParNodesSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list())\n",
-    "P279siblingsNodesSet = set(p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "pediatric-tomato",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 1)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "transparent-diversity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 25)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probaseNodesSet = set(probDF_Qnodes_DF_WQnodes1_subset.node1.to_list() + probDF_Qnodes_DF_WQnodes1_subset.node2.to_list())\n",
-    "\n",
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in probaseNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in probaseNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "uniform-civilian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "voluntary-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consolidated-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "irish-disposal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase, weightAssignment=False):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            if weightAssignment:\n",
-    "                sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            else:\n",
-    "                sumOfSims = sum([1 for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "                \n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {},
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-milan",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Sample attempt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "substantial-aggregate",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text_sample = p279WordSimSeededDF_wabs_text[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "analyzed-passion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "japanese-defense",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dying-nigeria",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "reduced-avenue",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1034"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "advised-hollywood",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "authentic-greene",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1034.000000\n",
-       "mean        0.281990\n",
-       "std         0.054759\n",
-       "min         0.069792\n",
-       "25%         0.244698\n",
-       "50%         0.278019\n",
-       "75%         0.319656\n",
-       "max         0.450444\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "variable-transportation",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## ChildPar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "central-spyware",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "white-genesis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "registered-original",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "experienced-conspiracy",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "every-interface",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "specified-berry",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.283712\n",
-       "std          0.056573\n",
-       "min          0.050719\n",
-       "25%          0.246137\n",
-       "50%          0.281897\n",
-       "75%          0.319687\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incoming-calvin",
-   "metadata": {},
-   "source": [
-    "# Correlation Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "answering-exhaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    responseDict = {}\n",
-    "    responseDict['wordSimMissingSet'] = wordSimMissingSet\n",
-    "    responseDict['coveredPairs'] = len(wordSim353AnnotDF_New2)\n",
-    "    responseDict['totalPairs'] = len(wordSim353AnnotDF_New)\n",
-    "    \n",
-    "#     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    \n",
-    "    # Logic 1: Scale min,max value to 1,4 strictly\n",
-    "#     min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "#     min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "#     wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    \n",
-    "    # Logic 2: Scale abs value to 1,4 strictly\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * abs(p))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "    \n",
-    "#     print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "#     print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "#     print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    responseDict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['old_acc'] = accuracy_score(wordSim353AnnotDF_New['textOld'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    responseDict['new_acc'] = accuracy_score(wordSim353AnnotDF_New['textNew'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    \n",
-    "    responseDict['class_rep_old'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), output_dict=True)\n",
-    "    responseDict['class_rep_new'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), output_dict=True)\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    responseDict['cm_old'] = cm_old\n",
-    "    responseDict['cm_new'] = cm_new\n",
-    "    \n",
-    "    return responseDict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "premier-crisis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "confusionMatrixMaster = {}\n",
-    "neighDictMaster = {}\n",
-    "responsesDictMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "previous-applicant",
-   "metadata": {},
-   "source": [
-    "## Input Datasets Processing and Parsing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spoken-buying",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Text 7 props Embeddings\n",
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props'] = embedDict\n",
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "closing-biotechnology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Text 2 props Embeddings\n",
-    "textEmb_2props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_2props_DF['value'] = textEmb_2props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "embedDict = fetchEmbeddings(textEmb_2props_DF)\n",
-    "embedDictMaster['text_2props'] = embedDict\n",
-    "len(textEmb_2props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "oriented-brush",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Complex Embeddings\n",
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')\n",
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "absolute-brazil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['complex'] = normalize(compEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "transsexual-lender",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transe Embeddings\n",
-    "transeEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')\n",
-    "transeEmbeddingsDF['transe_embedding'] = transeEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "transeEmbeddings = {}\n",
-    "for _, row in transeEmbeddingsDF.iterrows():\n",
-    "    transeEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "printable-trinity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['transe'] = normalize(transeEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "federal-conservation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "#     print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "narrative-security",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "choice-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptions = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "quick-insulin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_of_nodes = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "objective-pendant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptions1 = descriptions[descriptions.node1.apply(lambda p: p in list_of_nodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "headed-cheese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "desc_dict = {row['node1']: row['node2'][1:-4] for _, row in descriptions1.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "mature-folder",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f13d01b193b1463fb947c4a1b0f71dcb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "426e07be03de401096d623a573192db0",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Abstract Text Embeddings\n",
-    "absEmbeddings = {}\n",
-    "for _, row in tqdm(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows(), total=len(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))):\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])\n",
-    "\n",
-    "# missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])\n",
-    "for _, row in tqdm(wordSim353AnnotDF_New.iterrows(), total=len(wordSim353AnnotDF_New)):\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        if row.word1_kg_id in desc_dict:\n",
-    "            absEmbeddings[row.word1_kg_id] = getSentEmbeddings(desc_dict[row.word1_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        if row.word2_kg_id in desc_dict:\n",
-    "            absEmbeddings[row.word2_kg_id] = getSentEmbeddings(desc_dict[row.word2_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "metropolitan-matrix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['abstract'] = normalize(absEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "fossil-provision",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8698306e64854387a145a6a9adae6d99",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8f2d993df1a48f7be6d59bd7bc73464",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/344 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Abstract First Sentence Embeddings\n",
-    "absFirstSentEmbeddings = {}\n",
-    "for _, row in tqdm(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows(), total=len(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))):\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absFirstSentEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])\n",
-    "\n",
-    "# missingWordSimFirstSentSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absFirstSentEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absFirstSentEmbeddings)])\n",
-    "for _, row in tqdm(wordSim353AnnotDF_New.iterrows(), total=len(wordSim353AnnotDF_New)):\n",
-    "    if row.word1_kg_id not in absFirstSentEmbeddings:\n",
-    "        if row.word1_kg_id in desc_dict:\n",
-    "            absFirstSentEmbeddings[row.word1_kg_id] = getSentEmbeddings(desc_dict[row.word1_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absFirstSentEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absFirstSentEmbeddings:\n",
-    "        if row.word2_kg_id in desc_dict:\n",
-    "            absFirstSentEmbeddings[row.word2_kg_id] = getSentEmbeddings(desc_dict[row.word2_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absFirstSentEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "transparent-czech",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['abstract_first_sent'] = normalize(absFirstSentEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "brazilian-wisdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "embedDictMaster['h_emb'] = json.load(open('../data/HAS_embeddings/H_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['a_emb'] = json.load(open('../data/HAS_embeddings/A_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['s_emb'] = json.load(open('../data/HAS_embeddings/S_embeddings_original_19k_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "appreciated-motor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "# Three Embeddings\n",
-    "embedThreeDictConcat = {}\n",
-    "tempThreeDictLens = defaultdict(int)\n",
-    "for key1 in ['complex', 'transe', \n",
-    "            'abstract_first_sent']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempThreeDictLens[key1] = max(tempThreeDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedThreeDictConcat:\n",
-    "            embedThreeDictConcat[word] = []\n",
-    "for word in embedThreeDictConcat.keys():\n",
-    "    for key1 in ['complex', 'transe', \n",
-    "            'abstract_first_sent']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedThreeDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedThreeDictConcat[word].append([0]*(tempThreeDictLens[key1]))\n",
-    "for word in embedThreeDictConcat.keys():\n",
-    "    embedThreeDictConcat[word] = [item for sublist in embedThreeDictConcat[word] for item in sublist]\n",
-    "# for word1 in list(embedThreeDictConcat.keys()):\n",
-    "#     if len(embedThreeDictConcat[word1]) != 968:\n",
-    "#         del embedThreeDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "conservative-fitting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['three_emb'] = normalize(embedThreeDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "digital-license",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Six Embeddings\n",
-    "embedSixDictConcat = {}\n",
-    "tempSixDictLens = defaultdict(int)\n",
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempSixDictLens[key1] = max(tempSixDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedSixDictConcat:\n",
-    "            embedSixDictConcat[word] = []\n",
-    "for word in embedSixDictConcat.keys():\n",
-    "    for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedSixDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedSixDictConcat[word].append([0]*(tempSixDictLens[key1]))\n",
-    "for word in embedSixDictConcat.keys():\n",
-    "    embedSixDictConcat[word] = [item for sublist in embedSixDictConcat[word] for item in sublist]\n",
-    "# for word1 in list(embedSixDictConcat.keys()):\n",
-    "#     if len(embedSixDictConcat[word1]) != 3784:\n",
-    "#         del embedSixDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "brazilian-prefix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['six_emb'] = normalize(embedSixDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "homeless-converter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Eight Embeddings\n",
-    "embedEightDictConcat = {}\n",
-    "tempEightDictLens = defaultdict(int)\n",
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent', 'h_emb', 's_emb']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempEightDictLens[key1] = max(tempEightDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedEightDictConcat:\n",
-    "            embedEightDictConcat[word] = []\n",
-    "\n",
-    "for word in embedEightDictConcat.keys():\n",
-    "    for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent', 'h_emb', 's_emb']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedEightDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedEightDictConcat[word].append([0]*(tempEightDictLens[key1]))\n",
-    "for word in embedEightDictConcat.keys():\n",
-    "    embedEightDictConcat[word] = [item for sublist in embedEightDictConcat[word] for item in sublist]\n",
-    "\n",
-    "# for word1 in list(embedEightDictConcat.keys()):\n",
-    "#     if len(embedEightDictConcat[word1]) != 3784:\n",
-    "#         del embedEightDictConcat[word1]\n",
-    "        \n",
-    "        \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "overhead-healthcare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['eight_emb'] = normalize(embedEightDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "reflected-finger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for word_key in embedDictMaster[key1].keys():\n",
-    "        if type(embedDictMaster[key1][word_key]) != list:\n",
-    "            embedDictMaster[key1][word_key] = embedDictMaster[key1][word_key].tolist()\n",
-    "    \n",
-    "    with open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json', 'w') as f:\n",
-    "        json.dump(embedDictMaster[key1], f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "premier-membership",
-   "metadata": {},
-   "source": [
-    "## Load input datasets from checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "strange-sussex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', 'abstract', 'abstract_first_sent', 'three_emb', 'six_emb', 'eight_emb']:\n",
-    "    embedDictMaster[key1] = json.load(open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json'))\n",
-    "embedDictMaster['h_emb'] = json.load(open('../data/HAS_embeddings/H_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['a_emb'] = json.load(open('../data/HAS_embeddings/A_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['s_emb'] = json.load(open('../data/HAS_embeddings/S_embeddings_original_19k_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "exclusive-principle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for key2 in embedDictMaster[key1].keys():\n",
-    "        embedDictMaster[key1][key2] = np.array(embedDictMaster[key1][key2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "innocent-allergy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embList = list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bound-produce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excessive-significance",
-   "metadata": {},
-   "source": [
-    "## Neighbours Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "guided-evans",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDictMaster['19k_2'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "neighDictMaster['probase'] = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "neighDictMaster['probase+19k_2'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "neighDictMaster['19k_childPar'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text]))\n",
-    "neighDictMaster['19k_siblings'] = fetchNeighbours(pd.concat([p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "neighDictMaster['19k_class_childPar'] = fetchNeighbours(classSimTargetChildPar)\n",
-    "neighDictMaster['19k_class_siblings'] = fetchNeighbours(classSimTargetSiblings)\n",
-    "neighDictMaster['19k_2_class'] = fetchNeighbours(pd.concat([classSimTargetChildPar, classSimTargetSiblings]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "burning-decade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basisList = list(neighDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "increasing-survival",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incorporate-ecuador",
-   "metadata": {},
-   "source": [
-    "## Retrofitting using combination of input and basis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "studied-providence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "neighDictMasterBKP = copy.deepcopy(neighDictMaster)\n",
-    "embedDictMasterBKP = copy.deepcopy(embedDictMaster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prepared-culture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import copy\n",
-    "# neighDictMaster = copy.deepcopy(neighDictMasterBKP)\n",
-    "# embedDictMaster = copy.deepcopy(embedDictMasterBKP)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assisted-virtue",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "results = []\n",
-    "NUM_ITERS = 10\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            groupResults = []\n",
-    "            for weightCase in [1,2]:\n",
-    "                embedDict = embedDictMaster[emb]\n",
-    "                if weightedNess:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                else:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                for iterNum in range(1,NUM_ITERS+1):\n",
-    "                    newEmbedDict = retrofit(embedDict, neighDictMaster[basis], weightCase, weightedNess)\n",
-    "        #             dists = determineDistances(embedDict, newEmbedDict)\n",
-    "                    responsesDict = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        #                 print(responsesDict.keys())\n",
-    "                    groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                    responsesDict['old_acc']*100, \\\n",
-    "                                    responsesDict['new_acc']*100, \\\n",
-    "                                    (responsesDict['new_acc'] - responsesDict['old_acc'])*100, \\\n",
-    "                                    responsesDict['coveredPairs'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                    ])\n",
-    "                    embedDict = newEmbedDict\n",
-    "\n",
-    "                newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                responsesDictMaster[caseName] = responsesDict\n",
-    "            for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "                results.append(gR+[rank])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "super-engine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "strong-chamber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'Rank'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "opening-thomson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Embedding</th>\n",
-       "      <th>Basis</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Weightedness</th>\n",
-       "      <th>Old Acc</th>\n",
-       "      <th>New Acc</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>Pairs Covered</th>\n",
-       "      <th>Old I Precision</th>\n",
-       "      <th>Old I Recall</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Old U Precision</th>\n",
-       "      <th>Old U Recall</th>\n",
-       "      <th>Old U F1-Score</th>\n",
-       "      <th>New I Precision</th>\n",
-       "      <th>New I Recall</th>\n",
-       "      <th>New I F1-Score</th>\n",
-       "      <th>New U Precision</th>\n",
-       "      <th>New U Recall</th>\n",
-       "      <th>New U F1-Score</th>\n",
-       "      <th>Rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>s_emb</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>23.546512</td>\n",
-       "      <td>194</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.32069</td>\n",
-       "      <td>0.902913</td>\n",
-       "      <td>0.473282</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.366667</td>\n",
-       "      <td>0.106796</td>\n",
-       "      <td>0.165414</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>s_emb</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>23.255814</td>\n",
-       "      <td>194</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.32069</td>\n",
-       "      <td>0.902913</td>\n",
-       "      <td>0.473282</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.366667</td>\n",
-       "      <td>0.106796</td>\n",
-       "      <td>0.165414</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>184</th>\n",
-       "      <td>s_emb</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>23.255814</td>\n",
-       "      <td>194</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.32069</td>\n",
-       "      <td>0.902913</td>\n",
-       "      <td>0.473282</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.366667</td>\n",
-       "      <td>0.106796</td>\n",
-       "      <td>0.165414</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>s_emb</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>23.255814</td>\n",
-       "      <td>194</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.32069</td>\n",
-       "      <td>0.902913</td>\n",
-       "      <td>0.473282</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.366667</td>\n",
-       "      <td>0.106796</td>\n",
-       "      <td>0.165414</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>s_emb</td>\n",
-       "      <td>probase+19k_2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>58.430233</td>\n",
-       "      <td>19.476744</td>\n",
-       "      <td>194</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.32069</td>\n",
-       "      <td>0.902913</td>\n",
-       "      <td>0.473282</td>\n",
-       "      <td>0.157895</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.206897</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>text_2props</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>-9.883721</td>\n",
-       "      <td>326</td>\n",
-       "      <td>0.292683</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.009709</td>\n",
-       "      <td>0.019231</td>\n",
-       "      <td>0.178571</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.288462</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>text_7props</td>\n",
-       "      <td>probase+19k_2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>40.988372</td>\n",
-       "      <td>-19.476744</td>\n",
-       "      <td>326</td>\n",
-       "      <td>0.325000</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.009709</td>\n",
-       "      <td>0.019231</td>\n",
-       "      <td>0.124031</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.214765</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>text_2props</td>\n",
-       "      <td>probase+19k_2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>40.406977</td>\n",
-       "      <td>-19.476744</td>\n",
-       "      <td>326</td>\n",
-       "      <td>0.292683</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.009709</td>\n",
-       "      <td>0.019231</td>\n",
-       "      <td>0.119403</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.207792</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>text_7props</td>\n",
-       "      <td>probase</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>38.953488</td>\n",
-       "      <td>-21.511628</td>\n",
-       "      <td>326</td>\n",
-       "      <td>0.325000</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.009709</td>\n",
-       "      <td>0.019231</td>\n",
-       "      <td>0.121429</td>\n",
-       "      <td>0.85</td>\n",
-       "      <td>0.212500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>text_2props</td>\n",
-       "      <td>probase</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>36.627907</td>\n",
-       "      <td>-23.255814</td>\n",
-       "      <td>326</td>\n",
-       "      <td>0.292683</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.009709</td>\n",
-       "      <td>0.019231</td>\n",
-       "      <td>0.107383</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.189349</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>192 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Embedding               Basis  Weight  Weightedness    Old Acc  \\\n",
-       "16         s_emb               19k_2       1          True  38.953488   \n",
-       "136        s_emb  19k_class_childPar       1          True  38.953488   \n",
-       "184        s_emb         19k_2_class       1          True  38.953488   \n",
-       "88         s_emb        19k_childPar       1          True  38.953488   \n",
-       "64         s_emb       probase+19k_2       1          True  38.953488   \n",
-       "..           ...                 ...     ...           ...        ...   \n",
-       "170  text_2props         19k_2_class       1          True  59.883721   \n",
-       "48   text_7props       probase+19k_2       1          True  60.465116   \n",
-       "50   text_2props       probase+19k_2       1          True  59.883721   \n",
-       "24   text_7props             probase       1          True  60.465116   \n",
-       "26   text_2props             probase       1          True  59.883721   \n",
-       "\n",
-       "       New Acc   Increase  Pairs Covered  Old I Precision  Old I Recall  ...  \\\n",
-       "16   62.500000  23.546512            194         0.857143          0.30  ...   \n",
-       "136  62.209302  23.255814            194         0.857143          0.30  ...   \n",
-       "184  62.209302  23.255814            194         0.857143          0.30  ...   \n",
-       "88   62.209302  23.255814            194         0.857143          0.30  ...   \n",
-       "64   58.430233  19.476744            194         0.857143          0.30  ...   \n",
-       "..         ...        ...            ...              ...           ...  ...   \n",
-       "170  50.000000  -9.883721            326         0.292683          0.60  ...   \n",
-       "48   40.988372 -19.476744            326         0.325000          0.65  ...   \n",
-       "50   40.406977 -19.476744            326         0.292683          0.60  ...   \n",
-       "24   38.953488 -21.511628            326         0.325000          0.65  ...   \n",
-       "26   36.627907 -23.255814            326         0.292683          0.60  ...   \n",
-       "\n",
-       "     Old U Precision  Old U Recall  Old U F1-Score  New I Precision  \\\n",
-       "16           0.32069      0.902913        0.473282         0.533333   \n",
-       "136          0.32069      0.902913        0.473282         0.500000   \n",
-       "184          0.32069      0.902913        0.473282         0.500000   \n",
-       "88           0.32069      0.902913        0.473282         0.500000   \n",
-       "64           0.32069      0.902913        0.473282         0.157895   \n",
-       "..               ...           ...             ...              ...   \n",
-       "170          1.00000      0.009709        0.019231         0.178571   \n",
-       "48           1.00000      0.009709        0.019231         0.124031   \n",
-       "50           1.00000      0.009709        0.019231         0.119403   \n",
-       "24           1.00000      0.009709        0.019231         0.121429   \n",
-       "26           1.00000      0.009709        0.019231         0.107383   \n",
-       "\n",
-       "     New I Recall  New I F1-Score  New U Precision  New U Recall  \\\n",
-       "16           0.40        0.457143         0.366667      0.106796   \n",
-       "136          0.40        0.444444         0.366667      0.106796   \n",
-       "184          0.40        0.444444         0.366667      0.106796   \n",
-       "88           0.40        0.444444         0.366667      0.106796   \n",
-       "64           0.30        0.206897         0.000000      0.000000   \n",
-       "..            ...             ...              ...           ...   \n",
-       "170          0.75        0.288462         0.000000      0.000000   \n",
-       "48           0.80        0.214765         0.000000      0.000000   \n",
-       "50           0.80        0.207792         0.000000      0.000000   \n",
-       "24           0.85        0.212500         0.000000      0.000000   \n",
-       "26           0.80        0.189349         0.000000      0.000000   \n",
-       "\n",
-       "     New U F1-Score  Rank  \n",
-       "16         0.165414     0  \n",
-       "136        0.165414     0  \n",
-       "184        0.165414     0  \n",
-       "88         0.165414     0  \n",
-       "64         0.000000     0  \n",
-       "..              ...   ...  \n",
-       "170        0.000000     1  \n",
-       "48         0.000000     1  \n",
-       "50         0.000000     1  \n",
-       "24         0.000000     1  \n",
-       "26         0.000000     1  \n",
-       "\n",
-       "[192 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resultsDF.sort_values(by=['Increase'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "reasonable-occasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.to_csv('../data/retrofitting/retro_Aug16_2021.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "strong-singer",
-   "metadata": {},
-   "source": [
-    "## Analyze Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "id": "baking-ranking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def addColumnToGroundTruth(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "    min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     assert wordSim353AnnotDF_New['textOld'].min() >= 0, wordSim353AnnotDF_New['textOld'].min()\n",
-    "#     assert wordSim353AnnotDF_New['textNew'].min() >= 0, wordSim353AnnotDF_New['textNew'].min()\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    wordSim353AnnotDF_New['old_class'] = wordSim353AnnotDF_New['textOld'].apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['new_class'] = wordSim353AnnotDF_New['textNew'].apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['retro_worked'] = ((wordSim353AnnotDF_New['new_class'] == wordSim353AnnotDF_New['category']) & (wordSim353AnnotDF_New['old_class'] != wordSim353AnnotDF_New['category']))\n",
-    "    return wordSim353AnnotDF_New\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "id": "fatty-secretary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = addColumnToGroundTruth(embedDictMaster['three_emb'], newEmbedDictMaster['three_emb_19k_2_class_1_weighted'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "id": "antique-moscow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = addColumnToGroundTruth(embedDictMaster['six_emb'], newEmbedDictMaster['six_emb_19k_2_class_1_weighted'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "id": "defined-sense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>category</th>\n",
-       "      <th>textOld</th>\n",
-       "      <th>textNew</th>\n",
-       "      <th>old_class</th>\n",
-       "      <th>new_class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.260638</td>\n",
-       "      <td>3.557514</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q5369</td>\n",
-       "      <td>baseball</td>\n",
-       "      <td>Q1539532</td>\n",
-       "      <td>season</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.584545</td>\n",
-       "      <td>3.246840</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Q622821</td>\n",
-       "      <td>calculation</td>\n",
-       "      <td>Q12525525</td>\n",
-       "      <td>computation</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.173112</td>\n",
-       "      <td>1.663656</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Q578</td>\n",
-       "      <td>century</td>\n",
-       "      <td>Q6266</td>\n",
-       "      <td>nation</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.455742</td>\n",
-       "      <td>3.616025</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.925905</td>\n",
-       "      <td>1.667819</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Q6881511</td>\n",
-       "      <td>company</td>\n",
-       "      <td>Q1196314</td>\n",
-       "      <td>stock</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.508146</td>\n",
-       "      <td>3.224211</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>food</td>\n",
-       "      <td>Q2216236</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.828730</td>\n",
-       "      <td>3.393004</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133</th>\n",
-       "      <td>Q11410</td>\n",
-       "      <td>game</td>\n",
-       "      <td>Q11499267</td>\n",
-       "      <td>defeat</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.699801</td>\n",
-       "      <td>3.245156</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>Q798505</td>\n",
-       "      <td>listing</td>\n",
-       "      <td>Q21146257</td>\n",
-       "      <td>category</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.554506</td>\n",
-       "      <td>3.300242</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>Q61864890</td>\n",
-       "      <td>lobster</td>\n",
-       "      <td>Q282</td>\n",
-       "      <td>wine</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.534090</td>\n",
-       "      <td>3.345835</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190</th>\n",
-       "      <td>Q1368</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Q693464</td>\n",
-       "      <td>cash</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.914554</td>\n",
-       "      <td>1.497877</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>Q132821</td>\n",
-       "      <td>murder</td>\n",
-       "      <td>Q3882220</td>\n",
-       "      <td>manslaughter</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.023082</td>\n",
-       "      <td>1.646781</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>227</th>\n",
-       "      <td>Q17285</td>\n",
-       "      <td>plane</td>\n",
-       "      <td>Q1420</td>\n",
-       "      <td>car</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.646583</td>\n",
-       "      <td>3.394859</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>Q529711</td>\n",
-       "      <td>start</td>\n",
-       "      <td>Q577</td>\n",
-       "      <td>year</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.530899</td>\n",
-       "      <td>3.088973</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>Q79007</td>\n",
-       "      <td>street</td>\n",
-       "      <td>Q7543083</td>\n",
-       "      <td>avenue</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.513824</td>\n",
-       "      <td>1.561554</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    word1_kg_id       Word 1 word2_kg_id        Word 2  Avg category  \\\n",
-       "2         Q8333          FBI     Q178022   fingerprint  3.6        U   \n",
-       "28        Q5369     baseball    Q1539532        season  3.2        M   \n",
-       "41      Q622821  calculation   Q12525525   computation  1.0        I   \n",
-       "47         Q578      century       Q6266        nation  4.0        U   \n",
-       "56       Q93352        coast     Q468756         shore  1.6        I   \n",
-       "57     Q6881511      company    Q1196314         stock  3.0        M   \n",
-       "126       Q2095         food    Q2216236       rooster  3.2        M   \n",
-       "133      Q11410         game   Q11499267        defeat  3.0        M   \n",
-       "170     Q798505      listing   Q21146257      category  3.2        M   \n",
-       "173   Q61864890      lobster        Q282          wine  3.2        M   \n",
-       "190       Q1368        money     Q693464          cash  1.0        I   \n",
-       "209     Q132821       murder    Q3882220  manslaughter  1.6        I   \n",
-       "227      Q17285        plane       Q1420           car  2.6        M   \n",
-       "295     Q529711        start        Q577          year  3.4        M   \n",
-       "303      Q79007       street    Q7543083        avenue  1.4        I   \n",
-       "\n",
-       "      textOld   textNew old_class new_class  \n",
-       "2    3.260638  3.557514         M         U  \n",
-       "28   3.584545  3.246840         U         M  \n",
-       "41   2.173112  1.663656         M         I  \n",
-       "47   3.455742  3.616025         M         U  \n",
-       "56   1.925905  1.667819         M         I  \n",
-       "57   3.508146  3.224211         U         M  \n",
-       "126  3.828730  3.393004         U         M  \n",
-       "133  3.699801  3.245156         U         M  \n",
-       "170  3.554506  3.300242         U         M  \n",
-       "173  3.534090  3.345835         U         M  \n",
-       "190  1.914554  1.497877         M         I  \n",
-       "209  2.023082  1.646781         M         I  \n",
-       "227  3.646583  3.394859         U         M  \n",
-       "295  3.530899  3.088973         U         M  \n",
-       "303  2.513824  1.561554         M         I  "
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp1[temp1['retro_worked']][['word1_kg_id', 'Word 1', 'word2_kg_id', 'Word 2', 'Avg', 'category', 'textOld', 'textNew', 'old_class','new_class']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "renewable-stylus",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "leading-frontier",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "extensive-forty",
-   "metadata": {},
-   "source": [
-    "## Confusion Matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "maritime-director",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusionMatrixMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qualified-empty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = embedDictMaster['text_7props_19k']\n",
-    "newEmbedDict = newEmbedDictMaster['text_7props_19k_2']\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "\n",
-    "print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "distinct-timothy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    210\n",
-       "U    101\n",
-       "I     20\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['category'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "antique-infrastructure",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(figsize=(20,20))\n",
-    "for num, key1 in enumerate(['text_7props_19k_old_2', 'text_7props_19k_new_2', 'text_2props_19k_old_2', 'text_2props_19k_new_2', \n",
-    "             'transe_19k_old_1', 'transe_19k_new_1', 'complex_19k_old_1', 'complex_19k_new_1']):\n",
-    "    plt.subplot(4, 2, num+1)\n",
-    "    sns.heatmap(pd.DataFrame(confusionMatrixMaster[key1], LABELS, LABELS), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "micro-phrase",
-   "metadata": {},
-   "source": [
-    "# Save all embeddings and similarity scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "boring-campus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster['concat_probase_19k_v1_2'] = newEmbedDictMaster['concat_probase_19k__v1_2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "automatic-update",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d97162bbb58542999eb317ede34a3300",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "\n",
-    "for key1, key2 in tqdm(emb_pairs):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "related-opposition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "allied-sunrise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004515859562266559, -0.011723668837741234,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.384407</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.0050116109222357935, -0.013383541509188317...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004971298302113362, -0.013254267455943004,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.463734</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004862257036458548, 0.014077170447753274, ...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.005805280378137928, 0.015115419540555773, ...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004862255010517295, 0.01407716458226321, 0...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.01523113313292473, 0.005271417432183582, -0...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.850218</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013922101038066187, 0.0023911841962956006, ...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.01381456413075283, 0.0024352598782685863, -...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.895993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.013016090555331788, 0.01114268765690899, -0...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.962306</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013274960537868754, 0.010965634834463656, -...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013242882478240212, 0.010953106140234146, -...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.993596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626153814759, 0.005047111609608307, -0...</td>\n",
-       "      <td>[0.010410829355084429, 0.014776997941327313, -...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "1  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "2  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "3  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "4  ...  [0.01322626153814759, 0.005047111609608307, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859562266559, -0.011723668837741234,...                 3.468370   \n",
-       "1  [-0.004862257036458548, 0.014077170447753274, ...                 3.035534   \n",
-       "2  [0.01523113313292473, 0.005271417432183582, -0...                 2.902356   \n",
-       "3  [0.013016090555331788, 0.01114268765690899, -0...                 2.995206   \n",
-       "4  [0.010410829355084429, 0.014776997941327313, -...                 1.689315   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.384407   \n",
-       "1                    2.991152   \n",
-       "2                    2.850218   \n",
-       "3                    2.962306   \n",
-       "4                    1.689315   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.0050116109222357935, -0.013383541509188317...   \n",
-       "1  [-0.005805280378137928, 0.015115419540555773, ...   \n",
-       "2  [0.013922101038066187, 0.0023911841962956006, ...   \n",
-       "3  [0.013274960537868754, 0.010965634834463656, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.004971298302113362, -0.013254267455943004,...   \n",
-       "1  [-0.004862255010517295, 0.01407716458226321, 0...   \n",
-       "2  [0.01381456413075283, 0.0024352598782685863, -...   \n",
-       "3  [0.013242882478240212, 0.010953106140234146, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.468370                           3.463734  \n",
-       "1                         3.035534                           2.991152  \n",
-       "2                         2.902356                           2.895993  \n",
-       "3                         2.995206                           2.993596  \n",
-       "4                         1.689315                           1.689315  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "connected-limit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cons_ann_3 = pd.read_csv('../data/consolidated_annotation_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "id": "trying-authorization",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tool</td>\n",
-       "      <td>implement</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>5.313</td>\n",
-       "      <td>4.688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>arrangement</td>\n",
-       "      <td>accommodation</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.304</td>\n",
-       "      <td>5.125</td>\n",
-       "      <td>4.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>l</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>7.938</td>\n",
-       "      <td>2.063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>benchmark</td>\n",
-       "      <td>index</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>5.500</td>\n",
-       "      <td>4.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>practice</td>\n",
-       "      <td>institution</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L,I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.447</td>\n",
-       "      <td>3.563</td>\n",
-       "      <td>6.438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Word 1         Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0         tool      implement      1   NaN      3     U      1   NaN      2   \n",
-       "1  arrangement  accommodation      1   NaN      4   NaN      2     I      1   \n",
-       "2         wood         forest      3     H      1   NaN      1   NaN      1   \n",
-       "3    benchmark          index      2     I      2     I      2     I      4   \n",
-       "4     practice    institution      2     I      3   L,I      3     I      3   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg  Stdev  H_orig  H_reversed  \n",
-       "0     i      1   NaN  1.6  0.894   5.313       4.688  \n",
-       "1   NaN      1   NaN  1.8  1.304   5.125       4.875  \n",
-       "2   NaN      3     l  1.8  1.095   7.938       2.063  \n",
-       "3   NaN      4   NaN  2.8  1.095   5.500       4.500  \n",
-       "4     i      3     i  2.8  0.447   3.563       6.438  "
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cons_ann_3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "id": "political-laptop",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(cons_ann_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "id": "improved-relaxation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ca_cols = cons_ann_3.columns\n",
-    "for _,row in cons_ann_3.iterrows():\n",
-    "#     if ((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum() == 2:\n",
-    "#         print(row['Word 1'], row['Word 2'])\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2']))])\n",
-    "#     print (((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum())\n",
-    "    for col in ca_cols:\n",
-    "        if col in ['Word 1', 'Word 2']:\n",
-    "            continue\n",
-    "        \n",
-    "        wordSim353AnnotDF_New_Merged_DF.at[wordSim353AnnotDF_New_Merged_DF.index[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])], col] = row[col]\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])][col])\n",
-    "#     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "id": "relevant-dodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[~wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2']].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outside-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "pretty-scholar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "gorgeous-syndrome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'ID',\n",
-    " 'H_Sim',\n",
-    " 'H_Dim',\n",
-    " 'F_Sim',\n",
-    " 'F_Dim',\n",
-    " 'N_Sim',\n",
-    " 'N_Dim',\n",
-    " 'D_Sim',\n",
-    " 'D_Dim',\n",
-    " 'P_Sim',\n",
-    " 'P_Dim',\n",
-    " 'Avg',\n",
-    " 'Stdev',\n",
-    " 'H_orig',\n",
-    " 'H_reversed',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].to_csv('../data/wordsim353_all_embeddings_with_retrofits_scores_only_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ahead-syndication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "racial-behalf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.580594\n",
-       "std        0.457960\n",
-       "min        0.000000\n",
-       "25%        0.222619\n",
-       "50%        0.495179\n",
-       "75%        0.852326\n",
-       "max        5.000000\n",
-       "Name: transe_19k_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_old_cosSim_Diff'],ascending=False)['transe_19k_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.645644\n",
-       "std        0.526932\n",
-       "min        0.000000\n",
-       "25%        0.250461\n",
-       "50%        0.562808\n",
-       "75%        0.927608\n",
-       "max        5.000000\n",
-       "Name: transe_19k_1_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_1_new_cosSim_Diff'],ascending=False)['transe_19k_1_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.495179:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.562808:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'transe_19k_old_cosSim', 'transe_19k_1_new_cosSim', 'transe_19k_old_cosSim_Diff', 'transe_19k_1_new_cosSim_Diff', 'transe_19k_old_cosSim_Diff_Cat', 'transe_19k_1_new_cosSim_Diff_Cat']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff_Cat</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>0.523697</td>\n",
-       "      <td>0.122335</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>0.496332</td>\n",
-       "      <td>0.647819</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>0.688666</td>\n",
-       "      <td>0.473791</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>0.118237</td>\n",
-       "      <td>0.056250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>0.064958</td>\n",
-       "      <td>0.119978</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.807539</td>\n",
-       "      <td>3.161937</td>\n",
-       "      <td>0.192461</td>\n",
-       "      <td>0.161937</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.310261</td>\n",
-       "      <td>2.793832</td>\n",
-       "      <td>0.489739</td>\n",
-       "      <td>1.006168</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.449602</td>\n",
-       "      <td>2.739280</td>\n",
-       "      <td>0.550398</td>\n",
-       "      <td>0.260720</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.931610</td>\n",
-       "      <td>3.541754</td>\n",
-       "      <td>1.131610</td>\n",
-       "      <td>1.741754</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.648427</td>\n",
-       "      <td>2.758156</td>\n",
-       "      <td>1.101573</td>\n",
-       "      <td>0.991844</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  transe_19k_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60               3.076303   \n",
-       "1     Arafat         terror  3.60               3.103668   \n",
-       "2        FBI    fingerprint  3.60               2.911334   \n",
-       "3        FBI  investigation  3.00               2.881763   \n",
-       "4    Harvard           Yale  2.20               2.135042   \n",
-       "..       ...            ...   ...                    ...   \n",
-       "339      war         troops  3.00               2.807539   \n",
-       "340   weapon         secret  3.80               3.310261   \n",
-       "341  weather       forecast  3.00               2.449602   \n",
-       "342     wood         forest  1.80               2.931610   \n",
-       "343     word     similarity  3.75               2.648427   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim  transe_19k_old_cosSim_Diff  \\\n",
-       "0                   3.477665                    0.523697   \n",
-       "1                   2.952181                    0.496332   \n",
-       "2                   3.126209                    0.688666   \n",
-       "3                   2.943750                    0.118237   \n",
-       "4                   2.080022                    0.064958   \n",
-       "..                       ...                         ...   \n",
-       "339                 3.161937                    0.192461   \n",
-       "340                 2.793832                    0.489739   \n",
-       "341                 2.739280                    0.550398   \n",
-       "342                 3.541754                    1.131610   \n",
-       "343                 2.758156                    1.101573   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff  transe_19k_old_cosSim_Diff_Cat  \\\n",
-       "0                        0.122335                               1   \n",
-       "1                        0.647819                               1   \n",
-       "2                        0.473791                               1   \n",
-       "3                        0.056250                               0   \n",
-       "4                        0.119978                               0   \n",
-       "..                            ...                             ...   \n",
-       "339                      0.161937                               0   \n",
-       "340                      1.006168                               0   \n",
-       "341                      0.260720                               1   \n",
-       "342                      1.741754                               1   \n",
-       "343                      0.991844                               1   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff_Cat  \n",
-       "0                                   0  \n",
-       "1                                   1  \n",
-       "2                                   0  \n",
-       "3                                   0  \n",
-       "4                                   0  \n",
-       "..                                ...  \n",
-       "339                                 0  \n",
-       "340                                 1  \n",
-       "341                                 0  \n",
-       "342                                 1  \n",
-       "343                                 1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "compressed-emergency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['old_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "alert-holmes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['new_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "psychological-walter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['cat'] = wordSim353AnnotDF_New_Merged_DF['Avg'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badBefore.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badAfter.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consistent-difference",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "worldwide-genius",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "developing-option",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "catholic-compensation",
-   "metadata": {},
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
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-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
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-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
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-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "expanded-uniform",
-   "metadata": {},
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
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-       "      <th>complex_19k_2_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>abstract_19k_old_cosSim</th>\n",
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-       "      <th>...</th>\n",
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-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "indian-embassy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "vocational-nurse",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[col].apply(labelSamples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(labelSamples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
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-       "12350 -1.470588  8      \n",
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-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
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-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
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-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
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-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
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-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
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-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
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-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
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-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
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-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
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-       "1035  2.613445  4      \n",
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-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
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-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
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-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
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-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
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-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
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-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
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-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
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-     },
-     "execution_count": 259,
-     "metadata": {},
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-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>7</td>\n",
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-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
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-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
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-     "metadata": {},
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-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
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-   "execution_count": 261,
-   "id": "binary-committee",
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-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Scratchpad Nbook-checkpoint.ipynb b/.ipynb_checkpoints/Scratchpad Nbook-checkpoint.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/.ipynb_checkpoints/Scratchpad Nbook-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/TextEmbed UnitTest-checkpoint.ipynb b/.ipynb_checkpoints/TextEmbed UnitTest-checkpoint.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/.ipynb_checkpoints/TextEmbed UnitTest-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Wikidata CS Exploration-checkpoint.ipynb b/.ipynb_checkpoints/Wikidata CS Exploration-checkpoint.ipynb
deleted file mode 100644
index e05d231..0000000
--- a/.ipynb_checkpoints/Wikidata CS Exploration-checkpoint.ipynb	
+++ /dev/null
@@ -1,10354 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 339,
-   "id": "familiar-forest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "indonesian-mambo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs-20200504.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "distributed-spice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1449</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>P2561</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>name</td>\n",
-       "      <td>subproperty of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1449</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>Q49614</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>subject item of this property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1547</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>Q56859575</td>\n",
-       "      <td>depends on software</td>\n",
-       "      <td>software dependency</td>\n",
-       "      <td>subject item of this property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1592</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>P1593</td>\n",
-       "      <td>prosecutor</td>\n",
-       "      <td>defender</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1593</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>P1592</td>\n",
-       "      <td>defender</td>\n",
-       "      <td>prosecutor</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node1      relation      node2          node1;label          node2;label  \\\n",
-       "0  P1449        /r/IsA      P2561             nickname                 name   \n",
-       "1  P1449  /r/RelatedTo     Q49614             nickname             nickname   \n",
-       "2  P1547  /r/RelatedTo  Q56859575  depends on software  software dependency   \n",
-       "3  P1592  /r/RelatedTo      P1593           prosecutor             defender   \n",
-       "4  P1593  /r/RelatedTo      P1592             defender           prosecutor   \n",
-       "\n",
-       "                  relation;label  relation;dimension source  sentence  \n",
-       "0                 subproperty of                 NaN     WD       NaN  \n",
-       "1  subject item of this property                 NaN     WD       NaN  \n",
-       "2  subject item of this property                 NaN     WD       NaN  \n",
-       "3                       see also                 NaN     WD       NaN  \n",
-       "4                       see also                 NaN     WD       NaN  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "civilian-induction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimSet = list(set(wikiDF.node1.to_list() + wikiDF.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "peripheral-alignment",
-   "metadata": {},
-   "source": [
-    "# Fetch Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-magnet",
-   "metadata": {},
-   "source": [
-    "## Complex + Transe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "advisory-tablet",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64865436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74433572\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047190\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47708201\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16939336\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75815335\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638383\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74201282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29352977\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047182\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61948580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57636639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636435\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10384086\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18384500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618753\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49553430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56003279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7257714\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21993815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84288054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5102\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78849858\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23699967\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3730867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4819858\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45813532\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38456828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036634\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48667794\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2842797\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P8047\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36249200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84266649\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21119540\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47452597\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85509189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611037\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35848856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51917600\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37761983\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12860009\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36258590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746648\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77667037\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61314006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681853\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852604\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60669731\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625811\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734414\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49689498\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35588820\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61466331\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1208509\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56478376\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44687559\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3514338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89680726\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19990790\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29543009\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032274\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70704507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032617\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20026782\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50332811\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60621802\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10520944\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84757986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29578707\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5589518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12600952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46785334\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17158079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34768712\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42517203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3783318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50350470\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58902427\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22269594\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18603649\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50902564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61798329\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5460182\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53556002\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5328\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59383129\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61090213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10340288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17997011\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3411290\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24632433\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q805333\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87977249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61067884\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5502339\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2404985\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59475075\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2140654\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80432189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59211677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77667126\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q140702\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915160\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46256234\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56977400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59282004\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34200888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7442\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73863282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21162274\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59044212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71872469\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6720214\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047187\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58997978\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11194929\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60941978\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56021177\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64905317\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79679221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2695\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37286659\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58979956\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11497457\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965916\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52399926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58603740\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77340449\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51881207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17164790\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58998468\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2995983\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667095\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680038\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56030389\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20975596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851517\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84271640\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585136\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71470174\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17231698\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68065658\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585141\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5901\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36536067\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38069463\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038346\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60431603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1413969\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58647874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5280\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91515771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53496919\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59079146\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851567\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3746141\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56681204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78097957\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71283344\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73865001\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62404518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25504044\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6570\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58143241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88178923\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29306015\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59078556\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84298834\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87938350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2319440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1538854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59475077\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45799055\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45266300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72283908\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89184820\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3930265\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60723856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25380544\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29545850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57604775\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705289\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915169\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62585847\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56114341\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59033819\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090823\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24899199\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60547335\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29037088\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85715771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44294891\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693095\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12796995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46830821\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28008313\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769133\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681531\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1720112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667270\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43473459\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41835716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38506410\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58612101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36734639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78090690\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58240553\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21002290\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038547\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708672\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92011183\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16631246\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37671364\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64810046\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73310208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91702724\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734597\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047179\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78048467\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2366980\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2184\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59450702\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734473\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002263\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42790808\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072416\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5477678\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16937917\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72342517\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49585733\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58179448\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5028\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78090598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69361740\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51880054\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25507913\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41222313\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58487888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30893837\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73572813\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15935592\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21597741\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10270022\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89680721\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29644036\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12050975\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59005418\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10885494\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59406506\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55923238\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18286698\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P968\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73366170\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56270098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90132359\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74435781\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681565\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63395686\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63886763\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68582414\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57470203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12301679\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30156437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57398400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24962761\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681838\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54070999\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4818169\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11902783\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75838596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47772347\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67141864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10380381\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89350258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7283951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29819756\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37862578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30047267\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6571\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625581\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47226657\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10934814\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58966165\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74474288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6422240\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47576960\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746662\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52171487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6710\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58813489\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68731010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60363191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58043865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87885144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692762\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87169767\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71898438\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769097\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90865542\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1327189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72492945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77893681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73041322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627072\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980469\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55312002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35922832\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53911696\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11739050\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18119757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88009688\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14405929\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10546472\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93003322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77556661\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45763191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765317\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35760525\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87937643\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89223871\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74217532\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50454993\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18553215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29513828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56334790\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57657179\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42157065\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45945663\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58966208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78029495\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11379779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61699843\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68848439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5991\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56019945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71916144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6534\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59076817\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3710530\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46384387\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61249011\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693069\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71802787\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667303\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78967694\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30921616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41909633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17123440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59628350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2506578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681598\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84242091\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47122316\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55951022\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5706\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2361\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90916278\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852606\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57287735\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6427\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618755\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q9392141\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57407542\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6731581\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38907749\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67055629\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2535710\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40287877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58926227\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37415888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60357352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69368835\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59973208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29585699\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246614\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59915587\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60962156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78553203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q874632\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66525126\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61986723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1552\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57535936\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5307082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87070600\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54290607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7725\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6977\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681949\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q26700386\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33474600\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46921451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62399025\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980687\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5832\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12308638\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6605486\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65096040\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58257544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54469554\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62555519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36163707\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15312278\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66562936\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006052\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40423311\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36597458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30082251\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53009238\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091694\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90476672\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980351\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56641102\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74435148\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627074\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681813\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27500609\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84861637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59043198\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56972008\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59289286\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44705203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56593453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55729353\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78086915\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77088052\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75755497\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51969935\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41359681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6424\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21198084\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38964612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56158192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38887569\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2535605\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29963683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64864078\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30914189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48277039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90992880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1546\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55829848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58979822\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74477506\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60550474\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84274233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769137\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56672312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57612933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21014021\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64374415\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705292\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73375715\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74447306\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35219907\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708709\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74113345\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2579\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90117379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48930682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20980701\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q332209\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734421\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12593257\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33545318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84479168\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006766\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039381\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43787914\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61311233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734481\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56639043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667775\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25633436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61534824\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36526633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58772354\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693077\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42293501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734613\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7532\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37782758\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56030423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68406538\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56866696\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58079010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59002045\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70996933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1504425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39433287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57206946\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62101992\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45813280\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047183\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070275\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55632487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965337\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56604579\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28599815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5895\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55280602\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59261899\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030201\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59077205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49617371\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66556076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35033263\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52294396\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5920\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16068686\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5899\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53204287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61249025\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57437234\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56429662\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59066517\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87913720\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6533\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41845366\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37056861\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5295538\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41226557\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23923844\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58626642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36967806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60066070\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746809\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2729443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49137994\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60537443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38554054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73298134\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032333\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13415408\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64995852\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10605247\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72376329\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83133269\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58993316\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57454176\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55091104\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42365814\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73420497\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27334467\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59702429\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28026076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34624535\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73420767\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24241569\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51876193\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84274956\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58244802\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11302682\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19915274\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50869987\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41673723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18014941\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16023742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708534\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36536082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12041905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68287507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88524690\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18059293\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13164480\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73010357\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60723552\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58926708\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667515\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59406504\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89680732\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3625632\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39210110\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047186\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55968809\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64006842\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39974398\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705297\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60481617\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83365205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84276764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091067\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12016773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75224630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5994\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75798762\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34307362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33947320\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87282988\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89184453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56639568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82452792\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63041650\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73797856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68861473\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29789760\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75800566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12593409\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27437064\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275833\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58988215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91901288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59005988\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21082583\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33339426\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741273\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38023488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60059934\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84273997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61877212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59043484\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29035123\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852617\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57579202\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66425262\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84481119\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56430961\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708707\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1679325\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72839445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38268291\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80809031\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63616194\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10593480\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62001428\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2717928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37983307\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89866637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638153\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1916384\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46031408\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55579288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746754\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24289083\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66615778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56626243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20741385\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47880328\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q681501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25036082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59092300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56656109\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70569900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q86748173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49490137\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56522048\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1752\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84365227\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43906807\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61797590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3440287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66018184\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2557\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18393083\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58991654\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70529933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66573890\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75066484\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73128185\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53380051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59135880\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11885950\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84479926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58133296\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57497365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58626655\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70082418\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58033479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29032132\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1366\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681537\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24798621\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59520568\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34084891\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965858\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73358971\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66563571\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38070288\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78739799\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37176024\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78034362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84287790\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625984\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667560\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50147705\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58628265\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71404012\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611103\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59077830\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55933001\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032676\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694103\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59345268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7209\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89757030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22329445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73862249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66503444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852614\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57539737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59383678\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44464056\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75630156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16287483\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58985929\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3712\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667841\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2353\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22675364\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33945850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79448203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072435\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80467138\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611025\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58918513\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090470\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047196\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57395864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60431620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91848848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964833\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038117\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667503\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55880952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58173479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70710519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82434561\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66591130\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1269\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58169884\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66501877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6510\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42759688\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16327012\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16710805\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037753\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852627\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72076483\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q9311444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3643\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039442\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73301796\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81368221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7649212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88013874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57535318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58982742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33887149\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45820870\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q426745\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65602465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851595\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56658776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58135900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78085108\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11285593\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q498187\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88355601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61311241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59078491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22809145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7160\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7162\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22330256\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7253\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66558559\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66821598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82969330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87988388\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73848127\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74412363\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1581\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64357639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55025113\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60517680\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56040042\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88192695\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17142722\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40866874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34197343\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29802965\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66560167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60103941\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56675254\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82154252\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56851764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1697092\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681495\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068077\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5004\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60059926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73848149\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58898396\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74438741\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5341286\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35219848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006620\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14851902\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33980030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708657\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636727\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59034803\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80458698\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89757063\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q248376\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49576178\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66590580\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4500\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1547\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12884555\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3301\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90278705\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85111803\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49688339\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3729\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28050380\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30323970\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62555595\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60303776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60172104\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71331747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61459352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047174\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68087009\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24338379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61248632\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5898\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39570919\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84078018\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13373900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74387518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2198\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67696175\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77655668\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74205111\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667274\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42518897\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3343005\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64870206\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36536076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60565106\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59031039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87977781\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3037\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667537\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60680507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87289789\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q26454986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6875\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40840080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q76000631\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5905651\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7081\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84481066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88420616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12094812\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73118411\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692947\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56376142\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2566026\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090766\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55884446\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42641723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4495716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93290588\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91141252\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11224008\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091794\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44883975\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79213588\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35647276\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30082658\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29011738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15504007\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64812851\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16924633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38279875\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67735768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63431159\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74472668\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89722738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30891544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79388814\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55981170\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63436639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58283387\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60085543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47524426\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63400219\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91941315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36757992\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60682186\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20850849\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60123818\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692914\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84248466\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59002580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59035851\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q8261440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54399662\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3730\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15850590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708577\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38331872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47690568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56214810\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57591831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66505181\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20820011\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q846360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30019717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17542926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12336608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12183006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1227747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69336485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2987450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708572\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73864627\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2921726\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85809429\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667090\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78182962\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43950725\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84471501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91250837\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71880890\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68408145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35961983\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81120140\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44068575\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92701441\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6544\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5422\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54214049\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60594464\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91463287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23038556\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66589334\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3956339\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70460306\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5670\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980244\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59216528\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71872824\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2782326\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006354\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65028437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33701841\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89266623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1449\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44305455\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17991986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746751\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21075823\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18391611\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16474028\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047218\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17050236\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765371\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1859784\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56445754\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12027742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84298625\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1592\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3822\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4295\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58558163\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56698946\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50451671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7318358\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61249010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P559\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59616003\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43195545\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58812539\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51159225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746791\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53931299\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55396945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84247973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43916273\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61248612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33038168\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74030064\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636702\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030394\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12051308\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56605590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14905700\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49553500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068075\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84274685\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63410620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66789593\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78027215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33082998\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71125249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91666984\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66544192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12599076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59037526\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22911774\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61248784\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56384971\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852619\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78119547\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73923194\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37138983\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155052\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38247154\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88284939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57826006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2030245\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60094972\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89757102\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41806836\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746675\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52076135\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16034400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87939741\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40362623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22236200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73305703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60727419\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42463776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734359\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37750115\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84299551\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3006801\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58982779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28502569\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681586\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58133302\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72358764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35026666\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57629324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852599\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27885123\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58730533\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56687451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67785005\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7261\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74437022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27889310\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63437359\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62555520\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6587\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17781874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61834409\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79485608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037745\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83512382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681591\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637169\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638154\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89508939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18236098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59076898\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091862\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6166\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58981315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2281630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52171144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980125\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55890146\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2381062\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2852\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62554494\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6646014\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56236880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28008243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6902\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41446476\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q180043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91432327\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67040294\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44007369\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5900655\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40984512\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33767112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680078\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12046878\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7727\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2215100\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59157150\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2795\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46036445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63979927\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6563\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34585815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75801718\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64260953\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70568725\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55880194\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2032\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1047129\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81570471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7252\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63252853\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36465471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44329936\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59037465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734515\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91858696\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636913\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63770443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55789049\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37546082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49490023\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59486367\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694125\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74616066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66507099\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43184554\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091159\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61745706\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52089653\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55891501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61535026\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6224\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91739076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693072\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57933311\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89055460\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55092971\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7824266\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59001713\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16321430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45182324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36084786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66507330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66515634\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59034548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83069379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155028\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40648173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59731975\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P520\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66688937\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66724450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5769\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7415\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78086635\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58081477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q571112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5897\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18168614\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34388618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q130596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55670964\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73215457\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55933978\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58381920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915157\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681756\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59040112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55883560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89454997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q515487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30595453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037749\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58981855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2914865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3034808\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88013896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q941413\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35689748\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16578059\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12043671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17061485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67391763\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74506663\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46086677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072374\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36260332\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852613\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5967378\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2551\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59077764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657164\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58390623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15788778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57858242\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5465488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80120211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59261901\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18356933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59040057\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3030630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18762207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36475676\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708817\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87913717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47223703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77420921\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4533225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6545\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17152310\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627421\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60729272\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58713397\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47004757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89663016\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53027591\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q902733\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30625279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61452488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48015805\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35417711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68304097\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58024844\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44042015\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42904932\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62089315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980816\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34995584\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72681620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37669027\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52486321\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57826013\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734562\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57416641\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88909747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59611218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58082867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56773003\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1759797\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81568330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43555634\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7623420\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59401950\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45845277\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047180\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57261880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091516\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246795\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41639531\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091275\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3464\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57943840\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77253277\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681557\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56072650\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1880\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39292352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64385953\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15070163\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71036495\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57705098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62037675\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78157652\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46019721\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66511786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7719\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1022439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27918922\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12148487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92120857\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55623778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18340291\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55789494\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25933403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89756950\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42888697\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18679117\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59475250\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2313210\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61067917\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7561\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46410656\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59757607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30279388\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56344544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773849\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90467686\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29040297\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87979188\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79091747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19829402\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60568764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33695242\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708835\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70943739\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80927264\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29547799\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66591079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25446819\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18611872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88180294\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52022204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89722728\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22980233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33979600\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7165\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22808118\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83563299\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21076236\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3492548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047198\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q334622\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61661768\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33333393\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62515032\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75744042\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21862870\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625714\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28324850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039719\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3406405\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60065228\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12070726\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038641\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q230773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77982012\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5261096\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636641\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88862465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58742620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3268764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38247694\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56016940\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84288305\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61979400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6815348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10385896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84288633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030941\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62019215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032045\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068061\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60065211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59031265\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74202066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92700703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77696500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56672039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047232\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72041259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15077340\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681871\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84479664\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56060497\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070402\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33461541\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35717610\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3177867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64922682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66590089\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84567767\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34127834\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74424079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681908\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78295452\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5304\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7122\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681785\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734410\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60208424\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54452490\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65683204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28590574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53964612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89308296\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59000318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637063\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2341\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74200193\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q328566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79366664\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q76523972\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56092582\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35464229\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59493528\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53559051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q719419\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84286737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73935677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58129147\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93390200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87885164\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297970\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64101776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22074608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58297595\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60110\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28816010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51599582\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36388823\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28667703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57926311\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58158716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4569238\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12893940\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72729171\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58392821\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7469\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18012653\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18553681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91463220\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55788456\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27865780\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23067080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28129958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77166485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58506813\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7309\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71044374\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q31087626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73862359\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35554451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88020063\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59092080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52883628\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35803642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56659539\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20948792\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16637260\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60095043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59035285\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56659547\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73221682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63459006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58901786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64768673\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72463157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18059915\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708731\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43108199\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60045937\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48340037\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "complicated-puzzle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node1_compl_emb'] = wikiDF['node1'].apply(lambda p: compEmbeddings[p] if p in compEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "canadian-divorce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node2_compl_emb'] = wikiDF['node2'].apply(lambda p: compEmbeddings[p] if p in compEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "typical-delay",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node1_transe_emb'] = wikiDF['node1'].apply(lambda p: transeEmbeddings[p] if p in transeEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "passive-magazine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node2_transe_emb'] = wikiDF['node2'].apply(lambda p: transeEmbeddings[p] if p in transeEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interested-sympathy",
-   "metadata": {},
-   "source": [
-    "## Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "special-paradise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')\n",
-    "# sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "# sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "# sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "# print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "# # sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "\n",
-    "# df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "# df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "# df1 = df1.set_index('node1')\n",
-    "# df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "\n",
-    "# print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "# # df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "committed-bathroom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "# print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "# abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "given-sapphire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print(f\"We have {len(wordSimSet)} no. of Qnodes extracted from node1, node2 of wikidata CS\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "black-throat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "painful-survivor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF_subset = abstractsDF2[abstractsDF2.node1.apply(lambda p: p in wordSimSet)]\n",
-    "# abstractsDF_subset = abstractsDF_subset[~abstractsDF_subset.abstract.isna()]\n",
-    "# len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "democratic-priority",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from nltk.tokenize import sent_tokenize\n",
-    "# abstractsDF_subset['abstract_firstSent'] = abstractsDF_subset.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "likely-north",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF_subset.to_csv('../data/M-Pairs/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "legendary-contract",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset = pd.read_csv('../data/M-Pairs/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "worst-municipality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31144"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "durable-princeton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getBERTSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "def getBERTSentEmbeddingsForSent(val1, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(val1)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "victorian-orientation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "74.81354665756226 s\n",
-      "31.950600385665894 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "abstractsDF_subset['BERT_abstract_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract, 'bert-base-nli-mean-tokens')))\n",
-    "abstractsDF_subset['BERT_abstract_firstSent_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "stuffed-stamp",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb']:\n",
-    "    abstractsDF_subset[col] = abstractsDF_subset[col].apply(lambda p: p.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "excessive-compression",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [Unnamed: 0, index, id, node1, label, node2, ignore, url, ignore2, abstract, abstract_firstSent, BERT_abstract_emb, BERT_abstract_firstSent_emb]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF_subset[abstractsDF_subset.node1.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "instructional-wireless",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs = wikiDF.set_index('node1').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "flush-share",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.reset_index().set_index('node2').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "changing-calculator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs1.reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "mathematical-denver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_emb'] = wikiDF_wabs1['BERT_abstract_emb'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_emb'] = wikiDF_wabs1['BERT_abstract_firstSent_emb'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_emb_right'] = wikiDF_wabs1['BERT_abstract_emb_right'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_emb_right'] = wikiDF_wabs1['BERT_abstract_firstSent_emb_right'].tolist()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "patent-capability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['node1_compl_emb'] = wikiDF_wabs1['node1_compl_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node2_compl_emb'] = wikiDF_wabs1['node2_compl_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node1_transe_emb'] = wikiDF_wabs1['node1_transe_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node2_transe_emb'] = wikiDF_wabs1['node2_transe_emb'].apply(lambda p: p.split(',') if p else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "double-mediterranean",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>node1_transe_emb</th>\n",
-       "      <th>node2_transe_emb</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.178757623, -1.513463259, -1.526623845, 0.3...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.396865129, 0.509240150, -0.284384042, -0.5...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.363599300, -0.764536679, 0.060853526, -0.0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.337825149, -0.189670041, -0.480503321, -0.2...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.043086100, -0.041153915, -0.603652894, -0.4...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-1.035292268, 0.008348951, -1.242931604, -1.6...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.363599300, -0.764536679, 0.060853526, -0.0...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.043086100, -0.041153915, -0.603652894, -0.4...  ...   \n",
-       "\n",
-       "                                    node1_transe_emb node2_transe_emb  \\\n",
-       "0  [-0.178757623, -1.513463259, -1.526623845, 0.3...             None   \n",
-       "1  [-0.396865129, 0.509240150, -0.284384042, -0.5...             None   \n",
-       "2  [0.337825149, -0.189670041, -0.480503321, -0.2...             None   \n",
-       "3                                               None             None   \n",
-       "4  [-1.035292268, 0.008348951, -1.242931604, -1.6...             None   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                                NaN            NaN   \n",
-       "3                                                NaN            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                     NaN   \n",
-       "1                      NaN                     NaN   \n",
-       "2                      NaN                     NaN   \n",
-       "3                      NaN                     NaN   \n",
-       "4                      NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \n",
-       "0                               NaN  \n",
-       "1                               NaN  \n",
-       "2                               NaN  \n",
-       "3                               NaN  \n",
-       "4                               NaN  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dietary-function",
-   "metadata": {},
-   "source": [
-    "## Text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "seven-individual",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimList = list(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "identical-brighton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bsize = len(wordSimList) // 20\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(wordSimList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz --word-separator '|' -p '\" + '|'.join(wordSimList[i:i+bsize]) + \";;' -o ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "front-acquisition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(1,11):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "designing-rugby",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(11,21):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "incredible-cedar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DFArr = []\n",
-    "for cnt in range(1,21):\n",
-    "    textEmb_7props_DFArr.append(pd.read_csv(\"../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\", sep='\\t'))\n",
-    "textEmb_7props_DF = pd.concat(textEmb_7props_DFArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "underlying-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF.to_csv('../data/M-Pairs/output/P279-text-embedding-7-props-all.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "central-product",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = textEmb_7props_DF[textEmb_7props_DF.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "broke-cathedral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "renewable-brand",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q995878</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[1.3671061, 0.9123121, -0.79829437, 0.9297752,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q995609</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.27214786, 1.5027193, -1.0903299, -0.1850157...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q994507</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.35614717, 1.6611174, -0.2951356, 0.81781155...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q986635</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.26672798, 0.63939905, -1.2968327, 0.1320171...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9813868</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.08139745, 0.39049774, 0.24617954, -0.07873...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node        property                                              value\n",
-       "0   Q995878  text_embedding  [1.3671061, 0.9123121, -0.79829437, 0.9297752,...\n",
-       "2   Q995609  text_embedding  [0.27214786, 1.5027193, -1.0903299, -0.1850157...\n",
-       "4   Q994507  text_embedding  [0.35614717, 1.6611174, -0.2951356, 0.81781155...\n",
-       "6   Q986635  text_embedding  [0.26672798, 0.63939905, -1.2968327, 0.1320171...\n",
-       "8  Q9813868  text_embedding  [-0.08139745, 0.39049774, 0.24617954, -0.07873..."
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "gothic-california",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs2 = wikiDF_wabs1.set_index('node1').join(textEmb_7props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "smooth-albania",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs2 = wikiDF_wabs2.rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "basic-jumping",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs2.reset_index().set_index('node2').join(textEmb_7props_DF.set_index('node')[['value']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "original-canada",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs3.rename(columns={'index': 'node1'}).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "deadly-atmosphere",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs3.rename(columns={'index': 'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "palestinian-trance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "difficult-athletics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "dutch-principal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1 = wikiDF_wabs1.drop(columns=['value','value_right'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "relative-clearing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'relation', 'node1;label', 'node2;label',\n",
-       "       'relation;label', 'relation;dimension', 'source', 'sentence',\n",
-       "       'node1_compl_emb', 'node2_compl_emb', 'node1_transe_emb',\n",
-       "       'node2_transe_emb', 'abstract', 'abstract_firstSent',\n",
-       "       'BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'value', 'value_right'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "existing-juice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cols = ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'BERT_abstract_emb_right', \n",
-    "#         'BERT_abstract_firstSent_emb_right', \n",
-    "#         'node1_compl_emb', 'node2_compl_emb', \n",
-    "#         'node1_transe_emb', 'node2_transe_emb', 'value', 'value_right']\n",
-    "# for col in cols:\n",
-    "#     wikiDF_wabs1[col] = wikiDF_wabs1[col].apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "unlimited-monster",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.363599300, -0.764536679, 0.060853526, -0.0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.043086100, -0.041153915, -0.603652894, -0.4...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.363599300, -0.764536679, 0.060853526, -0.0...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.043086100, -0.041153915, -0.603652894, -0.4...  ...   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                                NaN            NaN   \n",
-       "3                                                NaN            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                     NaN   \n",
-       "1                      NaN                     NaN   \n",
-       "2                      NaN                     NaN   \n",
-       "3                      NaN                     NaN   \n",
-       "4                      NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "overall-blues",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1.to_csv('../data/M-Pairs/wikidataCS_wemb.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "radio-flexibility",
-   "metadata": {},
-   "source": [
-    "# Summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "appropriate-integer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs = pd.read_csv('../data/M-Pairs/wikidataCS_wemb.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "rational-kansas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "suburban-cruise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.copy()\n",
-    "cols = ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'BERT_abstract_emb_right', \n",
-    "        'BERT_abstract_firstSent_emb_right', \n",
-    "        'node1_compl_emb', 'node2_compl_emb', \n",
-    "        'node1_transe_emb', 'node2_transe_emb', 'value', 'value_right']\n",
-    "for col in cols:\n",
-    "    wikiDF_wabs1[col] = wikiDF_wabs1[col].apply(lambda p: [float(p1.replace(\"'\",\"\")) for p1 in p[1:-1].split(',')] if type(p) != float else None)\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "electronic-vietnamese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-design",
-   "metadata": {},
-   "source": [
-    "## Fix Abstract stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "little-split",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "separate-apache",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF1 = labelsDF.drop(columns=['id', 'label'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "medical-authority",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs1.set_index('node1').join(labelsDF1.set_index('node1'), rsuffix='_label')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "julian-sector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.reset_index().rename(columns={'index':'node1'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "marine-lloyd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.set_index('node2').join(labelsDF1.set_index('node1'), rsuffix='_label_right').rename(columns={'node2':'node2_label_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "biblical-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "piano-pencil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.rename(columns={'node2_label': 'node1_wiki_label', 'node2_label_right': 'node2_wiki_label'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "statistical-order",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "      <th>node1_wiki_label</th>\n",
-       "      <th>node2_wiki_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'temple'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'tram'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'number of objects'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "      <td>'student organization of'@en</td>\n",
-       "      <td>'represents'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "      <td>'award ceremony'@en</td>\n",
-       "      <td>'facet of'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \\\n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...   \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...   \n",
-       "\n",
-       "               node1_wiki_label node2_wiki_label  \n",
-       "0                   'temple'@en    'quantity'@en  \n",
-       "1                     'tram'@en    'quantity'@en  \n",
-       "2        'number of objects'@en    'quantity'@en  \n",
-       "3  'student organization of'@en  'represents'@en  \n",
-       "4           'award ceremony'@en    'facet of'@en  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs4.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "introductory-elizabeth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "563"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs4['node2_wiki_label'].isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "packed-simpson",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "established-preference",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['node1;label_temp'] = wikiDF_wabs1['node1;label'].fillna(\"\")\n",
-    "wikiDF_wabs1['node2;label_temp'] = wikiDF_wabs1['node2;label'].fillna(\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "sunrise-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "      <th>node1;label_temp</th>\n",
-       "      <th>node2;label_temp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right         node1;label_temp  \\\n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...                   temple   \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...                     tram   \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...        number of objects   \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  student organization of   \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...           award ceremony   \n",
-       "\n",
-       "  node2;label_temp  \n",
-       "0         quantity  \n",
-       "1         quantity  \n",
-       "2         quantity  \n",
-       "3       represents  \n",
-       "4         facet of  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "advance-bradley",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "36.53356313705444 s\n",
-      "45.23650908470154 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['node1;label_emb'] = pd.Series(list(getBERTSentEmbeddings(wikiDF_wabs1['node1;label_temp'], 'bert-base-nli-mean-tokens')))\n",
-    "wikiDF_wabs1['node2;label_emb'] = pd.Series(list(getBERTSentEmbeddings(wikiDF_wabs1['node2;label_temp'], 'bert-base-nli-mean-tokens')))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "grand-tooth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0         [1.1099516, 0.96340746, 1.9986696, -0.26332825...\n",
-       "1         [0.11062625, -0.8170154, 1.9477333, 0.6333619,...\n",
-       "2         [0.6716414, -0.08505987, 1.6483774, 1.0254748,...\n",
-       "3         [0.11234443, -0.34427452, 1.2779658, 0.5219726...\n",
-       "4         [-0.20389149, 0.1330459, 2.6067894, 0.7318787,...\n",
-       "                                ...                        \n",
-       "101766    [-0.19040215, 0.31874508, 0.7144903, 0.2607778...\n",
-       "101767    [-0.1481771, -0.42037064, 1.2999005, 0.4722842...\n",
-       "101768    [-0.38610217, -0.26171663, 1.5519874, 0.358043...\n",
-       "101769    [-0.32396185, -0.2601691, 1.5890372, 0.6090804...\n",
-       "101770    [0.12058014, 0.57743263, 1.3856344, 0.03655950...\n",
-       "Name: node1;label_emb, Length: 101771, dtype: object"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['node1;label_emb']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "satisfactory-minutes",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractEmbsLeft(row):\n",
-    "    if row['BERT_abstract_emb']:\n",
-    "        return row['BERT_abstract_emb']\n",
-    "    elif row['node1;label']:\n",
-    "        return row['node1;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_label_emb'] = wikiDF_wabs1.apply(fixAbstractEmbsLeft, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "alpha-dressing",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractEmbsRight(row):\n",
-    "    if row['BERT_abstract_emb_right']:\n",
-    "        return row['BERT_abstract_emb_right']\n",
-    "    elif row['node2;label']:\n",
-    "        return row['node2;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_label_emb_right'] = wikiDF_wabs1.apply(fixAbstractEmbsRight, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "short-consent",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractFirstSentEmbsLeft(row):\n",
-    "    if row['BERT_abstract_emb']:\n",
-    "        return row['BERT_abstract_firstSent_emb']\n",
-    "    elif row['node1;label']:\n",
-    "        return row['node1;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_emb'] = wikiDF_wabs1.apply(fixAbstractFirstSentEmbsLeft, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "derived-wholesale",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractFirstSentEmbsRight(row):\n",
-    "    if row['BERT_abstract_emb_right']:\n",
-    "        return row['BERT_abstract_firstSent_emb_right']\n",
-    "    elif row['node2;label']:\n",
-    "        return row['node2;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_emb_right'] = wikiDF_wabs1.apply(fixAbstractFirstSentEmbsRight, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "waiting-wholesale",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getCosSims(wikiDF_wabs1, col1, col2):\n",
-    "    return wikiDF_wabs1.apply(lambda p: cosine_similarity(np.array(p[col1]).reshape(1,-1), np.array(p[col2]).reshape(1,-1))[0][0] if (type(p[col1])==list and type(p[col2])==list) else -1, axis=1)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "invalid-recommendation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abstract_emb', 'BERT_abstract_emb_right')\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abstract_firstSent_emb', 'BERT_abstract_firstSent_emb_right')\n",
-    "wikiDF_wabs1['complex_cosSim'] = getCosSims(wikiDF_wabs1, 'node1_compl_emb', 'node2_compl_emb')\n",
-    "wikiDF_wabs1['transe_cosSim'] = getCosSims(wikiDF_wabs1, 'node1_transe_emb', 'node2_transe_emb')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "moderate-bulgarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def changeScale(val1, minVal=0, maxVal=1):\n",
-    "    return 4 - 3 * (val1 - minVal) / (maxVal - minVal) if val1 != -1 else -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "id": "imported-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    30740.000000\n",
-       "mean         0.664270\n",
-       "std          0.132545\n",
-       "min          0.054277\n",
-       "25%          0.577663\n",
-       "50%          0.678386\n",
-       "75%          0.764839\n",
-       "max          1.000000\n",
-       "Name: BERT_abstract_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_cosSim'][wikiDF_wabs1['BERT_abstract_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "id": "assumed-personality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    30740.000000\n",
-       "mean         0.614450\n",
-       "std          0.157435\n",
-       "min         -0.073644\n",
-       "25%          0.507432\n",
-       "50%          0.627924\n",
-       "75%          0.731958\n",
-       "max          1.000000\n",
-       "Name: BERT_abstract_firstSent_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_firstSent_cosSim'][wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "competitive-practice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    98495.000000\n",
-       "mean         0.654858\n",
-       "std          0.154211\n",
-       "min         -0.033131\n",
-       "25%          0.560126\n",
-       "50%          0.671793\n",
-       "75%          0.764953\n",
-       "max          1.000000\n",
-       "Name: complex_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['complex_cosSim'][wikiDF_wabs1['complex_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "historical-andrews",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    98495.000000\n",
-       "mean         0.652252\n",
-       "std          0.176201\n",
-       "min         -0.137044\n",
-       "25%          0.528159\n",
-       "50%          0.656617\n",
-       "75%          0.784122\n",
-       "max          1.000000\n",
-       "Name: transe_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['transe_cosSim'][wikiDF_wabs1['transe_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "buried-catalyst",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    97964.000000\n",
-       "mean         1.809035\n",
-       "std          0.387920\n",
-       "min          1.000000\n",
-       "25%          1.524458\n",
-       "50%          1.767705\n",
-       "75%          2.053570\n",
-       "max          4.000000\n",
-       "Name: textEmb_score, dtype: float64"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['textEmb_score'][wikiDF_wabs1['textEmb_score'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "unlimited-minnesota",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.05427744093567516"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "id": "false-sheep",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_score'] = wikiDF_wabs1['BERT_abstract_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abstract_cosSim'].where(wikiDF_wabs1['BERT_abstract_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abstract_cosSim'].max()))\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_score'] = wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].where(wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].max()))\n",
-    "wikiDF_wabs1['complex_score'] = wikiDF_wabs1['complex_cosSim'].apply(changeScale, args=(wikiDF_wabs1['complex_cosSim'].where(wikiDF_wabs1['complex_cosSim'] != -1).min(), wikiDF_wabs1['complex_cosSim'].max()))\n",
-    "wikiDF_wabs1['transe_score'] = wikiDF_wabs1['transe_cosSim'].apply(changeScale, args=(wikiDF_wabs1['transe_cosSim'].where(wikiDF_wabs1['transe_cosSim'] != -1).min(), wikiDF_wabs1['transe_cosSim'].max()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "caring-passenger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1['BERT_abstract_score'] = wikiDF_wabs1['BERT_abstract_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['BERT_abstract_firstSent_score'] = wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['complex_score'] = wikiDF_wabs1['complex_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['transe_score'] = wikiDF_wabs1['transe_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "weighted-heavy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['textEmb_cosSim'] = getCosSims(wikiDF_wabs1, 'value', 'value_right')\n",
-    "wikiDF_wabs1['textEmb_score'] = wikiDF_wabs1['textEmb_cosSim'].apply(changeScale, args=(wikiDF_wabs1['textEmb_cosSim'].where(wikiDF_wabs1['textEmb_cosSim'] != -1).min(), wikiDF_wabs1['textEmb_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "international-neighborhood",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abs_label_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abs_label_emb', 'BERT_abs_label_emb_right')\n",
-    "wikiDF_wabs1['BERT_abs_label_score'] = wikiDF_wabs1['BERT_abs_label_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abs_label_cosSim'].where(wikiDF_wabs1['BERT_abs_label_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abs_label_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "sized-coupon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abs_firstSent_label_emb', 'BERT_abs_firstSent_label_emb_right')\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_score'] = wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].where(wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "emotional-affect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs1.rename(columns={'value_score': 'textEmb_score'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "pleased-medline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BERT_abs_label_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "      <th>complex_cosSim</th>\n",
-       "      <th>transe_cosSim</th>\n",
-       "      <th>textEmb_cosSim</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.560186</td>\n",
-       "      <td>-0.497306</td>\n",
-       "      <td>-0.512354</td>\n",
-       "      <td>0.601589</td>\n",
-       "      <td>0.599066</td>\n",
-       "      <td>0.659361</td>\n",
-       "      <td>0.577693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.219777</td>\n",
-       "      <td>0.767613</td>\n",
-       "      <td>0.746306</td>\n",
-       "      <td>0.329139</td>\n",
-       "      <td>0.339258</td>\n",
-       "      <td>0.351968</td>\n",
-       "      <td>0.201635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-0.199269</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.200145</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.405427</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.544812</td>\n",
-       "      <td>0.512282</td>\n",
-       "      <td>0.624796</td>\n",
-       "      <td>0.439193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.569074</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.665646</td>\n",
-       "      <td>0.648872</td>\n",
-       "      <td>0.731336</td>\n",
-       "      <td>0.582915</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.728068</td>\n",
-       "      <td>0.536618</td>\n",
-       "      <td>0.458004</td>\n",
-       "      <td>0.761654</td>\n",
-       "      <td>0.779334</td>\n",
-       "      <td>0.817482</td>\n",
-       "      <td>0.723494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       BERT_abs_label_cosSim  BERT_abstract_cosSim  \\\n",
-       "count          101771.000000         101771.000000   \n",
-       "mean                0.560186             -0.497306   \n",
-       "std                 0.219777              0.767613   \n",
-       "min                -0.199269             -1.000000   \n",
-       "25%                 0.405427             -1.000000   \n",
-       "50%                 0.569074             -1.000000   \n",
-       "75%                 0.728068              0.536618   \n",
-       "max                 1.000000              1.000000   \n",
-       "\n",
-       "       BERT_abstract_firstSent_cosSim  complex_cosSim  transe_cosSim  \\\n",
-       "count                   101771.000000   101771.000000  101771.000000   \n",
-       "mean                        -0.512354        0.601589       0.599066   \n",
-       "std                          0.746306        0.329139       0.339258   \n",
-       "min                         -1.000000       -1.000000      -1.000000   \n",
-       "25%                         -1.000000        0.544812       0.512282   \n",
-       "50%                         -1.000000        0.665646       0.648872   \n",
-       "75%                          0.458004        0.761654       0.779334   \n",
-       "max                          1.000000        1.000000       1.000000   \n",
-       "\n",
-       "       textEmb_cosSim  BERT_abs_firstSent_label_cosSim  \n",
-       "count   101771.000000                    101771.000000  \n",
-       "mean         0.659361                         0.577693  \n",
-       "std          0.351968                         0.201635  \n",
-       "min         -1.000000                        -0.200145  \n",
-       "25%          0.624796                         0.439193  \n",
-       "50%          0.731336                         0.582915  \n",
-       "75%          0.817482                         0.723494  \n",
-       "max          1.000000                         1.000000  "
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1[filter(lambda p: 'cosSim' in p, wikiDF_wabs1.columns)].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "impossible-navigator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1.to_csv('../data/M-Pairs/wikidataCS_wemb.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "welsh-salem",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF_wabs1['dim'] = wikiDF_wabs1['relation'].apply(lambda p: mapper1[p])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "common-building",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>[-0.21400201320648193, -0.1853264421224594, 2....</td>\n",
-       "      <td>0.111954</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.570280</td>\n",
-       "      <td>3.949128</td>\n",
-       "      <td>3.664139</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.066402</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.662764</td>\n",
-       "      <td>3.914897</td>\n",
-       "      <td>3.800794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6716414093971252, -0.08505986630916595, 1.6...</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.701743</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.519090</td>\n",
-       "      <td>1.894770</td>\n",
-       "      <td>1.894770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.11234442889690399, -0.34427452087402344, 1....</td>\n",
-       "      <td>[0.1607293337583542, -0.1969822645187378, 2.40...</td>\n",
-       "      <td>0.546000</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.110830</td>\n",
-       "      <td>2.362001</td>\n",
-       "      <td>2.362001</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>[-0.03644487261772156, -0.5494412183761597, 2....</td>\n",
-       "      <td>0.372485</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.443632</td>\n",
-       "      <td>3.798896</td>\n",
-       "      <td>2.882546</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                        BERT_abs_firstSent_label_emb  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....   \n",
-       "2  [0.6716414093971252, -0.08505986630916595, 1.6...   \n",
-       "3  [0.11234442889690399, -0.34427452087402344, 1....   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...   \n",
-       "\n",
-       "                  BERT_abs_firstSent_label_emb_right  \\\n",
-       "0  [-0.21400201320648193, -0.1853264421224594, 2....   \n",
-       "1  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "2  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "3  [0.1607293337583542, -0.1969822645187378, 2.40...   \n",
-       "4  [-0.03644487261772156, -0.5494412183761597, 2....   \n",
-       "\n",
-       "  BERT_abs_firstSent_label_cosSim BERT_abstract_score  \\\n",
-       "0                        0.111954                -1.0   \n",
-       "1                        0.066402                -1.0   \n",
-       "2                        0.701743                -1.0   \n",
-       "3                        0.546000                -1.0   \n",
-       "4                        0.372485                -1.0   \n",
-       "\n",
-       "  BERT_abstract_firstSent_score complex_score transe_score textEmb_score  \\\n",
-       "0                          -1.0          -1.0         -1.0      2.570280   \n",
-       "1                          -1.0          -1.0         -1.0      2.662764   \n",
-       "2                          -1.0          -1.0         -1.0      2.519090   \n",
-       "3                          -1.0          -1.0         -1.0      2.110830   \n",
-       "4                          -1.0          -1.0         -1.0      2.443632   \n",
-       "\n",
-       "  BERT_abs_label_score BERT_abs_firstSent_label_score  \n",
-       "0             3.949128                       3.664139  \n",
-       "1             3.914897                       3.800794  \n",
-       "2             1.894770                       1.894770  \n",
-       "3             2.362001                       2.362001  \n",
-       "4             3.798896                       2.882546  \n",
-       "\n",
-       "[5 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "polar-constitutional",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>0 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [node2, node1, relation, node1;label, node2;label, relation;label, relation;dimension, source, sentence, node1_compl_emb, node2_compl_emb, node1_transe_emb, node2_transe_emb, abstract, abstract_firstSent, BERT_abstract_emb, BERT_abstract_firstSent_emb, abstract_right, abstract_firstSent_right, BERT_abstract_emb_right, BERT_abstract_firstSent_emb_right, value, value_right, node1;label_temp, node2;label_temp, node1;label_emb, node2;label_emb, BERT_abs_label_emb, BERT_abs_label_emb_right, BERT_abs_label_cosSim, BERT_abstract_cosSim, BERT_abstract_firstSent_cosSim, complex_cosSim, transe_cosSim, textEmb_cosSim, dim, BERT_abs_firstSent_label_emb, BERT_abs_firstSent_label_emb_right, BERT_abs_firstSent_label_cosSim, BERT_abstract_score, BERT_abstract_firstSent_score, complex_score, transe_score, textEmb_score, BERT_abs_label_score, BERT_abs_firstSent_label_score]\n",
-       "Index: []\n",
-       "\n",
-       "[0 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1[(wikiDF_wabs1.dim == 'other') & (wikiDF_wabs1.complex_score != -1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "id": "revolutionary-benchmark",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     72707\n",
-       "meronymy         6886\n",
-       "context          5541\n",
-       "distinctness     4934\n",
-       "utility          2243\n",
-       "antonymy         2184\n",
-       "ordering         2107\n",
-       "material         1426\n",
-       "synonymy         1070\n",
-       "properties       1049\n",
-       "derivation        540\n",
-       "causation         510\n",
-       "similarity        345\n",
-       "attribution       187\n",
-       "other              42\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 341,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.dim.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "id": "naughty-station",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "cnts = []\n",
-    "for col in scoreCols:\n",
-    "    cnts.append(wikiDF_wabs1[wikiDF_wabs1[col] > -1].groupby('dim')[col].agg('count'))\n",
-    "cnts = pd.concat(cnts, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 343,
-   "id": "missing-fighter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2131.0</td>\n",
-       "      <td>2131.0</td>\n",
-       "      <td>2154</td>\n",
-       "      <td>2184</td>\n",
-       "      <td>2184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>181.0</td>\n",
-       "      <td>181.0</td>\n",
-       "      <td>181</td>\n",
-       "      <td>187</td>\n",
-       "      <td>187</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>494.0</td>\n",
-       "      <td>494.0</td>\n",
-       "      <td>481</td>\n",
-       "      <td>510</td>\n",
-       "      <td>510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>5409.0</td>\n",
-       "      <td>5409.0</td>\n",
-       "      <td>5413</td>\n",
-       "      <td>5541</td>\n",
-       "      <td>5541</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>537.0</td>\n",
-       "      <td>537.0</td>\n",
-       "      <td>524</td>\n",
-       "      <td>540</td>\n",
-       "      <td>540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>4831.0</td>\n",
-       "      <td>4831.0</td>\n",
-       "      <td>4807</td>\n",
-       "      <td>4934</td>\n",
-       "      <td>4934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>70211.0</td>\n",
-       "      <td>70211.0</td>\n",
-       "      <td>69793</td>\n",
-       "      <td>72707</td>\n",
-       "      <td>72707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1403.0</td>\n",
-       "      <td>1403.0</td>\n",
-       "      <td>1372</td>\n",
-       "      <td>1426</td>\n",
-       "      <td>1426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>6791.0</td>\n",
-       "      <td>6791.0</td>\n",
-       "      <td>6504</td>\n",
-       "      <td>6886</td>\n",
-       "      <td>6886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2105.0</td>\n",
-       "      <td>2105.0</td>\n",
-       "      <td>2103</td>\n",
-       "      <td>2107</td>\n",
-       "      <td>2107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>833.0</td>\n",
-       "      <td>833.0</td>\n",
-       "      <td>1026</td>\n",
-       "      <td>1049</td>\n",
-       "      <td>1049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>343.0</td>\n",
-       "      <td>343.0</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1033.0</td>\n",
-       "      <td>1033.0</td>\n",
-       "      <td>1030</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>1070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2193.0</td>\n",
-       "      <td>2193.0</td>\n",
-       "      <td>2189</td>\n",
-       "      <td>2243</td>\n",
-       "      <td>2243</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>42</td>\n",
-       "      <td>42</td>\n",
-       "      <td>42</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy             2131.0        2131.0           2154   \n",
-       "attribution           181.0         181.0            181   \n",
-       "causation             494.0         494.0            481   \n",
-       "context              5409.0        5409.0           5413   \n",
-       "derivation            537.0         537.0            524   \n",
-       "distinctness         4831.0        4831.0           4807   \n",
-       "inheritance         70211.0       70211.0          69793   \n",
-       "material             1403.0        1403.0           1372   \n",
-       "meronymy             6791.0        6791.0           6504   \n",
-       "ordering             2105.0        2105.0           2103   \n",
-       "properties            833.0         833.0           1026   \n",
-       "similarity            343.0         343.0            345   \n",
-       "synonymy             1033.0        1033.0           1030   \n",
-       "utility              2193.0        2193.0           2189   \n",
-       "other                   NaN           NaN             42   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                      2184                            2184  \n",
-       "attribution                    187                             187  \n",
-       "causation                      510                             510  \n",
-       "context                       5541                            5541  \n",
-       "derivation                     540                             540  \n",
-       "distinctness                  4934                            4934  \n",
-       "inheritance                  72707                           72707  \n",
-       "material                      1426                            1426  \n",
-       "meronymy                      6886                            6886  \n",
-       "ordering                      2107                            2107  \n",
-       "properties                    1049                            1049  \n",
-       "similarity                     345                             345  \n",
-       "synonymy                      1070                            1070  \n",
-       "utility                       2243                            2243  \n",
-       "other                           42                              42  "
-      ]
-     },
-     "execution_count": 343,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "id": "endless-globe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                      98495.0\n",
-       "transe_score                       98495.0\n",
-       "textEmb_score                      97964.0\n",
-       "BERT_abs_label_score              101771.0\n",
-       "BERT_abs_firstSent_label_score    101771.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 344,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "id": "perfect-eagle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                     0.967810\n",
-       "transe_score                      0.967810\n",
-       "textEmb_score                     0.962592\n",
-       "BERT_abs_label_score              1.000000\n",
-       "BERT_abs_firstSent_label_score    1.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 345,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()/len(wikiDF_wabs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 346,
-   "id": "apart-moisture",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
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-       "      <td>0.388491</td>\n",
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-       "      <td>0.425505</td>\n",
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-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>0.370387</td>\n",
-       "      <td>0.420851</td>\n",
-       "      <td>0.373756</td>\n",
-       "      <td>0.526051</td>\n",
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-       "      <td>0.439842</td>\n",
-       "      <td>0.448856</td>\n",
-       "      <td>0.373036</td>\n",
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-       "      <td>0.448309</td>\n",
-       "      <td>0.378352</td>\n",
-       "      <td>0.571034</td>\n",
-       "      <td>0.543981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>0.353069</td>\n",
-       "      <td>0.408219</td>\n",
-       "      <td>0.396728</td>\n",
-       "      <td>0.540599</td>\n",
-       "      <td>0.486378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>0.201187</td>\n",
-       "      <td>0.181561</td>\n",
-       "      <td>0.279172</td>\n",
-       "      <td>0.360187</td>\n",
-       "      <td>0.352700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>0.388960</td>\n",
-       "      <td>0.377157</td>\n",
-       "      <td>0.407805</td>\n",
-       "      <td>0.582726</td>\n",
-       "      <td>0.523747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>0.308485</td>\n",
-       "      <td>0.316569</td>\n",
-       "      <td>0.301411</td>\n",
-       "      <td>0.500654</td>\n",
-       "      <td>0.435636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>0.352660</td>\n",
-       "      <td>0.339445</td>\n",
-       "      <td>0.332432</td>\n",
-       "      <td>0.532540</td>\n",
-       "      <td>0.461569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>0.335069</td>\n",
-       "      <td>0.351518</td>\n",
-       "      <td>0.398808</td>\n",
-       "      <td>0.515661</td>\n",
-       "      <td>0.476737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.334116</td>\n",
-       "      <td>0.581674</td>\n",
-       "      <td>0.450678</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           0.327302      0.366009       0.434431   \n",
-       "attribution        0.364051      0.417825       0.322049   \n",
-       "causation          0.320966      0.372731       0.395919   \n",
-       "context            0.386523      0.408721       0.419279   \n",
-       "derivation         0.331256      0.388491       0.327087   \n",
-       "distinctness       0.370387      0.420851       0.373756   \n",
-       "inheritance        0.439842      0.448856       0.373036   \n",
-       "material           0.401528      0.448309       0.378352   \n",
-       "meronymy           0.353069      0.408219       0.396728   \n",
-       "ordering           0.201187      0.181561       0.279172   \n",
-       "properties         0.388960      0.377157       0.407805   \n",
-       "similarity         0.308485      0.316569       0.301411   \n",
-       "synonymy           0.352660      0.339445       0.332432   \n",
-       "utility            0.335069      0.351518       0.398808   \n",
-       "other                   NaN           NaN       0.334116   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  0.500274                        0.466038  \n",
-       "attribution               0.476401                        0.398419  \n",
-       "causation                 0.511707                        0.480563  \n",
-       "context                   0.540979                        0.504633  \n",
-       "derivation                0.433289                        0.425505  \n",
-       "distinctness              0.526051                        0.463646  \n",
-       "inheritance               0.540227                        0.494134  \n",
-       "material                  0.571034                        0.543981  \n",
-       "meronymy                  0.540599                        0.486378  \n",
-       "ordering                  0.360187                        0.352700  \n",
-       "properties                0.582726                        0.523747  \n",
-       "similarity                0.500654                        0.435636  \n",
-       "synonymy                  0.532540                        0.461569  \n",
-       "utility                   0.515661                        0.476737  \n",
-       "other                     0.581674                        0.450678  "
-      ]
-     },
-     "execution_count": 346,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summsStd = []\n",
-    "for col in scoreCols:\n",
-    "    summsStd.append(wikiDF_wabs1[(wikiDF_wabs1[col] > -1) & ~(wikiDF_wabs1[col].isna())].groupby('dim')[col].agg('std'))\n",
-    "summsStd = pd.concat(summsStd, axis=1)\n",
-    "summsStd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "id": "italic-rehabilitation",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "      <th></th>\n",
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-       "      <th>antonymy</th>\n",
-       "      <td>1.472307</td>\n",
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-       "      <td>1.961361</td>\n",
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-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>2.058412</td>\n",
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-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>1.878191</td>\n",
-       "      <td>2.074840</td>\n",
-       "      <td>1.894913</td>\n",
-       "      <td>2.058835</td>\n",
-       "      <td>2.034320</td>\n",
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-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>2.198949</td>\n",
-       "      <td>2.287010</td>\n",
-       "      <td>1.925819</td>\n",
-       "      <td>2.142938</td>\n",
-       "      <td>2.086860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>2.101236</td>\n",
-       "      <td>2.236187</td>\n",
-       "      <td>2.117177</td>\n",
-       "      <td>2.080477</td>\n",
-       "      <td>2.041239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>1.762127</td>\n",
-       "      <td>1.689348</td>\n",
-       "      <td>1.742884</td>\n",
-       "      <td>1.961034</td>\n",
-       "      <td>1.892194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>2.052364</td>\n",
-       "      <td>1.924857</td>\n",
-       "      <td>1.802573</td>\n",
-       "      <td>2.141092</td>\n",
-       "      <td>2.098482</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>2.181508</td>\n",
-       "      <td>2.178909</td>\n",
-       "      <td>1.986060</td>\n",
-       "      <td>2.117815</td>\n",
-       "      <td>2.062357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>1.933547</td>\n",
-       "      <td>1.889699</td>\n",
-       "      <td>1.852277</td>\n",
-       "      <td>2.042396</td>\n",
-       "      <td>1.989395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1.244715</td>\n",
-       "      <td>1.161449</td>\n",
-       "      <td>1.337445</td>\n",
-       "      <td>1.319064</td>\n",
-       "      <td>1.322444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1.988557</td>\n",
-       "      <td>2.172564</td>\n",
-       "      <td>2.017539</td>\n",
-       "      <td>2.193653</td>\n",
-       "      <td>2.095989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>1.791589</td>\n",
-       "      <td>1.897246</td>\n",
-       "      <td>1.554273</td>\n",
-       "      <td>2.025497</td>\n",
-       "      <td>1.945071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1.769929</td>\n",
-       "      <td>1.696103</td>\n",
-       "      <td>1.678028</td>\n",
-       "      <td>1.998886</td>\n",
-       "      <td>1.880054</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>1.927094</td>\n",
-       "      <td>2.132264</td>\n",
-       "      <td>1.918617</td>\n",
-       "      <td>2.080665</td>\n",
-       "      <td>2.037705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.773898</td>\n",
-       "      <td>2.134766</td>\n",
-       "      <td>1.918897</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           1.472307      1.552708       1.890103   \n",
-       "attribution        2.058412      2.369082       1.795290   \n",
-       "causation          1.878191      2.074840       1.894913   \n",
-       "context            2.198949      2.287010       1.925819   \n",
-       "derivation         2.101236      2.236187       2.117177   \n",
-       "distinctness       1.762127      1.689348       1.742884   \n",
-       "inheritance        2.052364      1.924857       1.802573   \n",
-       "material           2.181508      2.178909       1.986060   \n",
-       "meronymy           1.933547      1.889699       1.852277   \n",
-       "ordering           1.244715      1.161449       1.337445   \n",
-       "properties         1.988557      2.172564       2.017539   \n",
-       "similarity         1.791589      1.897246       1.554273   \n",
-       "synonymy           1.769929      1.696103       1.678028   \n",
-       "utility            1.927094      2.132264       1.918617   \n",
-       "other                   NaN           NaN       1.773898   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  1.928249                        1.961361  \n",
-       "attribution               2.003034                        1.864052  \n",
-       "causation                 2.058835                        2.034320  \n",
-       "context                   2.142938                        2.086860  \n",
-       "derivation                2.080477                        2.041239  \n",
-       "distinctness              1.961034                        1.892194  \n",
-       "inheritance               2.141092                        2.098482  \n",
-       "material                  2.117815                        2.062357  \n",
-       "meronymy                  2.042396                        1.989395  \n",
-       "ordering                  1.319064                        1.322444  \n",
-       "properties                2.193653                        2.095989  \n",
-       "similarity                2.025497                        1.945071  \n",
-       "synonymy                  1.998886                        1.880054  \n",
-       "utility                   2.080665                        2.037705  \n",
-       "other                     2.134766                        1.918897  "
-      ]
-     },
-     "execution_count": 347,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summs = []\n",
-    "for col in scoreCols:\n",
-    "    summs.append(wikiDF_wabs1[(wikiDF_wabs1[col] > -1) & ~(wikiDF_wabs1[col].isna())].groupby('dim')[col].agg('mean'))\n",
-    "summs = pd.concat(summs, axis=1)\n",
-    "summs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "id": "abandoned-armor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dim\n",
-       "ordering        1.277024\n",
-       "antonymy        1.760945\n",
-       "synonymy        1.804600\n",
-       "distinctness    1.809517\n",
-       "similarity      1.842735\n",
-       "meronymy        1.941463\n",
-       "other           1.942520\n",
-       "causation       1.988220\n",
-       "inheritance     2.003874\n",
-       "attribution     2.017974\n",
-       "utility         2.019269\n",
-       "properties      2.093660\n",
-       "material        2.105330\n",
-       "derivation      2.115263\n",
-       "context         2.128315\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 348,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summs.apply(lambda p: np.mean(list(filter(lambda y12: y12 != -1, [p[x] if not(pd.isna(p[x])) else -1 for x in scoreCols]))), axis=1).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "id": "accurate-faculty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  1             2              3   \n",
-       "attribution               4             5              1   \n",
-       "causation                 1             5              2   \n",
-       "context                   4             5              1   \n",
-       "derivation                3             5              4   \n",
-       "distinctness              3             1              2   \n",
-       "inheritance               3             2              1   \n",
-       "material                  5             4              1   \n",
-       "meronymy                  3             2              1   \n",
-       "ordering                  2             1              5   \n",
-       "properties                1             4              2   \n",
-       "similarity                2             3              1   \n",
-       "synonymy                  3             2              1   \n",
-       "utility                   2             5              1   \n",
-       "other                     4             5              1   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         4                               5  \n",
-       "attribution                      3                               2  \n",
-       "causation                        4                               3  \n",
-       "context                          3                               2  \n",
-       "derivation                       2                               1  \n",
-       "distinctness                     5                               4  \n",
-       "inheritance                      5                               4  \n",
-       "material                         3                               2  \n",
-       "meronymy                         5                               4  \n",
-       "ordering                         3                               4  \n",
-       "properties                       5                               3  \n",
-       "similarity                       5                               4  \n",
-       "synonymy                         5                               4  \n",
-       "utility                          4                               3  \n",
-       "other                            3                               2  "
-      ]
-     },
-     "execution_count": 349,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosRowWise(row):\n",
-    "    return (np.array([row[col] for col in scoreCols])).argsort().argsort() + 1\n",
-    "pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 352,
-   "id": "substantial-seven",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='dim'>"
-      ]
-     },
-     "execution_count": 352,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = pyplot.subplots(figsize=(15,10))\n",
-    "sns.heatmap(pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "id": "spatial-wrist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>11</td>\n",
-       "      <td>14</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>6</td>\n",
-       "      <td>8</td>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>14</td>\n",
-       "      <td>13</td>\n",
-       "      <td>12</td>\n",
-       "      <td>14</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>15</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>13</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>13</td>\n",
-       "      <td>11</td>\n",
-       "      <td>13</td>\n",
-       "      <td>11</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "      <td>14</td>\n",
-       "      <td>15</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>7</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>15</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             2              9   \n",
-       "attribution              11            14              6   \n",
-       "causation                 6             8             10   \n",
-       "context                  14            13             12   \n",
-       "derivation               12            12             15   \n",
-       "distinctness              3             3              4   \n",
-       "inheritance              10             7              7   \n",
-       "material                 13            11             13   \n",
-       "meronymy                  8             5              8   \n",
-       "ordering                  1             1              1   \n",
-       "properties                9            10             14   \n",
-       "similarity                5             6              2   \n",
-       "synonymy                  4             4              3   \n",
-       "utility                   7             9             11   \n",
-       "other                    15            15              5   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         2                               7  \n",
-       "attribution                      5                               2  \n",
-       "causation                        8                               9  \n",
-       "context                         14                              13  \n",
-       "derivation                       9                              11  \n",
-       "distinctness                     3                               4  \n",
-       "inheritance                     13                              15  \n",
-       "material                        11                              12  \n",
-       "meronymy                         7                               8  \n",
-       "ordering                         1                               1  \n",
-       "properties                      15                              14  \n",
-       "similarity                       6                               6  \n",
-       "synonymy                         4                               3  \n",
-       "utility                         10                              10  \n",
-       "other                           12                               5  "
-      ]
-     },
-     "execution_count": 353,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosColWise(colVals):\n",
-    "    return pd.Series(((np.array([colVals.to_list()])).argsort().argsort() + 1)[0], index=summs.index, name=colVals.name)\n",
-    "pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "id": "confused-miami",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='dim'>"
-      ]
-     },
-     "execution_count": 354,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = pyplot.subplots(figsize=(15,10))\n",
-    "sns.heatmap(pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "rocky-power",
-   "metadata": {},
-   "source": [
-    "# Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "alive-surprise",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "properties\n",
-      "                        node1;label node2;label\n",
-      "0                            temple    quantity\n",
-      "1                              tram    quantity\n",
-      "2                 number of objects    quantity\n",
-      "4                    award ceremony    facet of\n",
-      "5  geography of geographic location    facet of\n",
-      "other\n",
-      "                node1;label    node2;label\n",
-      "3   student organization of     represents\n",
-      "20                     name       nickname\n",
-      "25        mission statement     motto text\n",
-      "42              house organ  official blog\n",
-      "44                 defender     prosecutor\n",
-      "inheritance\n",
-      "             node1;label           node2;label\n",
-      "69              nickname                  name\n",
-      "214    geometric algebra  algebra over a field\n",
-      "215     division algebra  algebra over a field\n",
-      "217     operator algebra  algebra over a field\n",
-      "219  alternative algebra  algebra over a field\n",
-      "context\n",
-      "            node1;label             node2;label\n",
-      "216            mutation    algebra over a field\n",
-      "228           filmmaker     cinematic technique\n",
-      "254     film technician     cinematic technique\n",
-      "264  periodical library              periodical\n",
-      "393                 web  mechanical engineering\n",
-      "distinctness\n",
-      "             node1;label           node2;label\n",
-      "218  algebra over a ring  algebra over a field\n",
-      "300         power series   formal power series\n",
-      "311              testing                  test\n",
-      "315   test  (assessment)                  test\n",
-      "338                 lawn             grassland\n",
-      "meronymy\n",
-      "             node1;label         node2;label\n",
-      "257  decomposition model  geometric modeling\n",
-      "258          solid model  geometric modeling\n",
-      "262               volume          periodical\n",
-      "279               column          periodical\n",
-      "281                issue          periodical\n",
-      "antonymy\n",
-      "                    node1;label   node2;label\n",
-      "260  non-periodical publication    periodical\n",
-      "340              younger sister  elder sister\n",
-      "341               elder brother  elder sister\n",
-      "425              composite knot    prime knot\n",
-      "526                      prefix        suffix\n",
-      "utility\n",
-      "             node1;label       node2;label\n",
-      "308  elevator test tower              test\n",
-      "319           dummy load              test\n",
-      "479            prototype        experiment\n",
-      "591        running shoes  indoor athletics\n",
-      "597       room and board          boarding\n",
-      "attribution\n",
-      "             node1;label                node2;label\n",
-      "324          test result                       test\n",
-      "328   electronic circuit    through-hole technology\n",
-      "1239             shotgun              rifle factory\n",
-      "1644   image information  information visualization\n",
-      "1985    furniture fabric            tapestry stitch\n",
-      "derivation\n",
-      "               node1;label  node2;label\n",
-      "386              surrogate      acronym\n",
-      "428              milestone    milestone\n",
-      "435  fictional brown dwarf  brown dwarf\n",
-      "582                   rose         rose\n",
-      "755  fictional cargo pants  cargo pants\n",
-      "synonymy\n",
-      "     node1;label     node2;label\n",
-      "418   after-name     family name\n",
-      "534   playwright    stage author\n",
-      "539       knight          knight\n",
-      "575  resort town  seaside resort\n",
-      "608     mourning           grief\n",
-      "material\n",
-      "                    node1;label        node2;label\n",
-      "586       list of minor planets       minor planet\n",
-      "676   list of educational years   educational year\n",
-      "724  list of martial arts films  martial arts film\n",
-      "828                  fur collar           fake fur\n",
-      "857          champagne cocktail         sugar cube\n",
-      "causation\n",
-      "            node1;label      node2;label\n",
-      "606                loss            grief\n",
-      "736   double beta decay    beta particle\n",
-      "778              flight  wake turbulence\n",
-      "1896              spice        longevity\n",
-      "1961       brain cancer          vertigo\n",
-      "ordering\n",
-      "              node1;label         node2;label\n",
-      "766              tomorrow  day after tomorrow\n",
-      "896              group 15            group 16\n",
-      "1740     secondary source     tertiary source\n",
-      "1965  construction permit      groundbreaking\n",
-      "2065                 kilo                mega\n",
-      "similarity\n",
-      "             node1;label            node2;label\n",
-      "1039           expertise              specialty\n",
-      "1040       skilled trade              specialty\n",
-      "1224  multi-purpose hall  multi-purpose stadium\n",
-      "1369      signature song         one-hit wonder\n",
-      "1370  nine days\\' wonder         one-hit wonder\n"
-     ]
-    }
-   ],
-   "source": [
-    "for dim1 in wikiDF_wabs1.dim.unique():\n",
-    "    print(dim1)\n",
-    "    print(wikiDF_wabs1[wikiDF_wabs1.dim == dim1].head()[['node1;label', 'node2;label']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "compact-right",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vocal-convertible",
-   "metadata": {},
-   "source": [
-    "# Sampling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "laden-kentucky",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "id": "eastern-worth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_wikiDF = wikiDF_wabs1.iloc[sample_without_replacement(len(wikiDF_wabs1), 100, random_state=1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "id": "junior-service",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-247-5e0097ea79b5>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['complex_score'] = sample_wikiDF['complex_cosSim'].apply(changeScale, args=(sample_wikiDF['complex_cosSim'].where(sample_wikiDF['complex_cosSim'] != -1).min(), sample_wikiDF['complex_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['transe_score'] = sample_wikiDF['transe_cosSim'].apply(changeScale, args=(sample_wikiDF['transe_cosSim'].where(sample_wikiDF['transe_cosSim'] != -1).min(), sample_wikiDF['transe_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['textEmb_score'] = sample_wikiDF['textEmb_cosSim'].apply(changeScale, args=(sample_wikiDF['textEmb_cosSim'].where(sample_wikiDF['textEmb_cosSim'] != -1).min(), sample_wikiDF['textEmb_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['BERT_abs_label_score'] = sample_wikiDF['BERT_abs_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_label_cosSim'].where(sample_wikiDF['BERT_abs_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_label_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['BERT_abs_firstSent_label_score'] = sample_wikiDF['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_firstSent_label_cosSim'].where(sample_wikiDF['BERT_abs_firstSent_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_firstSent_label_cosSim'].max()))\n"
-     ]
-    }
-   ],
-   "source": [
-    "sample_wikiDF['complex_score'] = sample_wikiDF['complex_cosSim'].apply(changeScale, args=(sample_wikiDF['complex_cosSim'].where(sample_wikiDF['complex_cosSim'] != -1).min(), sample_wikiDF['complex_cosSim'].max()))\n",
-    "sample_wikiDF['transe_score'] = sample_wikiDF['transe_cosSim'].apply(changeScale, args=(sample_wikiDF['transe_cosSim'].where(sample_wikiDF['transe_cosSim'] != -1).min(), sample_wikiDF['transe_cosSim'].max()))\n",
-    "sample_wikiDF['textEmb_score'] = sample_wikiDF['textEmb_cosSim'].apply(changeScale, args=(sample_wikiDF['textEmb_cosSim'].where(sample_wikiDF['textEmb_cosSim'] != -1).min(), sample_wikiDF['textEmb_cosSim'].max()))\n",
-    "sample_wikiDF['BERT_abs_label_score'] = sample_wikiDF['BERT_abs_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_label_cosSim'].where(sample_wikiDF['BERT_abs_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_label_cosSim'].max()))\n",
-    "sample_wikiDF['BERT_abs_firstSent_label_score'] = sample_wikiDF['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_firstSent_label_cosSim'].where(sample_wikiDF['BERT_abs_firstSent_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_firstSent_label_cosSim'].max()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "id": "musical-penny",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.373734</td>\n",
-       "      <td>2.467629</td>\n",
-       "      <td>2.171393</td>\n",
-       "      <td>2.162535</td>\n",
-       "      <td>2.348227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.897240</td>\n",
-       "      <td>0.942942</td>\n",
-       "      <td>0.727955</td>\n",
-       "      <td>0.594187</td>\n",
-       "      <td>0.658818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>1.926772</td>\n",
-       "      <td>2.081556</td>\n",
-       "      <td>1.711501</td>\n",
-       "      <td>1.788340</td>\n",
-       "      <td>1.874007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.457755</td>\n",
-       "      <td>2.581206</td>\n",
-       "      <td>2.042745</td>\n",
-       "      <td>2.161676</td>\n",
-       "      <td>2.360556</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.872336</td>\n",
-       "      <td>3.102654</td>\n",
-       "      <td>2.599425</td>\n",
-       "      <td>2.539433</td>\n",
-       "      <td>2.828806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       complex_score  transe_score  textEmb_score  BERT_abs_label_score  \\\n",
-       "count     100.000000    100.000000     100.000000            100.000000   \n",
-       "mean        2.373734      2.467629       2.171393              2.162535   \n",
-       "std         0.897240      0.942942       0.727955              0.594187   \n",
-       "min        -1.000000     -1.000000      -1.000000              1.000000   \n",
-       "25%         1.926772      2.081556       1.711501              1.788340   \n",
-       "50%         2.457755      2.581206       2.042745              2.161676   \n",
-       "75%         2.872336      3.102654       2.599425              2.539433   \n",
-       "max         4.000000      4.000000       4.000000              4.000000   \n",
-       "\n",
-       "       BERT_abs_firstSent_label_score  \n",
-       "count                      100.000000  \n",
-       "mean                         2.348227  \n",
-       "std                          0.658818  \n",
-       "min                          1.000000  \n",
-       "25%                          1.874007  \n",
-       "50%                          2.360556  \n",
-       "75%                          2.828806  \n",
-       "max                          4.000000  "
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "sample_wikiDF[scoreCols].describe()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "id": "guilty-berkeley",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     75\n",
-       "context          6\n",
-       "meronymy         5\n",
-       "distinctness     4\n",
-       "utility          3\n",
-       "antonymy         2\n",
-       "synonymy         1\n",
-       "causation        1\n",
-       "ordering         1\n",
-       "material         1\n",
-       "properties       1\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample_wikiDF.dim.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "funded-geology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "cnts = []\n",
-    "for col in scoreCols:\n",
-    "    cnts.append(sample_wikiDF[sample_wikiDF[col] > -1].groupby('dim')[col].agg('count'))\n",
-    "cnts = pd.concat(cnts, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "id": "threaded-writer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>74</td>\n",
-       "      <td>74</td>\n",
-       "      <td>75</td>\n",
-       "      <td>75</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             2              2   \n",
-       "causation                 1             1              1   \n",
-       "context                   6             6              6   \n",
-       "distinctness              3             3              3   \n",
-       "inheritance              74            74             75   \n",
-       "material                  1             1              1   \n",
-       "meronymy                  4             4              5   \n",
-       "ordering                  1             1              1   \n",
-       "properties                1             1              1   \n",
-       "synonymy                  1             1              1   \n",
-       "utility                   3             3              3   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         2                               2  \n",
-       "causation                        1                               1  \n",
-       "context                          6                               6  \n",
-       "distinctness                     4                               4  \n",
-       "inheritance                     75                              75  \n",
-       "material                         1                               1  \n",
-       "meronymy                         5                               5  \n",
-       "ordering                         1                               1  \n",
-       "properties                       1                               1  \n",
-       "synonymy                         1                               1  \n",
-       "utility                          3                               3  "
-      ]
-     },
-     "execution_count": 251,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "weird-employer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                      97\n",
-       "transe_score                       97\n",
-       "textEmb_score                      99\n",
-       "BERT_abs_label_score              100\n",
-       "BERT_abs_firstSent_label_score    100\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "excellent-principal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                     0.97\n",
-       "transe_score                      0.97\n",
-       "textEmb_score                     0.99\n",
-       "BERT_abs_label_score              1.00\n",
-       "BERT_abs_firstSent_label_score    1.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()/len(sample_wikiDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
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-       "      <th>dim</th>\n",
-       "      <th></th>\n",
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-       "      <th>antonymy</th>\n",
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-       "      <td>1.808202</td>\n",
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-       "      <th>distinctness</th>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>0.309635</td>\n",
-       "      <td>0.496549</td>\n",
-       "      <td>0.469327</td>\n",
-       "      <td>0.541985</td>\n",
-       "      <td>0.448966</td>\n",
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-       "      <th>ordering</th>\n",
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-       "      <th>properties</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>0.516512</td>\n",
-       "      <td>0.497344</td>\n",
-       "      <td>0.489440</td>\n",
-       "      <td>0.809166</td>\n",
-       "      <td>0.995372</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "</div>"
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-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           0.664200      0.078457       1.808202   \n",
-       "causation               NaN           NaN            NaN   \n",
-       "context            0.608626      0.665737       1.014546   \n",
-       "distinctness       0.478050      0.832449       0.247507   \n",
-       "inheritance        0.677163      0.717881       0.591921   \n",
-       "material                NaN           NaN            NaN   \n",
-       "meronymy           0.309635      0.496549       0.469327   \n",
-       "ordering                NaN           NaN            NaN   \n",
-       "properties              NaN           NaN            NaN   \n",
-       "synonymy                NaN           NaN            NaN   \n",
-       "utility            0.516512      0.497344       0.489440   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  1.247541                        1.091346  \n",
-       "causation                      NaN                             NaN  \n",
-       "context                   0.713613                        0.737005  \n",
-       "distinctness              0.662065                        0.737140  \n",
-       "inheritance               0.558481                        0.625973  \n",
-       "material                       NaN                             NaN  \n",
-       "meronymy                  0.541985                        0.448966  \n",
-       "ordering                       NaN                             NaN  \n",
-       "properties                     NaN                             NaN  \n",
-       "synonymy                       NaN                             NaN  \n",
-       "utility                   0.809166                        0.995372  "
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summsStd = []\n",
-    "for col in scoreCols:\n",
-    "    summsStd.append(sample_wikiDF[(sample_wikiDF[col] > -1) & ~(sample_wikiDF[col].isna())].groupby('dim')[col].agg('std'))\n",
-    "summsStd = pd.concat(summsStd, axis=1)\n",
-    "summsStd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "bulgarian-essence",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>1.641262</td>\n",
-       "      <td>1.635409</td>\n",
-       "      <td>2.535406</td>\n",
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-       "      <td>2.550744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>2.803628</td>\n",
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-       "      <th>context</th>\n",
-       "      <td>2.733342</td>\n",
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-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>1.532177</td>\n",
-       "      <td>1.746123</td>\n",
-       "      <td>1.437266</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>2.564439</td>\n",
-       "      <td>2.598065</td>\n",
-       "      <td>2.190634</td>\n",
-       "      <td>2.110764</td>\n",
-       "      <td>2.324834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>2.501223</td>\n",
-       "      <td>2.950043</td>\n",
-       "      <td>3.033550</td>\n",
-       "      <td>2.970278</td>\n",
-       "      <td>3.233506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>2.344857</td>\n",
-       "      <td>2.852795</td>\n",
-       "      <td>2.102023</td>\n",
-       "      <td>1.742755</td>\n",
-       "      <td>1.817167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1.531941</td>\n",
-       "      <td>1.496260</td>\n",
-       "      <td>1.084469</td>\n",
-       "      <td>2.612951</td>\n",
-       "      <td>2.893667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>2.118738</td>\n",
-       "      <td>3.175830</td>\n",
-       "      <td>3.486557</td>\n",
-       "      <td>2.236175</td>\n",
-       "      <td>2.988376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1.524502</td>\n",
-       "      <td>1.543396</td>\n",
-       "      <td>2.030404</td>\n",
-       "      <td>2.199188</td>\n",
-       "      <td>2.016465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2.155438</td>\n",
-       "      <td>3.004060</td>\n",
-       "      <td>2.201133</td>\n",
-       "      <td>2.468939</td>\n",
-       "      <td>2.770659</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           1.641262      1.635409       2.535406   \n",
-       "causation          2.803628      2.030427       2.769243   \n",
-       "context            2.733342      2.896265       2.490245   \n",
-       "distinctness       1.532177      1.746123       1.437266   \n",
-       "inheritance        2.564439      2.598065       2.190634   \n",
-       "material           2.501223      2.950043       3.033550   \n",
-       "meronymy           2.344857      2.852795       2.102023   \n",
-       "ordering           1.531941      1.496260       1.084469   \n",
-       "properties         2.118738      3.175830       3.486557   \n",
-       "synonymy           1.524502      1.543396       2.030404   \n",
-       "utility            2.155438      3.004060       2.201133   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  2.525113                        2.550744  \n",
-       "causation                 2.061884                        2.102512  \n",
-       "context                   2.657254                        2.815580  \n",
-       "distinctness              2.187860                        1.958226  \n",
-       "inheritance               2.110764                        2.324834  \n",
-       "material                  2.970278                        3.233506  \n",
-       "meronymy                  1.742755                        1.817167  \n",
-       "ordering                  2.612951                        2.893667  \n",
-       "properties                2.236175                        2.988376  \n",
-       "synonymy                  2.199188                        2.016465  \n",
-       "utility                   2.468939                        2.770659  "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summs = []\n",
-    "for col in scoreCols:\n",
-    "    summs.append(sample_wikiDF[(sample_wikiDF[col] > -1) & ~(sample_wikiDF[col].isna())].groupby('dim')[col].agg('mean'))\n",
-    "summs = pd.concat(summs, axis=1)\n",
-    "summs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "elegant-statement",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dim\n",
-       "distinctness    1.772331\n",
-       "synonymy        1.862791\n",
-       "ordering        1.923858\n",
-       "meronymy        2.171920\n",
-       "antonymy        2.177587\n",
-       "causation       2.353539\n",
-       "inheritance     2.357747\n",
-       "utility         2.520046\n",
-       "context         2.718537\n",
-       "properties      2.801135\n",
-       "material        2.937720\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summs.apply(lambda p: np.mean(list(filter(lambda y12: y12 != -1, [p[x] if not(pd.isna(p[x])) else -1 for x in scoreCols]))), axis=1).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "equal-headset",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             1              4   \n",
-       "causation                 5             1              4   \n",
-       "context                   3             5              1   \n",
-       "distinctness              2             3              1   \n",
-       "inheritance               4             5              2   \n",
-       "material                  1             2              4   \n",
-       "meronymy                  4             5              3   \n",
-       "ordering                  3             2              1   \n",
-       "properties                1             4              5   \n",
-       "synonymy                  1             2              4   \n",
-       "utility                   1             5              2   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         3                               5  \n",
-       "causation                        2                               3  \n",
-       "context                          2                               4  \n",
-       "distinctness                     5                               4  \n",
-       "inheritance                      1                               3  \n",
-       "material                         3                               5  \n",
-       "meronymy                         1                               2  \n",
-       "ordering                         4                               5  \n",
-       "properties                       2                               3  \n",
-       "synonymy                         5                               3  \n",
-       "utility                          3                               4  "
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosRowWise(row):\n",
-    "    return (np.array([row[col] for col in scoreCols])).argsort().argsort() + 1\n",
-    "pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "funky-acting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>11</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>9</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>5</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>6</td>\n",
-       "      <td>10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  4             3              8   \n",
-       "causation                11             5              9   \n",
-       "context                  10             8              7   \n",
-       "distinctness              3             4              2   \n",
-       "inheritance               9             6              5   \n",
-       "material                  8             9             10   \n",
-       "meronymy                  7             7              4   \n",
-       "ordering                  2             1              1   \n",
-       "properties                5            11             11   \n",
-       "synonymy                  1             2              3   \n",
-       "utility                   6            10              6   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         8                               6  \n",
-       "causation                        2                               4  \n",
-       "context                         10                               8  \n",
-       "distinctness                     4                               2  \n",
-       "inheritance                      3                               5  \n",
-       "material                        11                              11  \n",
-       "meronymy                         1                               1  \n",
-       "ordering                         9                               9  \n",
-       "properties                       6                              10  \n",
-       "synonymy                         5                               3  \n",
-       "utility                          7                               7  "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosColWise(colVals):\n",
-    "    return pd.Series(((np.array([colVals.to_list()])).argsort().argsort() + 1)[0], index=summs.index, name=colVals.name)\n",
-    "pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "filled-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "armed-incidence",
-   "metadata": {},
-   "source": [
-    "# Retrofitting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "id": "satisfied-china",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "\n",
-    "def normalizeTargetScores(val1, minVal, maxVal):\n",
-    "    return (val1 - minVal) / (maxVal - minVal) if val1 != -1 else -1\n",
-    "\n",
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict\n",
-    "\n",
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)\n",
-    "\n",
-    "def standardizeDataset(df, targetCol, val1):\n",
-    "#     df1 = df[df[targetCol] > -1]\n",
-    "    df1 = df.copy()\n",
-    "    df1['bert2SentSim'] = val1\n",
-    "#     df1['bert2SentSim'] = df1[targetCol].apply(normalizeTargetScores, args=(df1[targetCol].where(df1[targetCol] != -1).min(), df1[targetCol].max()))\n",
-    "#     print(df1[targetCol].describe())\n",
-    "#     print(df1[targetCol+'_norm'].describe())\n",
-    "    return df1\n",
-    "\n",
-    "def fetchNeighbours(df):\n",
-    "    \n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row['bert2SentSim']))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row['bert2SentSim']))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict\n",
-    "\n",
-    "def retrofit(embedDict, neighDict, weightCase):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "            sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict\n",
-    "\n",
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "id": "enhanced-journalist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "\n",
-    "LABELS = ['I','U','M']\n",
-    "\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')\n",
-    "    print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "    print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "    print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "    print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    return wordSimMissingSet, cm_old, cm_new, accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category']), accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "id": "dried-subcommittee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transeEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')\n",
-    "transeEmbeddingsDF['transe_embedding'] = transeEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "transeEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "id": "italic-stations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transeEmbeddings = {}\n",
-    "for _, row in transeEmbeddingsDF.iterrows():\n",
-    "    transeEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "id": "imperial-baptist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>[-0.21400201320648193, -0.1853264421224594, 2....</td>\n",
-       "      <td>0.111954</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.533456</td>\n",
-       "      <td>3.459105</td>\n",
-       "      <td>3.219849</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.066402</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.623771</td>\n",
-       "      <td>3.430562</td>\n",
-       "      <td>3.333714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6716414093971252, -0.08505986630916595, 1.6...</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.701743</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.483466</td>\n",
-       "      <td>1.746096</td>\n",
-       "      <td>1.745552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.11234442889690399, -0.34427452087402344, 1....</td>\n",
-       "      <td>[0.1607293337583542, -0.1969822645187378, 2.40...</td>\n",
-       "      <td>0.546000</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.084781</td>\n",
-       "      <td>2.135693</td>\n",
-       "      <td>2.134864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>[-0.03644487261772156, -0.5494412183761597, 2....</td>\n",
-       "      <td>0.372485</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.409778</td>\n",
-       "      <td>3.333835</td>\n",
-       "      <td>2.568600</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                        BERT_abs_firstSent_label_emb  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....   \n",
-       "2  [0.6716414093971252, -0.08505986630916595, 1.6...   \n",
-       "3  [0.11234442889690399, -0.34427452087402344, 1....   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...   \n",
-       "\n",
-       "                  BERT_abs_firstSent_label_emb_right  \\\n",
-       "0  [-0.21400201320648193, -0.1853264421224594, 2....   \n",
-       "1  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "2  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "3  [0.1607293337583542, -0.1969822645187378, 2.40...   \n",
-       "4  [-0.03644487261772156, -0.5494412183761597, 2....   \n",
-       "\n",
-       "  BERT_abs_firstSent_label_cosSim BERT_abstract_score  \\\n",
-       "0                        0.111954                -1.0   \n",
-       "1                        0.066402                -1.0   \n",
-       "2                        0.701743                -1.0   \n",
-       "3                        0.546000                -1.0   \n",
-       "4                        0.372485                -1.0   \n",
-       "\n",
-       "  BERT_abstract_firstSent_score complex_score transe_score textEmb_score  \\\n",
-       "0                          -1.0          -1.0         -1.0      2.533456   \n",
-       "1                          -1.0          -1.0         -1.0      2.623771   \n",
-       "2                          -1.0          -1.0         -1.0      2.483466   \n",
-       "3                          -1.0          -1.0         -1.0      2.084781   \n",
-       "4                          -1.0          -1.0         -1.0      2.409778   \n",
-       "\n",
-       "  BERT_abs_label_score BERT_abs_firstSent_label_score  \n",
-       "0             3.459105                       3.219849  \n",
-       "1             3.430562                       3.333714  \n",
-       "2             1.746096                       1.745552  \n",
-       "3             2.135693                       2.134864  \n",
-       "4             3.333835                       2.568600  \n",
-       "\n",
-       "[5 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "id": "mexican-dispatch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    2.000000\n",
-      "mean    -0.031714\n",
-      "std      0.002003\n",
-      "min     -0.033131\n",
-      "25%     -0.032423\n",
-      "50%     -0.031714\n",
-      "75%     -0.031006\n",
-      "max     -0.030298\n",
-      "Name: complex_cosSim, dtype: float64\n",
-      "count    12.000000\n",
-      "mean     -0.063752\n",
-      "std       0.041979\n",
-      "min      -0.137044\n",
-      "25%      -0.093370\n",
-      "50%      -0.051462\n",
-      "75%      -0.039529\n",
-      "max      -0.009669\n",
-      "Name: transe_cosSim, dtype: float64\n",
-      "count    0.0\n",
-      "mean     NaN\n",
-      "std      NaN\n",
-      "min      NaN\n",
-      "25%      NaN\n",
-      "50%      NaN\n",
-      "75%      NaN\n",
-      "max      NaN\n",
-      "Name: textEmb_cosSim, dtype: float64\n",
-      "count    313.000000\n",
-      "mean      -0.041530\n",
-      "std        0.036422\n",
-      "min       -0.181201\n",
-      "25%       -0.059406\n",
-      "50%       -0.031702\n",
-      "75%       -0.013665\n",
-      "max       -0.000024\n",
-      "Name: BERT_abs_label_cosSim, dtype: float64\n",
-      "count    79.000000\n",
-      "mean     -0.035752\n",
-      "std       0.029876\n",
-      "min      -0.122427\n",
-      "25%      -0.054458\n",
-      "50%      -0.026226\n",
-      "75%      -0.009441\n",
-      "max      -0.000182\n",
-      "Name: BERT_abs_firstSent_label_cosSim, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "cosSimCols = ['complex_cosSim', 'transe_cosSim', 'textEmb_cosSim', 'BERT_abs_label_cosSim', 'BERT_abs_firstSent_label_cosSim']\n",
-    "repelDims = {'other', 'meronymy', 'context', 'material', 'properties', 'utility'}\n",
-    "for col1 in cosSimCols:\n",
-    "#     print((wikiDF_wabs1[col1] == -1).sum())\n",
-    "    print(wikiDF_wabs1[(wikiDF_wabs1[col1] > -1) & (wikiDF_wabs1[col1] < 0) & (wikiDF_wabs1.dim.apply(lambda p: p not in repelDims))][col1].describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "id": "talented-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "id": "expensive-warner",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n",
-      "374\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.504525e-03\n",
-      "std      4.784244e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      4.424263e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24716970867633406, pvalue=7.762158676245256e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22502397615435152, pvalue=5.730424928972872e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6034985422740525\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    1.910100e+04\n",
-      "mean     6.937777e-03\n",
-      "std      4.561719e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      4.424263e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24843225530937377, pvalue=6.217152652954295e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22380720194731576, pvalue=7.083559265864719e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "374\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     2.274155e-04\n",
-      "std      1.449770e-03\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      1.340686e-02\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2507469034952738, pvalue=4.1314425443641745e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22557152137805614, pvalue=5.206423573456538e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    1.910100e+04\n",
-      "mean     2.120965e-04\n",
-      "std      1.386917e-03\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      1.340686e-02\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2510476149348689, pvalue=3.912200370865155e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2257539655761838, pvalue=5.037446475691195e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327801\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23983260775448598, pvalue=2.752926819013835e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21456745507793118, pvalue=3.4267954366117823e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237342\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.244711271374783, pvalue=1.194655795369227e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22603182014110224, pvalue=4.814978154680426e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.329680\n",
-      "std          0.085445\n",
-      "min          0.000000\n",
-      "25%          0.266447\n",
-      "50%          0.323794\n",
-      "75%          0.390543\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24010565224095312, pvalue=2.6288081698945844e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21420995798618558, pvalue=3.6387685007723226e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6530612244897959\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.238297\n",
-      "std          0.149913\n",
-      "min          0.000000\n",
-      "25%          0.093200\n",
-      "50%          0.228559\n",
-      "75%          0.374263\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.244711271374783, pvalue=1.194655795369227e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22445752023550897, pvalue=6.343021648612184e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.329605\n",
-      "std          0.085511\n",
-      "min          0.000000\n",
-      "25%          0.266179\n",
-      "50%          0.323686\n",
-      "75%          0.390543\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23974431943095922, pvalue=2.7955198144322416e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21383352064488867, pvalue=3.87455333255265e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6530612244897959\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.238361\n",
-      "std          0.149862\n",
-      "min          0.000000\n",
-      "25%          0.093316\n",
-      "50%          0.228559\n",
-      "75%          0.374263\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24453059264517965, pvalue=1.2326605897936912e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2245944158794736, pvalue=6.193255676859552e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "results1 = []\n",
-    "confusionMatrixMaster = {}\n",
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "\n",
-    "\n",
-    "embedDict = normalize(transeEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "\n",
-    "# Version 1: 0.25 for 6 dims of Wiki CS\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.25)\n",
-    "    neighDict = fetchNeighbours(wikiCSDF)\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['wikiCS_0.25_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, 'wikiCS_0.25',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 2: 0.01 for 6 dims of Wiki CS\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.01)\n",
-    "    neighDict = fetchNeighbours(wikiCSDF)\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['wikiCS_0.01_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, 'wikiCS_0.01',oldAcc, newAcc))\n",
-    "        \n",
-    "# Version 3: 19k Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "#     wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, 0.25)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 4: 19k+0.25 Wiki CS Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.25)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, wikiCSDF]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wikiCS_0.25_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k+wikiCS_0.25',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 5: 19k+0.01 Wiki CS Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.01)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, wikiCSDF]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wikiCS_0.01_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k+wikiCS_0.01',oldAcc, newAcc))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "id": "specialized-syria",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'transe_19k_old_1': array([[  9,   1,  10],\n",
-       "        [  0,  20,  83],\n",
-       "        [  2,  41, 177]]),\n",
-       " 'transe_19k_new_1': array([[ 13,   0,   7],\n",
-       "        [  0,  10,  93],\n",
-       "        [  7,  12, 201]]),\n",
-       " 'transe_19k_old_2': array([[  9,   1,  10],\n",
-       "        [  0,  20,  83],\n",
-       "        [  2,  41, 177]]),\n",
-       " 'transe_19k_new_2': array([[ 10,   1,   9],\n",
-       "        [  0,  20,  83],\n",
-       "        [  3,  39, 178]])}"
-      ]
-     },
-     "execution_count": 337,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusionMatrixMaster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "id": "damaged-education",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17187"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "id": "conservative-compact",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "280"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)].apply(lambda p: p.node1 in embedDict and p.node2 in embedDict, axis=1)).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "theoretical-delay",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "id": "swedish-cornwall",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CS_column</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>Target</th>\n",
-       "      <th>Old Accuracy</th>\n",
-       "      <th>New Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.603499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.655977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.609329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k+wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.653061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k+wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.606414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k+wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.653061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k+wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.606414</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        CS_column  weight           Target  Old Accuracy  New Accuracy\n",
-       "0  complex_cosSim       1      wikiCS_0.25      0.600583      0.603499\n",
-       "1  complex_cosSim       2      wikiCS_0.25      0.600583      0.600583\n",
-       "2  complex_cosSim       1      wikiCS_0.01      0.600583      0.600583\n",
-       "3  complex_cosSim       2      wikiCS_0.01      0.600583      0.600583\n",
-       "4  complex_cosSim       1              19k      0.600583      0.655977\n",
-       "5  complex_cosSim       2              19k      0.600583      0.609329\n",
-       "6  complex_cosSim       1  19k+wikiCS_0.25      0.600583      0.653061\n",
-       "7  complex_cosSim       2  19k+wikiCS_0.25      0.600583      0.606414\n",
-       "8  complex_cosSim       1  19k+wikiCS_0.01      0.600583      0.653061\n",
-       "9  complex_cosSim       2  19k+wikiCS_0.01      0.600583      0.606414"
-      ]
-     },
-     "execution_count": 335,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(results1, columns=['CS_column', 'weight', 'Target', 'Old Accuracy', 'New Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "id": "palestinian-strategy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb4967f59df544e2b0cb6806d06dd1bf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/10 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')\n",
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New[['word1_kg_id', 'word2_kg_id', 'Word 1', 'Word 2', 'Avg', 'category']]\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "key1 = 'transe_19k'\n",
-    "\n",
-    "for key2 in tqdm(newEmbedDictMaster.keys()):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "dressed-confidentiality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>category</th>\n",
-       "      <th>transe_19k_word1_old</th>\n",
-       "      <th>transe_19k_word2_old</th>\n",
-       "      <th>transe_19k_word1_new</th>\n",
-       "      <th>transe_19k_word2_new</th>\n",
-       "      <th>...</th>\n",
-       "      <th>wikiCS_0.25_wt_1_new_cosSim</th>\n",
-       "      <th>wikiCS_0.25_wt_2_new_cosSim</th>\n",
-       "      <th>wikiCS_0.01_wt_1_new_cosSim</th>\n",
-       "      <th>wikiCS_0.01_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.25_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.25_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.01_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.01_wt_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.056786171992006404, 0.01787462254737516, 0...</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.050833232942279184, 0.012123900440314235, ...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.03512899124845519, -0.06824488430804913, 0...</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.01589834711067725, -0.07306407013094582, 0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.017732972826050936, -0.023766644378558594, ...</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[-0.01674760225052191, -0.04519811208586652, 0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.052228162576186316, -0.07922249700554501, -...</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.051399986151325704, -0.07859687607569352, -...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>[-0.015944469265519467, 0.22193716590314821, 0...</td>\n",
-       "      <td>[-0.01835552362856266, 0.11140088837689806, -0...</td>\n",
-       "      <td>[-0.015944469265519467, 0.22193716590314821, 0...</td>\n",
-       "      <td>[-0.01835552362856266, 0.11140088837689806, -0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id   Word 1         Word 2  Avg category  \\\n",
-       "0      Q34211        Q454   Arafat          peace  3.6        U   \n",
-       "1      Q34211   Q13648784   Arafat         terror  3.6        U   \n",
-       "2       Q8333     Q178022      FBI    fingerprint  3.6        U   \n",
-       "3       Q8333   Q21004260      FBI  investigation  3.0        M   \n",
-       "4      Q13371      Q49112  Harvard           Yale  2.2        M   \n",
-       "\n",
-       "                                transe_19k_word1_old  \\\n",
-       "0  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "1  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "2  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "3  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "4  [-0.015944469265519467, 0.22193716590314821, 0...   \n",
-       "\n",
-       "                                transe_19k_word2_old  \\\n",
-       "0  [-0.056786171992006404, 0.01787462254737516, 0...   \n",
-       "1  [-0.03512899124845519, -0.06824488430804913, 0...   \n",
-       "2  [0.017732972826050936, -0.023766644378558594, ...   \n",
-       "3  [0.052228162576186316, -0.07922249700554501, -...   \n",
-       "4  [-0.01835552362856266, 0.11140088837689806, -0...   \n",
-       "\n",
-       "                                transe_19k_word1_new  \\\n",
-       "0  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "1  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "2  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "3  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "4  [-0.015944469265519467, 0.22193716590314821, 0...   \n",
-       "\n",
-       "                                transe_19k_word2_new  ...  \\\n",
-       "0  [-0.050833232942279184, 0.012123900440314235, ...  ...   \n",
-       "1  [-0.01589834711067725, -0.07306407013094582, 0...  ...   \n",
-       "2  [-0.01674760225052191, -0.04519811208586652, 0...  ...   \n",
-       "3  [0.051399986151325704, -0.07859687607569352, -...  ...   \n",
-       "4  [-0.01835552362856266, 0.11140088837689806, -0...  ...   \n",
-       "\n",
-       "   wikiCS_0.25_wt_1_new_cosSim  wikiCS_0.25_wt_2_new_cosSim  \\\n",
-       "0                     3.747795                     3.747795   \n",
-       "1                     2.955021                     2.955021   \n",
-       "2                     3.149125                     3.149125   \n",
-       "3                     3.104421                     3.104421   \n",
-       "4                     2.080022                     2.080022   \n",
-       "\n",
-       "   wikiCS_0.01_wt_1_new_cosSim  wikiCS_0.01_wt_2_new_cosSim  \\\n",
-       "0                     3.747795                     3.747795   \n",
-       "1                     2.955021                     2.955021   \n",
-       "2                     3.149125                     3.149125   \n",
-       "3                     3.104421                     3.104421   \n",
-       "4                     2.080022                     2.080022   \n",
-       "\n",
-       "   19k_wt_1_new_cosSim  19k_wt_2_new_cosSim  19k_wikiCS_0.25_wt_1_new_cosSim  \\\n",
-       "0             3.477665             3.676618                         3.477665   \n",
-       "1             2.952181             2.947066                         2.952181   \n",
-       "2             3.126209             3.126209                         3.126209   \n",
-       "3             2.943750             3.079927                         2.943750   \n",
-       "4             2.080022             2.080022                         2.080022   \n",
-       "\n",
-       "   19k_wikiCS_0.25_wt_2_new_cosSim  19k_wikiCS_0.01_wt_1_new_cosSim  \\\n",
-       "0                         3.676618                         3.477665   \n",
-       "1                         2.947066                         2.952181   \n",
-       "2                         3.126209                         3.126209   \n",
-       "3                         3.079927                         2.943750   \n",
-       "4                         2.080022                         2.080022   \n",
-       "\n",
-       "   19k_wikiCS_0.01_wt_2_new_cosSim  \n",
-       "0                         3.676618  \n",
-       "1                         2.947066  \n",
-       "2                         3.126209  \n",
-       "3                         3.079927  \n",
-       "4                         2.080022  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "associate-cleaning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['transe_19k_old_cosSim',\n",
-       " 'wikiCS_0.25_wt_1_new_cosSim',\n",
-       " 'wikiCS_0.25_wt_2_new_cosSim',\n",
-       " 'wikiCS_0.01_wt_1_new_cosSim',\n",
-       " 'wikiCS_0.01_wt_2_new_cosSim',\n",
-       " '19k_wt_1_new_cosSim',\n",
-       " '19k_wt_2_new_cosSim',\n",
-       " '19k_wikiCS_0.25_wt_1_new_cosSim',\n",
-       " '19k_wikiCS_0.25_wt_2_new_cosSim',\n",
-       " '19k_wikiCS_0.01_wt_1_new_cosSim',\n",
-       " '19k_wikiCS_0.01_wt_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 323,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "tight-location",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "scheduled-massachusetts",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "        \n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "starting-slovenia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.668655462184874"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, ['transe_19k_old_cosSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "looking-color",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cosSimNewCols = ['wikiCS_0.25_wt_1_new_cosSim',\n",
-    " 'wikiCS_0.25_wt_2_new_cosSim',\n",
-    " 'wikiCS_0.01_wt_1_new_cosSim',\n",
-    " 'wikiCS_0.01_wt_2_new_cosSim',\n",
-    " '19k_wt_1_new_cosSim',\n",
-    " '19k_wt_2_new_cosSim',\n",
-    " '19k_wikiCS_0.25_wt_1_new_cosSim',\n",
-    " '19k_wikiCS_0.25_wt_2_new_cosSim',\n",
-    " '19k_wikiCS_0.01_wt_1_new_cosSim',\n",
-    " '19k_wikiCS_0.01_wt_2_new_cosSim']\n",
-    "\n",
-    "svmResults = []\n",
-    "for col1 in cosSimNewCols:\n",
-    "    svmResults.append((col1, trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col1])*100))\n",
-    "svmResults = pd.DataFrame(svmResults, columns=['target', 'Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "id": "nutritional-chicken",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svmResults['Increase'] = svmResults['Accuracy'] - trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, ['transe_19k_old_cosSim'])*100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "id": "charitable-journalism",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>target</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>wikiCS_0.25_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>wikiCS_0.25_wt_2_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wikiCS_0.01_wt_1_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>wikiCS_0.01_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>19k_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>19k_wt_2_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>19k_wikiCS_0.25_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>19k_wikiCS_0.25_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>19k_wikiCS_0.01_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>19k_wikiCS_0.01_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            target   Accuracy  Increase\n",
-       "0      wikiCS_0.25_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "1      wikiCS_0.25_wt_2_new_cosSim  66.571429 -0.294118\n",
-       "2      wikiCS_0.01_wt_1_new_cosSim  66.865546  0.000000\n",
-       "3      wikiCS_0.01_wt_2_new_cosSim  66.865546  0.000000\n",
-       "4              19k_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "5              19k_wt_2_new_cosSim  66.571429 -0.294118\n",
-       "6  19k_wikiCS_0.25_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "7  19k_wikiCS_0.25_wt_2_new_cosSim  66.865546  0.000000\n",
-       "8  19k_wikiCS_0.01_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "9  19k_wikiCS_0.01_wt_2_new_cosSim  66.865546  0.000000"
-      ]
-     },
-     "execution_count": 332,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmResults"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "naval-avenue",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analogical Proj Experiments-Copy1.ipynb b/Analogical Proj Experiments-Copy1.ipynb
deleted file mode 100644
index c154c5e..0000000
--- a/Analogical Proj Experiments-Copy1.ipynb	
+++ /dev/null
@@ -1,3989 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "historic-drive",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !isa_file=../data/P279_dataset/wikidata-P279.tsv\n",
-    "# !labels_file=../data/labels.en.tsv\n",
-    "!kgtk query -i ../data/P279_dataset/wikidata-P279.tsv -i ../data/labels.en.tsv \\\n",
-    "--match 'i: (x)-[:P279]->(y), l: (x)-[:label]->(lbl), (y)-[:label]->(lbl2)' \\\n",
-    "--where 'lbl =~ \".* .*\" AND instr(kgtk_unstringify(kgtk_lqstring_text(lbl)), kgtk_unstringify(kgtk_lqstring_text(lbl2))) AND lbl!=lbl2' \\\n",
-    "--return 'distinct x, y, kgtk_lqstring_text_string(lbl), kgtk_lqstring_text_string(lbl2)' \\\n",
-    "-o ../data/compositional.tsv\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "geographic-andorra",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "node1\tnode2\tkgtk_lqstring_text_string(graph_2_c2.\"node2\")\tkgtk_lqstring_text_string(graph_2_c3.\"node2\")\r\n",
-      "Q1000064\tQ11016\t\"nuclear reactor technology\"\t\"technology\"\r\n",
-      "Q1000120\tQ12510\t\"maximal torus\"\t\"torus\"\r\n",
-      "Q100061973\tQ1799072\t\"presentation method\"\t\"method\"\r\n",
-      "Q1000828\tQ918385\t\"team training\"\t\"training\"\r\n",
-      "Q1000850\tQ748\t\"Buddhism in Germany\"\t\"Buddhism\"\r\n",
-      "Q1000859\tQ748\t\"Buddhism in Iceland\"\t\"Buddhism\"\r\n",
-      "Q1000861\tQ748\t\"Buddhism in Ireland\"\t\"Buddhism\"\r\n",
-      "Q1000863\tQ376\t\"master clock\"\t\"clock\"\r\n",
-      "Q1000869\tQ748\t\"Buddhism in Bangladesh\"\t\"Buddhism\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!head ../data/compositional.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "prospective-commission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "comp = pd.read_csv('../data/compositional.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "personalized-liberal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp['node1_len'] = comp.node1.apply(lambda p: len(p))\n",
-    "comp['node2_len'] = comp.node2.apply(lambda p: len(p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "naval-python",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp = comp.sort_values(by=['node1_len', 'node2_len'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "postal-baseline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "      <th>node1_len</th>\n",
-       "      <th>node2_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>67979</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55355</th>\n",
-       "      <td>Q416</td>\n",
-       "      <td>Q41298</td>\n",
-       "      <td>disk magazine</td>\n",
-       "      <td>magazine</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98710</th>\n",
-       "      <td>Q944</td>\n",
-       "      <td>Q41217</td>\n",
-       "      <td>quantum mechanics</td>\n",
-       "      <td>mechanics</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49124</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41635</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1     node2 kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "67979  Q561      Q553                                   human tooth   \n",
-       "55355  Q416    Q41298                                 disk magazine   \n",
-       "98710  Q944    Q41217                             quantum mechanics   \n",
-       "49124  Q305   Q186361                           corporal punishment   \n",
-       "41635  Q250  Q1921606                             computer keyboard   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  node1_len  node2_len  \n",
-       "67979                                         tooth          4          4  \n",
-       "55355                                      magazine          4          6  \n",
-       "98710                                     mechanics          4          6  \n",
-       "49124                                    punishment          4          7  \n",
-       "41635                                      keyboard          4          8  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "median-singer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tabulate import tabulate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "charitable-abraham",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "      <th>node1_len</th>\n",
-       "      <th>node2_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>94514</th>\n",
-       "      <td>Q8141</td>\n",
-       "      <td>Q9288</td>\n",
-       "      <td>Modern Hebrew</td>\n",
-       "      <td>Hebrew</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41635</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93984</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98024</th>\n",
-       "      <td>Q9192</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Mandarin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67979</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69745</th>\n",
-       "      <td>Q5856</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>synthetic fiber</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87295</th>\n",
-       "      <td>Q6754</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>charm quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57146</th>\n",
-       "      <td>Q4543</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>beach volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99864</th>\n",
-       "      <td>Q9778</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>electronic music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88511</th>\n",
-       "      <td>Q699</td>\n",
-       "      <td>Q17991521</td>\n",
-       "      <td>fairy tale</td>\n",
-       "      <td>tale</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43353</th>\n",
-       "      <td>Q2642</td>\n",
-       "      <td>Q2638</td>\n",
-       "      <td>exponential-Golomb coding</td>\n",
-       "      <td>Golomb coding</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98991</th>\n",
-       "      <td>Q9585</td>\n",
-       "      <td>Q432</td>\n",
-       "      <td>Shia Islam</td>\n",
-       "      <td>Islam</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87447</th>\n",
-       "      <td>Q6786</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>bottom quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89314</th>\n",
-       "      <td>Q7141</td>\n",
-       "      <td>Q420</td>\n",
-       "      <td>cell biology</td>\n",
-       "      <td>biology</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69759</th>\n",
-       "      <td>Q5860</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>carbon fibers</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73413</th>\n",
-       "      <td>Q6311</td>\n",
-       "      <td>Q8104</td>\n",
-       "      <td>stellar atmosphere</td>\n",
-       "      <td>atmosphere</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95355</th>\n",
-       "      <td>Q8494</td>\n",
-       "      <td>Q8481</td>\n",
-       "      <td>human sternum</td>\n",
-       "      <td>sternum</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55941</th>\n",
-       "      <td>Q426</td>\n",
-       "      <td>Q2386606</td>\n",
-       "      <td>animal rights</td>\n",
-       "      <td>right</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49124</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94475</th>\n",
-       "      <td>Q8124</td>\n",
-       "      <td>Q58</td>\n",
-       "      <td>human penis</td>\n",
-       "      <td>penis</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94875</th>\n",
-       "      <td>Q8345</td>\n",
-       "      <td>Q8343</td>\n",
-       "      <td>bass clarinet</td>\n",
-       "      <td>clarinet</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58838</th>\n",
-       "      <td>Q4812</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>day sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18599</th>\n",
-       "      <td>Q1827</td>\n",
-       "      <td>Q282</td>\n",
-       "      <td>red wine</td>\n",
-       "      <td>wine</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88919</th>\n",
-       "      <td>Q7076</td>\n",
-       "      <td>Q6199</td>\n",
-       "      <td>individualist anarchism</td>\n",
-       "      <td>anarchism</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3014</th>\n",
-       "      <td>Q1111</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>electric charge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1      node2 kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "94514  Q8141      Q9288                                 Modern Hebrew   \n",
-       "41635   Q250   Q1921606                             computer keyboard   \n",
-       "93984  Q7991       Q336                               natural science   \n",
-       "98024  Q9192      Q7850                              Mandarin Chinese   \n",
-       "67979   Q561       Q553                                   human tooth   \n",
-       "69745  Q5856       Q161                               synthetic fiber   \n",
-       "87295  Q6754      Q6718                                   charm quark   \n",
-       "57146  Q4543      Q1734                              beach volleyball   \n",
-       "99864  Q9778       Q638                              electronic music   \n",
-       "88511   Q699  Q17991521                                    fairy tale   \n",
-       "43353  Q2642      Q2638                     exponential-Golomb coding   \n",
-       "98991  Q9585       Q432                                    Shia Islam   \n",
-       "87447  Q6786      Q6718                                  bottom quark   \n",
-       "89314  Q7141       Q420                                  cell biology   \n",
-       "69759  Q5860       Q161                                 carbon fibers   \n",
-       "73413  Q6311      Q8104                            stellar atmosphere   \n",
-       "95355  Q8494      Q8481                                 human sternum   \n",
-       "55941   Q426   Q2386606                                 animal rights   \n",
-       "49124   Q305    Q186361                           corporal punishment   \n",
-       "94475  Q8124        Q58                                   human penis   \n",
-       "94875  Q8345      Q8343                                 bass clarinet   \n",
-       "58838  Q4812       Q527                                       day sky   \n",
-       "18599  Q1827       Q282                                      red wine   \n",
-       "88919  Q7076      Q6199                       individualist anarchism   \n",
-       "3014   Q1111     Q73792                               electric charge   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  node1_len  node2_len  \n",
-       "94514                                        Hebrew          5          5  \n",
-       "41635                                      keyboard          4          8  \n",
-       "93984                                       science          5          4  \n",
-       "98024                                       Chinese          5          5  \n",
-       "67979                                         tooth          4          4  \n",
-       "69745                                         fiber          5          4  \n",
-       "87295                                         quark          5          5  \n",
-       "57146                                    volleyball          5          5  \n",
-       "99864                                         music          5          4  \n",
-       "88511                                          tale          4          9  \n",
-       "43353                                 Golomb coding          5          5  \n",
-       "98991                                         Islam          5          4  \n",
-       "87447                                         quark          5          5  \n",
-       "89314                                       biology          5          4  \n",
-       "69759                                         fiber          5          4  \n",
-       "73413                                    atmosphere          5          5  \n",
-       "95355                                       sternum          5          5  \n",
-       "55941                                         right          4          8  \n",
-       "49124                                    punishment          4          7  \n",
-       "94475                                         penis          5          3  \n",
-       "94875                                      clarinet          5          5  \n",
-       "58838                                           sky          5          4  \n",
-       "18599                                          wine          5          4  \n",
-       "88919                                     anarchism          5          5  \n",
-       "3014                                         charge          5          6  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp[:50].sample(n=25, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "random-dress",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       node1    node2      kgtk_lqstring_text_string(graph_2_c2.\"node2\")    kgtk_lqstring_text_string(graph_2_c3.\"node2\")      node1_len    node2_len\n",
-      "-----  -------  ---------  -----------------------------------------------  -----------------------------------------------  -----------  -----------\n",
-      "94514  Q8141    Q9288      Modern Hebrew                                    Hebrew                                                     5            5\n",
-      "41635  Q250     Q1921606   computer keyboard                                keyboard                                                   4            8\n",
-      "93984  Q7991    Q336       natural science                                  science                                                    5            4\n",
-      "98024  Q9192    Q7850      Mandarin Chinese                                 Chinese                                                    5            5\n",
-      "67979  Q561     Q553       human tooth                                      tooth                                                      4            4\n",
-      "69745  Q5856    Q161       synthetic fiber                                  fiber                                                      5            4\n",
-      "87295  Q6754    Q6718      charm quark                                      quark                                                      5            5\n",
-      "57146  Q4543    Q1734      beach volleyball                                 volleyball                                                 5            5\n",
-      "99864  Q9778    Q638       electronic music                                 music                                                      5            4\n",
-      "88511  Q699     Q17991521  fairy tale                                       tale                                                       4            9\n",
-      "43353  Q2642    Q2638      exponential-Golomb coding                        Golomb coding                                              5            5\n",
-      "98991  Q9585    Q432       Shia Islam                                       Islam                                                      5            4\n",
-      "87447  Q6786    Q6718      bottom quark                                     quark                                                      5            5\n",
-      "89314  Q7141    Q420       cell biology                                     biology                                                    5            4\n",
-      "69759  Q5860    Q161       carbon fibers                                    fiber                                                      5            4\n",
-      "73413  Q6311    Q8104      stellar atmosphere                               atmosphere                                                 5            5\n",
-      "95355  Q8494    Q8481      human sternum                                    sternum                                                    5            5\n",
-      "55941  Q426     Q2386606   animal rights                                    right                                                      4            8\n",
-      "49124  Q305     Q186361    corporal punishment                              punishment                                                 4            7\n",
-      "94475  Q8124    Q58        human penis                                      penis                                                      5            3\n",
-      "94875  Q8345    Q8343      bass clarinet                                    clarinet                                                   5            5\n",
-      "58838  Q4812    Q527       day sky                                          sky                                                        5            4\n",
-      "18599  Q1827    Q282       red wine                                         wine                                                       5            4\n",
-      "88919  Q7076    Q6199      individualist anarchism                          anarchism                                                  5            5\n",
-      " 3014  Q1111    Q73792     electric charge                                  charge                                                     5            6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tabulate(comp[:50].sample(n=25, random_state=13), headers = 'keys', tablefmt = 'simple'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "governing-execution",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c2.\"node2\")</th>\n",
-       "      <th>kgtk_lqstring_text_string(graph_2_c3.\"node2\")</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>76391</th>\n",
-       "      <td>Q66502527</td>\n",
-       "      <td>Q27058096</td>\n",
-       "      <td>right fifth intercostal nerve</td>\n",
-       "      <td>fifth intercostal nerve</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64439</th>\n",
-       "      <td>Q5453438</td>\n",
-       "      <td>Q392316</td>\n",
-       "      <td>First Nations in Alberta</td>\n",
-       "      <td>First Nations</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54764</th>\n",
-       "      <td>Q3998922</td>\n",
-       "      <td>Q43304311</td>\n",
-       "      <td>clarinet-violin-piano trio</td>\n",
-       "      <td>piano trio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71844</th>\n",
-       "      <td>Q60687320</td>\n",
-       "      <td>Q29385978</td>\n",
-       "      <td>President of Communal Council of Tremithousa</td>\n",
-       "      <td>President of Communal Council</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26611</th>\n",
-       "      <td>Q21116244</td>\n",
-       "      <td>Q14878311</td>\n",
-       "      <td>glutamate catabolic process to 2-oxoglutarate</td>\n",
-       "      <td>glutamate catabolic process</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76415</th>\n",
-       "      <td>Q66502641</td>\n",
-       "      <td>Q66502435</td>\n",
-       "      <td>medial branch of cervicothoracic ganglion</td>\n",
-       "      <td>branch of cervicothoracic ganglion</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24160</th>\n",
-       "      <td>Q20901636</td>\n",
-       "      <td>Q705135</td>\n",
-       "      <td>Grand Officer of the Order of Adolphe of Nassau</td>\n",
-       "      <td>Order of Adolphe of Nassau</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27271</th>\n",
-       "      <td>Q21127225</td>\n",
-       "      <td>Q133492</td>\n",
-       "      <td>letters of Pliny the Younger</td>\n",
-       "      <td>letter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3924</th>\n",
-       "      <td>Q1152224</td>\n",
-       "      <td>Q12050496</td>\n",
-       "      <td>distinctive feature</td>\n",
-       "      <td>feature</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84513</th>\n",
-       "      <td>Q66571241</td>\n",
-       "      <td>Q66570136</td>\n",
-       "      <td>right cuneate fasciculus of medulla</td>\n",
-       "      <td>cuneate fasciculus of medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92282</th>\n",
-       "      <td>Q7624573</td>\n",
-       "      <td>Q1084</td>\n",
-       "      <td>strong noun</td>\n",
-       "      <td>noun</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23304</th>\n",
-       "      <td>Q20205194</td>\n",
-       "      <td>Q1644573</td>\n",
-       "      <td>Orthodox pilgrimage</td>\n",
-       "      <td>pilgrimage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9611</th>\n",
-       "      <td>Q14860562</td>\n",
-       "      <td>Q14862215</td>\n",
-       "      <td>positive regulation of cell motility</td>\n",
-       "      <td>regulation of cell motility</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92883</th>\n",
-       "      <td>Q77584712</td>\n",
-       "      <td>Q837924</td>\n",
-       "      <td>skew-symmetric bilinear form</td>\n",
-       "      <td>bilinear form</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42826</th>\n",
-       "      <td>Q2593298</td>\n",
-       "      <td>Q1319755</td>\n",
-       "      <td>European Speed Skating Championships for Men</td>\n",
-       "      <td>European Speed Skating Championships</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29359</th>\n",
-       "      <td>Q2213536</td>\n",
-       "      <td>Q193343</td>\n",
-       "      <td>extensional tectonics</td>\n",
-       "      <td>tectonics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38430</th>\n",
-       "      <td>Q22328939</td>\n",
-       "      <td>Q22328937</td>\n",
-       "      <td>nuclear subtelomeric heterochromatin</td>\n",
-       "      <td>subtelomeric heterochromatin</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51013</th>\n",
-       "      <td>Q32861734</td>\n",
-       "      <td>Q5758703</td>\n",
-       "      <td>Western Highland Chatino</td>\n",
-       "      <td>Highland Chatino</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70167</th>\n",
-       "      <td>Q5898386</td>\n",
-       "      <td>Q105688</td>\n",
-       "      <td>hypobaric hypoxia</td>\n",
-       "      <td>hypoxia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7736</th>\n",
-       "      <td>Q13631670</td>\n",
-       "      <td>Q588723</td>\n",
-       "      <td>Passenger rail transport in China</td>\n",
-       "      <td>rail transport in China</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           node1      node2    kgtk_lqstring_text_string(graph_2_c2.\"node2\")  \\\n",
-       "76391  Q66502527  Q27058096                    right fifth intercostal nerve   \n",
-       "64439   Q5453438    Q392316                         First Nations in Alberta   \n",
-       "54764   Q3998922  Q43304311                       clarinet-violin-piano trio   \n",
-       "71844  Q60687320  Q29385978     President of Communal Council of Tremithousa   \n",
-       "26611  Q21116244  Q14878311    glutamate catabolic process to 2-oxoglutarate   \n",
-       "76415  Q66502641  Q66502435        medial branch of cervicothoracic ganglion   \n",
-       "24160  Q20901636    Q705135  Grand Officer of the Order of Adolphe of Nassau   \n",
-       "27271  Q21127225    Q133492                     letters of Pliny the Younger   \n",
-       "3924    Q1152224  Q12050496                              distinctive feature   \n",
-       "84513  Q66571241  Q66570136              right cuneate fasciculus of medulla   \n",
-       "92282   Q7624573      Q1084                                      strong noun   \n",
-       "23304  Q20205194   Q1644573                              Orthodox pilgrimage   \n",
-       "9611   Q14860562  Q14862215             positive regulation of cell motility   \n",
-       "92883  Q77584712    Q837924                     skew-symmetric bilinear form   \n",
-       "42826   Q2593298   Q1319755     European Speed Skating Championships for Men   \n",
-       "29359   Q2213536    Q193343                            extensional tectonics   \n",
-       "38430  Q22328939  Q22328937             nuclear subtelomeric heterochromatin   \n",
-       "51013  Q32861734   Q5758703                         Western Highland Chatino   \n",
-       "70167   Q5898386    Q105688                                hypobaric hypoxia   \n",
-       "7736   Q13631670    Q588723                Passenger rail transport in China   \n",
-       "\n",
-       "      kgtk_lqstring_text_string(graph_2_c3.\"node2\")  \n",
-       "76391                       fifth intercostal nerve  \n",
-       "64439                                 First Nations  \n",
-       "54764                                    piano trio  \n",
-       "71844                 President of Communal Council  \n",
-       "26611                   glutamate catabolic process  \n",
-       "76415            branch of cervicothoracic ganglion  \n",
-       "24160                    Order of Adolphe of Nassau  \n",
-       "27271                                        letter  \n",
-       "3924                                        feature  \n",
-       "84513                 cuneate fasciculus of medulla  \n",
-       "92282                                          noun  \n",
-       "23304                                    pilgrimage  \n",
-       "9611                    regulation of cell motility  \n",
-       "92883                                 bilinear form  \n",
-       "42826          European Speed Skating Championships  \n",
-       "29359                                     tectonics  \n",
-       "38430                  subtelomeric heterochromatin  \n",
-       "51013                              Highland Chatino  \n",
-       "70167                                       hypoxia  \n",
-       "7736                        rail transport in China  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comp.sample(n=20, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "bacterial-commerce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       node1      node2      kgtk_lqstring_text_string(graph_2_c2.\"node2\")    kgtk_lqstring_text_string(graph_2_c3.\"node2\")\n",
-      "-----  ---------  ---------  -----------------------------------------------  -----------------------------------------------\n",
-      "76391  Q66502527  Q27058096  right fifth intercostal nerve                    fifth intercostal nerve\n",
-      "64439  Q5453438   Q392316    First Nations in Alberta                         First Nations\n",
-      "54764  Q3998922   Q43304311  clarinet-violin-piano trio                       piano trio\n",
-      "71844  Q60687320  Q29385978  President of Communal Council of Tremithousa     President of Communal Council\n",
-      "26611  Q21116244  Q14878311  glutamate catabolic process to 2-oxoglutarate    glutamate catabolic process\n",
-      "76415  Q66502641  Q66502435  medial branch of cervicothoracic ganglion        branch of cervicothoracic ganglion\n",
-      "24160  Q20901636  Q705135    Grand Officer of the Order of Adolphe of Nassau  Order of Adolphe of Nassau\n",
-      "27271  Q21127225  Q133492    letters of Pliny the Younger                     letter\n",
-      " 3924  Q1152224   Q12050496  distinctive feature                              feature\n",
-      "84513  Q66571241  Q66570136  right cuneate fasciculus of medulla              cuneate fasciculus of medulla\n",
-      "92282  Q7624573   Q1084      strong noun                                      noun\n",
-      "23304  Q20205194  Q1644573   Orthodox pilgrimage                              pilgrimage\n",
-      " 9611  Q14860562  Q14862215  positive regulation of cell motility             regulation of cell motility\n",
-      "92883  Q77584712  Q837924    skew-symmetric bilinear form                     bilinear form\n",
-      "42826  Q2593298   Q1319755   European Speed Skating Championships for Men     European Speed Skating Championships\n",
-      "29359  Q2213536   Q193343    extensional tectonics                            tectonics\n",
-      "38430  Q22328939  Q22328937  nuclear subtelomeric heterochromatin             subtelomeric heterochromatin\n",
-      "51013  Q32861734  Q5758703   Western Highland Chatino                         Highland Chatino\n",
-      "70167  Q5898386   Q105688    hypobaric hypoxia                                hypoxia\n",
-      " 7736  Q13631670  Q588723    Passenger rail transport in China                rail transport in China\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tabulate(comp.sample(n=20, random_state=13), headers = 'keys', tablefmt = 'simple'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "future-peace",
-   "metadata": {},
-   "source": [
-    "# Determine Top Parents based on transitive child count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "attempted-calgary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import gzip\n",
-    "\n",
-    "p279StarDF = pd.read_csv('../../wd-correctness/wikidata-20210215/derived.P279star.tsv.gz', sep='\\t', compression='gzip')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "blocked-monitor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts = p279StarDF.node2.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "informal-destruction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts = p279StarDF_node2_counts.sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "mathematical-mandate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279StarDF_node2_counts_dict = p279StarDF_node2_counts.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "median-details",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Q35120       2461316\n",
-       "Q99527517    2254480\n",
-       "Q28813620    1363013\n",
-       "Q16887380    1362538\n",
-       "Q488383      1286312\n",
-       "Q20937557    1267164\n",
-       "Q58415929    1201300\n",
-       "Q4406616     1175217\n",
-       "Q223557      1164930\n",
-       "Q6671777     1122346\n",
-       "Q337060      1079449\n",
-       "Q7184903     1072421\n",
-       "Q219858      1057006\n",
-       "Q50365914    1056956\n",
-       "Q23958946    1038346\n",
-       "Q97669203    1007383\n",
-       "Q15712714    1007381\n",
-       "Q3511065     1007335\n",
-       "Q3771876     1004686\n",
-       "Q37500013    1004683\n",
-       "Name: node2, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279StarDF_node2_counts[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "beginning-element",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q35120,entity,2461316\n",
-      "Q99527517,collection entity,2254480\n",
-      "Q28813620,set,1363013\n",
-      "Q16887380,group,1362538\n",
-      "Q488383,object,1286312\n",
-      "Q20937557,series,1267164\n",
-      "Q58415929,spatio-temporal entity,1201300\n",
-      "Q4406616,concrete object,1175217\n",
-      "Q223557,physical object,1164930\n",
-      "Q6671777,structure,1122346\n",
-      "Q337060,perceptible object,1079449\n",
-      "Q7184903,abstract object,1072421\n",
-      "Q219858,zone,1057006\n",
-      "Q50365914,biological region,1056956\n",
-      "Q23958946,individual entity,1038346\n",
-      "Q97669203,molecular conformation,1007383\n",
-      "Q15712714,biomolecular structure,1007381\n",
-      "Q3511065,biological sequence,1007335\n",
-      "Q3771876,nucleic acid structure,1004686\n",
-      "Q37500013,primary structure,1004683\n",
-      "Q863908,nucleic acid sequence,1004680\n",
-      "Q7187,gene,1004634\n",
-      "Q103940464,continuant,1003328\n",
-      "Q53617489,independent continuant,1003327\n",
-      "Q27043950,anatomical entity,1003325\n",
-      "Q28732711,physical substance,977526\n",
-      "Q4503801,part and whole,969424\n",
-      "Q1310239,component,960112\n",
-      "Q20747295,protein-coding gene,942018\n",
-      "Q58416391,spatial entity,927246\n",
-      "Q43460564,chemical entity,894604\n",
-      "Q79529,chemical substance,893534\n",
-      "Q28728771,material substance,892254\n",
-      "Q35758,matter,891736\n",
-      "Q66394244,physical anatomical entity,877724\n",
-      "Q5127848,class,875770\n",
-      "Q578779,pure substance,874023\n",
-      "Q19478619,metaclass,873959\n",
-      "Q20026787,chemical component,873903\n",
-      "Q11173,chemical compound,873902\n",
-      "Q29651224,natural object,866276\n",
-      "Q16686022,natural physical object,866273\n",
-      "Q53617407,material entity,865793\n",
-      "Q27043948,material anatomical entity,865792\n",
-      "Q28845870,biological component,862053\n",
-      "Q4936952,anatomical structure,862034\n",
-      "Q1621273,particle,856456\n",
-      "Q46344,quantum,856370\n",
-      "Q28693603,quantum particle,856367\n",
-      "Q2923525,massive particle,856265\n",
-      "Q28877390,massive quantum particle,856264\n",
-      "Q908049,bound state,856239\n",
-      "Q3366856,composite particle,856208\n",
-      "Q2393187,molecular entity,856080\n",
-      "Q2901852,compound of carbon,855799\n",
-      "Q174211,organic compound,855763\n",
-      "Q169336,mixture,853910\n",
-      "Q11369,molecule,853417\n",
-      "Q178593,macromolecule,853231\n",
-      "Q206229,biomolecule,852989\n",
-      "Q81163,polymer,851896\n",
-      "Q422649,biopolymer,851714\n",
-      "Q424689,gene product,851333\n",
-      "Q66560214,biological macromolecule,789641\n",
-      "Q181394,nutrient,788439\n",
-      "Q145273,polyamide,788369\n",
-      "Q8054,protein,788357\n",
-      "Q16686448,artificial entity,309666\n",
-      "Q26907166,temporal entity,281532\n",
-      "Q1190554,occurrence,273428\n",
-      "Q4026292,action,231193\n",
-      "Q15401930,product,227605\n",
-      "Q937228,property,219920\n",
-      "Q8205328,artificial physical object,218567\n",
-      "Q1914636,activity,198404\n",
-      "Q1207505,quality,193010\n",
-      "Q24229398,agent,156590\n",
-      "Q61788060,human activity,148527\n",
-      "Q830077,subject,141105\n",
-      "Q28877,goods,139125\n",
-      "Q1150070,change,134268\n",
-      "Q18336849,item with given name property,133824\n",
-      "Q795052,individual,131442\n",
-      "Q3249551,process,130568\n",
-      "Q2424752,product,129982\n",
-      "Q215627,person,129884\n",
-      "Q4897819,role,129416\n",
-      "Q26720107,subject of a right,128685\n",
-      "Q3778211,legal person,128684\n",
-      "Q7239,organism,125704\n",
-      "Q16889133,class,125538\n",
-      "Q11028,information,122503\n",
-      "Q159344,heterotroph,122144\n",
-      "Q72638,consumer,122136\n",
-      "Q154954,natural person,121570\n",
-      "Q45983014,organisms by adaptation,121568\n",
-      "Q164509,omnivore,121567\n",
-      "Q5,human,121566\n",
-      "Q930933,relation,121018\n",
-      "Q16722960,phenomenon,120253\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "\n",
-    "top_parents = []\n",
-    "\n",
-    "for qnode,qnode_count in zip(p279StarDF_node2_counts[:100].index, p279StarDF_node2_counts[:100]):\n",
-    "    top_parents.append((qnode,requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0],qnode_count))\n",
-    "    print(qnode+','+requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0] + \",\" + str(qnode_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "proud-remainder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_parents = pd.DataFrame(top_parents, columns=['qnode','label','count'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "asian-pleasure",
-   "metadata": {},
-   "source": [
-    "## Analyze Parents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cardiac-prison",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3caff386d7254beab10e799a453273bb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "child_parent_dict = defaultdict(set)\n",
-    "parent_child_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        child_parent_dict[line_s[1]].add(line_s[3])\n",
-    "        parent_child_dict[line_s[3]].add(line_s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "defined-darwin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_depths = {}\n",
-    "min_depths = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "bibliographic-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_max_depth(qnode, depth=0, visited=set()):\n",
-    "    global parent_child_dict\n",
-    "#     print(qnode, depth, visited)\n",
-    "    if qnode not in parent_child_dict:\n",
-    "        return depth\n",
-    "    max_val = depth\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child not in visited:\n",
-    "            if child not in max_depths:\n",
-    "                max_depths[child] = find_max_depth(child, depth+1, visited | {child})\n",
-    "            max_val = max(max_val, max_depths[child])\n",
-    "    return max_val\n",
-    "\n",
-    "def find_min_depth(qnode, depth=0, visited=set()):\n",
-    "    global parent_child_dict\n",
-    "#     print(qnode, depth, visited)\n",
-    "    if qnode not in parent_child_dict:\n",
-    "        return depth\n",
-    "    min_val = float('inf')\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child not in visited:\n",
-    "            if child not in min_depths:\n",
-    "                min_depths[child] = find_min_depth(child, depth+1, visited | {child})\n",
-    "            min_val = min(min_val, min_depths[child])\n",
-    "    if min_val == float('inf'):\n",
-    "        return depth\n",
-    "    else:\n",
-    "        return min_val"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "opponent-vertex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "def entropy_of_children(qnode):\n",
-    "    entropy_sum = 0\n",
-    "    for child in parent_child_dict[qnode]:\n",
-    "        if child in p279StarDF_node2_counts_dict:\n",
-    "            assert p279StarDF_node2_counts_dict[child] <= p279StarDF_node2_counts_dict[qnode], (child, qnode)\n",
-    "            entropy_sum -= p279StarDF_node2_counts_dict[child] / p279StarDF_node2_counts_dict[qnode] * np.log2(p279StarDF_node2_counts_dict[child] / p279StarDF_node2_counts_dict[qnode])\n",
-    "    return float(entropy_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "widespread-storage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_reduction_ratio(qnode):\n",
-    "    return [p279StarDF_node2_counts_dict[qnode] / p279StarDF_node2_counts_dict[parent] for parent in child_parent_dict[qnode]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "exciting-representation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e0f343423654e6d93249f3f454fae38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newRows = []\n",
-    "for _, row in tqdm(top_parents.iterrows()):\n",
-    "    row['child_count'] = len(parent_child_dict[row['qnode']])\n",
-    "    row['max_no_of_siblings'] = max([len(parent_child_dict[parent]) - 1 for parent in child_parent_dict[row['qnode']]]) if child_parent_dict[row['qnode']] else 0\n",
-    "    row['min_no_of_siblings'] = min([len(parent_child_dict[parent]) - 1 for parent in child_parent_dict[row['qnode']]]) if child_parent_dict[row['qnode']] else 0\n",
-    "    row['max_depth'] = find_max_depth(row['qnode'])\n",
-    "    row['min_depth'] = find_min_depth(row['qnode'])\n",
-    "    row['entropy'] = entropy_of_children(row['qnode'])\n",
-    "    row['red_ratios'] = get_reduction_ratio(row['qnode'])\n",
-    "    newRows.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "lucky-teens",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Q15401930', 'Q28877', 'Q8205328'}"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parent_child_dict['Q2424752']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "numerical-sharp",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intense-bibliography",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blessed-biodiversity",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "common-cooking",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "chinese-competition",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "solid-final",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "known-logan",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "careful-linux",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>qnode</th>\n",
-       "      <th>label</th>\n",
-       "      <th>count</th>\n",
-       "      <th>child_count</th>\n",
-       "      <th>max_no_of_siblings</th>\n",
-       "      <th>min_no_of_siblings</th>\n",
-       "      <th>max_depth</th>\n",
-       "      <th>min_depth</th>\n",
-       "      <th>entropy</th>\n",
-       "      <th>red_ratios</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q35120</td>\n",
-       "      <td>entity</td>\n",
-       "      <td>2461316</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.986354</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99527517</td>\n",
-       "      <td>collection entity</td>\n",
-       "      <td>2254480</td>\n",
-       "      <td>6</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>29</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.201021</td>\n",
-       "      <td>[0.9159652803622127]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q28813620</td>\n",
-       "      <td>set</td>\n",
-       "      <td>1363013</td>\n",
-       "      <td>41</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.035319</td>\n",
-       "      <td>[0.6045797700578404]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q16887380</td>\n",
-       "      <td>group</td>\n",
-       "      <td>1362538</td>\n",
-       "      <td>147</td>\n",
-       "      <td>40</td>\n",
-       "      <td>40</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.786249</td>\n",
-       "      <td>[0.999651507359064]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q488383</td>\n",
-       "      <td>object</td>\n",
-       "      <td>1286312</td>\n",
-       "      <td>56</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.487792</td>\n",
-       "      <td>[0.5226114810125965]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>Q45983014</td>\n",
-       "      <td>organisms by adaptation</td>\n",
-       "      <td>121568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>146</td>\n",
-       "      <td>146</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>[0.08922173179757188]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>Q164509</td>\n",
-       "      <td>omnivore</td>\n",
-       "      <td>121567</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>[0.9953412589244777, 0.9999917741510924]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q5</td>\n",
-       "      <td>human</td>\n",
-       "      <td>121566</td>\n",
-       "      <td>254</td>\n",
-       "      <td>542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>21</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.311051</td>\n",
-       "      <td>[0.9999917740834272, 0.9999670971456774, 0.935...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q930933</td>\n",
-       "      <td>relation</td>\n",
-       "      <td>121018</td>\n",
-       "      <td>56</td>\n",
-       "      <td>44</td>\n",
-       "      <td>44</td>\n",
-       "      <td>29</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.937792</td>\n",
-       "      <td>[0.11284560820797056]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>Q16722960</td>\n",
-       "      <td>phenomenon</td>\n",
-       "      <td>120253</td>\n",
-       "      <td>9</td>\n",
-       "      <td>59</td>\n",
-       "      <td>59</td>\n",
-       "      <td>25</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.341837</td>\n",
-       "      <td>[0.04885719671915349]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        qnode                    label    count  child_count  \\\n",
-       "0      Q35120                   entity  2461316           60   \n",
-       "1   Q99527517        collection entity  2254480            6   \n",
-       "2   Q28813620                      set  1363013           41   \n",
-       "3   Q16887380                    group  1362538          147   \n",
-       "4     Q488383                   object  1286312           56   \n",
-       "..        ...                      ...      ...          ...   \n",
-       "95  Q45983014  organisms by adaptation   121568            1   \n",
-       "96    Q164509                 omnivore   121567            1   \n",
-       "97         Q5                    human   121566          254   \n",
-       "98    Q930933                 relation   121018           56   \n",
-       "99  Q16722960               phenomenon   120253            9   \n",
-       "\n",
-       "    max_no_of_siblings  min_no_of_siblings  max_depth  min_depth   entropy  \\\n",
-       "0                    0                   0         29          1  4.986354   \n",
-       "1                   59                  59         29          2  1.201021   \n",
-       "2                    5                   5         25          2  0.035319   \n",
-       "3                   40                  40         25          2  0.786249   \n",
-       "4                   59                  59         29          1  1.487792   \n",
-       "..                 ...                 ...        ...        ...       ...   \n",
-       "95                 146                 146         21          2  0.000012   \n",
-       "96                   5                   0         21          2  0.000012   \n",
-       "97                 542                   0         21          2  0.311051   \n",
-       "98                  44                  44         29          2  1.937792   \n",
-       "99                  59                  59         25          2  0.341837   \n",
-       "\n",
-       "                                           red_ratios  \n",
-       "0                                                  []  \n",
-       "1                                [0.9159652803622127]  \n",
-       "2                                [0.6045797700578404]  \n",
-       "3                                 [0.999651507359064]  \n",
-       "4                                [0.5226114810125965]  \n",
-       "..                                                ...  \n",
-       "95                              [0.08922173179757188]  \n",
-       "96           [0.9953412589244777, 0.9999917741510924]  \n",
-       "97  [0.9999917740834272, 0.9999670971456774, 0.935...  \n",
-       "98                              [0.11284560820797056]  \n",
-       "99                              [0.04885719671915349]  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(newRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "interested-reply",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(newRows).to_csv('../data/Analogical_Dataset/top_parents_analysis.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sporting-radius",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "pacific-hughes",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "needed-grenada",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordinary-schema",
-   "metadata": {},
-   "source": [
-    "# Determine if training file has common parent or common ancestor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "anonymous-penalty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "comp = pd.read_csv('../data/compositional.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "statutory-supplier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compDict = {row['node1']: row['node2'] for _, row in comp.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "legal-handy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDF = pd.read_csv('../data/Analogical_Dataset/Analogical_Test_File.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "democratic-hebrew",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q444321</td>\n",
-       "      <td>Q2009740</td>\n",
-       "      <td>telephone keybad</td>\n",
-       "      <td>keypad</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q807980</td>\n",
-       "      <td>Q82744</td>\n",
-       "      <td>barcode reader</td>\n",
-       "      <td>image scanner</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2914650</td>\n",
-       "      <td>Q7163</td>\n",
-       "      <td>identity politics</td>\n",
-       "      <td>politics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q4259813</td>\n",
-       "      <td>Q9174</td>\n",
-       "      <td>liberal religion</td>\n",
-       "      <td>religion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q10404171</td>\n",
-       "      <td>Q186005</td>\n",
-       "      <td>thermal therapy</td>\n",
-       "      <td>physiotherapy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    node1     node2        node1_label node2_label  relation other_node1  \\\n",
-       "25   Q250  Q1921606  computer keyboard    keyboard   PURPOSE     Q444321   \n",
-       "26   Q250  Q1921606  computer keyboard    keyboard   PURPOSE     Q807980   \n",
-       "27  Q7991      Q336    natural science     science  PROPERTY    Q2914650   \n",
-       "28  Q7991      Q336    natural science     science  PROPERTY    Q4259813   \n",
-       "29  Q7991      Q336    natural science     science  PROPERTY   Q10404171   \n",
-       "\n",
-       "   other_node2  other_node1_label other_node2_label other_relation  \\\n",
-       "25    Q2009740   telephone keybad            keypad        PURPOSE   \n",
-       "26      Q82744     barcode reader     image scanner        PURPOSE   \n",
-       "27       Q7163  identity politics          politics       PROPERTY   \n",
-       "28       Q9174   liberal religion          religion       PROPERTY   \n",
-       "29     Q186005    thermal therapy     physiotherapy       PROPERTY   \n",
-       "\n",
-       "            type  \n",
-       "25  POS_SAME_PAR  \n",
-       "26  POS_SAME_PAR  \n",
-       "27  POS_SAME_PAR  \n",
-       "28  POS_SAME_PAR  \n",
-       "29  POS_SAME_PAR  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDF[trainingDF.type == 'POS_SAME_PAR'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "scientific-charles",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDFSubset = trainingDF[trainingDF.type == 'POS_SAME_PAR']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "moral-shaft",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "      <th>Unnamed: 11</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q444321</td>\n",
-       "      <td>Q2009740</td>\n",
-       "      <td>telephone keybad</td>\n",
-       "      <td>keypad</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q54854916</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>typewriter keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q807980</td>\n",
-       "      <td>Q82744</td>\n",
-       "      <td>barcode reader</td>\n",
-       "      <td>image scanner</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2914650</td>\n",
-       "      <td>Q7163</td>\n",
-       "      <td>identity politics</td>\n",
-       "      <td>politics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q1637030</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>food science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q4259813</td>\n",
-       "      <td>Q9174</td>\n",
-       "      <td>liberal religion</td>\n",
-       "      <td>religion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q16387</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>information science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q10404171</td>\n",
-       "      <td>Q186005</td>\n",
-       "      <td>thermal therapy</td>\n",
-       "      <td>physiotherapy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_PAR</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1     node2          node1_label node2_label  relation  \\\n",
-       "25       Q250  Q1921606    computer keyboard    keyboard   PURPOSE   \n",
-       "26  Q54854916  Q1921606  typewriter keyboard    keyboard   PURPOSE   \n",
-       "27      Q7991      Q336      natural science     science  PROPERTY   \n",
-       "28   Q1637030      Q336         food science     science  PROPERTY   \n",
-       "29     Q16387      Q336  information science     science  PROPERTY   \n",
-       "\n",
-       "   other_node1 other_node2  other_node1_label other_node2_label  \\\n",
-       "25     Q444321    Q2009740   telephone keybad            keypad   \n",
-       "26     Q807980      Q82744     barcode reader     image scanner   \n",
-       "27    Q2914650       Q7163  identity politics          politics   \n",
-       "28    Q4259813       Q9174   liberal religion          religion   \n",
-       "29   Q10404171     Q186005    thermal therapy     physiotherapy   \n",
-       "\n",
-       "   other_relation          type Unnamed: 11  \n",
-       "25        PURPOSE  POS_SAME_PAR         NaN  \n",
-       "26        PURPOSE  POS_SAME_PAR         NaN  \n",
-       "27       PROPERTY  POS_SAME_PAR         NaN  \n",
-       "28       PROPERTY  POS_SAME_PAR         NaN  \n",
-       "29       PROPERTY  POS_SAME_PAR         NaN  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "competitive-medication",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-44-dd012d3407e6>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  trainingDFSubset['pair1_exists'] = trainingDFSubset.apply(checkIfExists, args=('node1','node2'), axis=1)\n",
-      "<ipython-input-44-dd012d3407e6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  trainingDFSubset['pair2_exists'] = trainingDFSubset.apply(checkIfExists, args=('other_node1','other_node2'), axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def checkIfExists(row, node1_col, node2_col):\n",
-    "    return row[node1_col] in compDict and row[node2_col] == compDict[row[node1_col]]\n",
-    "trainingDFSubset['pair1_exists'] = trainingDFSubset.apply(checkIfExists, args=('node1','node2'), axis=1)\n",
-    "trainingDFSubset['pair2_exists'] = trainingDFSubset.apply(checkIfExists, args=('other_node1','other_node2'), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "wrapped-softball",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True     35\n",
-       "False    15\n",
-       "Name: pair2_exists, dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset['pair2_exists'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "physical-fireplace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25   -1\n",
-       "26   -1\n",
-       "27   -1\n",
-       "28   -1\n",
-       "29   -1\n",
-       "30   -1\n",
-       "31   -1\n",
-       "32   -1\n",
-       "33   -1\n",
-       "34   -1\n",
-       "35   -1\n",
-       "36   -1\n",
-       "37   -1\n",
-       "38   -1\n",
-       "39   -1\n",
-       "40   -1\n",
-       "41   -1\n",
-       "42   -1\n",
-       "43   -1\n",
-       "44   -1\n",
-       "45   -1\n",
-       "46   -1\n",
-       "47   -1\n",
-       "48   -1\n",
-       "49   -1\n",
-       "50   -1\n",
-       "51   -1\n",
-       "52   -1\n",
-       "53   -1\n",
-       "54   -1\n",
-       "55   -1\n",
-       "56   -1\n",
-       "57   -1\n",
-       "58   -1\n",
-       "59   -1\n",
-       "60   -1\n",
-       "61   -1\n",
-       "62   -1\n",
-       "63   -1\n",
-       "64   -1\n",
-       "65   -1\n",
-       "66   -1\n",
-       "67   -1\n",
-       "68   -1\n",
-       "69   -1\n",
-       "70   -1\n",
-       "71   -1\n",
-       "72   -1\n",
-       "73   -1\n",
-       "74   -1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def checkIfCommonParent(row):\n",
-    "    if row['node2'] not in compDict or row['other_node2'] not in compDict:\n",
-    "        return -1\n",
-    "    elif compDict[row['node2']] == compDict[row['other_node2']]:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0\n",
-    "trainingDFSubset.apply(checkIfCommonParent, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "according-shark",
-   "metadata": {},
-   "source": [
-    "# Same Ancestor Subset Top Parent Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "hourly-december",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDF = pd.read_csv('../data/Analogical_Dataset/Analogical_Test_File.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "alternate-bookmark",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainingDFSubset = trainingDF[trainingDF.type == 'POS_SAME_ANC']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "existing-gauge",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>other_node1</th>\n",
-       "      <th>other_node2</th>\n",
-       "      <th>other_node1_label</th>\n",
-       "      <th>other_node2_label</th>\n",
-       "      <th>other_relation</th>\n",
-       "      <th>type</th>\n",
-       "      <th>Verified</th>\n",
-       "      <th>comment</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>pair1_ancestor</th>\n",
-       "      <th>pair2_ancestor</th>\n",
-       "      <th>top_pars_list</th>\n",
-       "      <th>Unnamed: 18</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>Q250</td>\n",
-       "      <td>Q1921606</td>\n",
-       "      <td>computer keyboard</td>\n",
-       "      <td>keyboard</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>Q7708416</td>\n",
-       "      <td>Q327065</td>\n",
-       "      <td>Text display</td>\n",
-       "      <td>display device</td>\n",
-       "      <td>PURPOSE</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1921606:keyboard|Q864114:input device|Q47146:...</td>\n",
-       "      <td>Q327065:display device|Q778637:output device|Q...</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>Q7991</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>natural science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q1140700</td>\n",
-       "      <td>Q11635</td>\n",
-       "      <td>improvisational theatre</td>\n",
-       "      <td>theatre</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q336:science|Q11862829:academic discipline|Q10...</td>\n",
-       "      <td>Q11635:theater|Q184485:performing arts|Q201852...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>Q16387</td>\n",
-       "      <td>Q336</td>\n",
-       "      <td>information science</td>\n",
-       "      <td>science</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q107181871</td>\n",
-       "      <td>Q11032</td>\n",
-       "      <td>manuscript newspaper</td>\n",
-       "      <td>newspaper</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q336:science|Q11862829:academic discipline|Q10...</td>\n",
-       "      <td>Q11032:newspaper|Q1002697:periodical|Q234460:t...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>Q9192</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Mandarin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q1122452</td>\n",
-       "      <td>Q29536</td>\n",
-       "      <td>Germanic paganism</td>\n",
-       "      <td>paganism</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q7850:Chinese|Q33857:Sinitic languages|Q45961:...</td>\n",
-       "      <td>Q29536:paganism|Q9174:religion|Q3702971:person...</td>\n",
-       "      <td>entity,object,abstract object,property</td>\n",
-       "      <td>entity,object,abstract object,structure,proper...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>Q56479</td>\n",
-       "      <td>Q7850</td>\n",
-       "      <td>Jin Chinese</td>\n",
-       "      <td>Chinese</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q702065</td>\n",
-       "      <td>Q9134</td>\n",
-       "      <td>Korean mythology</td>\n",
-       "      <td>mythology</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q7850:Chinese|Q33857:Sinitic languages|Q45961:...</td>\n",
-       "      <td>Q9134:mythology|Q49447:world view|Q20978643:po...</td>\n",
-       "      <td>entity,object,abstract object,property</td>\n",
-       "      <td>entity,object,abstract object,property,relatio...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>Q561</td>\n",
-       "      <td>Q553</td>\n",
-       "      <td>human tooth</td>\n",
-       "      <td>tooth</td>\n",
-       "      <td>OWNERSHIP</td>\n",
-       "      <td>Q106907987</td>\n",
-       "      <td>Q106907993</td>\n",
-       "      <td>mammalian oocyte</td>\n",
-       "      <td>vertebrate oocyte</td>\n",
-       "      <td>OWNERSHIP</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>Q5856</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>synthetic fiber</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q16676963</td>\n",
-       "      <td>Q6584340</td>\n",
-       "      <td>mint syrup</td>\n",
-       "      <td>syrup</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q161:fiber|Q214609:material|Q2424752:product</td>\n",
-       "      <td>Q6584340:syrup|Q40050:drink|Q2095:food|Q119405...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>Q4543</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>beach volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q55491</td>\n",
-       "      <td>Q55488</td>\n",
-       "      <td>underground railway station</td>\n",
-       "      <td>railway station</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1734:volleyball|Q877517:ball game|Q4438147:sp...</td>\n",
-       "      <td>Q55488:railway station|Q1318558:operation poin...</td>\n",
-       "      <td>entity,object,action,phenomenon</td>\n",
-       "      <td>entity,object,physical object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>Q15728017</td>\n",
-       "      <td>Q1734</td>\n",
-       "      <td>snow volleyball</td>\n",
-       "      <td>volleyball</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q99689267</td>\n",
-       "      <td>Q3305213</td>\n",
-       "      <td>leather painting</td>\n",
-       "      <td>painting</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1734:volleyball|Q877517:ball game|Q4438147:sp...</td>\n",
-       "      <td>Q3305213:painting|Q4502142:visual artwork|Q838...</td>\n",
-       "      <td>entity,object,action,phenomenon</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>Q9778</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>electronic music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q20358335</td>\n",
-       "      <td>Q8261</td>\n",
-       "      <td>political novel</td>\n",
-       "      <td>novel</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q638:music|Q735:art|Q2018526:arts|Q80083:human...</td>\n",
-       "      <td>Q8261:novel|Q571:book|Q49848:document|Q234460:...</td>\n",
-       "      <td>entity,object,abstract object,structure,action</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Q1045756</td>\n",
-       "      <td>Q638</td>\n",
-       "      <td>microtonal music</td>\n",
-       "      <td>music</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q5707594</td>\n",
-       "      <td>Q191067</td>\n",
-       "      <td>news article</td>\n",
-       "      <td>article</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q638:music|Q735:art|Q2018526:arts|Q80083:human...</td>\n",
-       "      <td>Q191067:article|Q234460:text|Q11028:informatio...</td>\n",
-       "      <td>entity,object,abstract object,structure,action</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>Q699</td>\n",
-       "      <td>Q17991521</td>\n",
-       "      <td>fairy tale</td>\n",
-       "      <td>tale</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q58079064</td>\n",
-       "      <td>Q2977</td>\n",
-       "      <td>protestant cathedral</td>\n",
-       "      <td>cathedral</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q17991521:tale|Q1318295:narrative|Q17537576:cr...</td>\n",
-       "      <td>Q2977:cathedral|Q16970:church building|Q137059...</td>\n",
-       "      <td>entity,object</td>\n",
-       "      <td>entity,object,physical object,structure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>Q9585</td>\n",
-       "      <td>Q432</td>\n",
-       "      <td>Shia Islam</td>\n",
-       "      <td>Islam</td>\n",
-       "      <td>INSTANCE</td>\n",
-       "      <td>Q4775051</td>\n",
-       "      <td>Q132203</td>\n",
-       "      <td>Antiochian Greeks</td>\n",
-       "      <td>Greek diaspora</td>\n",
-       "      <td>INSTANCE</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,abstract object,structure,proper...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Q6786</td>\n",
-       "      <td>Q6718</td>\n",
-       "      <td>bottom quark</td>\n",
-       "      <td>quark</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q55511397</td>\n",
-       "      <td>Q107968</td>\n",
-       "      <td>monoatomic anion</td>\n",
-       "      <td>anion</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q6718:quark|Q52234516:elementary fermion|Q4311...</td>\n",
-       "      <td>Q107968:anion|Q36496:ion|Q2393187:molecular en...</td>\n",
-       "      <td>entity,object,physical object,particle</td>\n",
-       "      <td>entity,object,physical object,particle</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>Q7141</td>\n",
-       "      <td>Q420</td>\n",
-       "      <td>cell biology</td>\n",
-       "      <td>biology</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q81197</td>\n",
-       "      <td>Q413</td>\n",
-       "      <td>nuclear physics</td>\n",
-       "      <td>physics</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q420:biology|Q7991:natural science|Q336:scienc...</td>\n",
-       "      <td>Q413:physics|Q14632398:physical sciences|Q7991...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>Q5860</td>\n",
-       "      <td>Q161</td>\n",
-       "      <td>carbon fibers</td>\n",
-       "      <td>fiber</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q5567094</td>\n",
-       "      <td>Q80228</td>\n",
-       "      <td>glass bottle</td>\n",
-       "      <td>bottle</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q161:fiber|Q214609:material|Q2424752:product</td>\n",
-       "      <td>Q80228:bottle|Q16896124:bin|Q987767:container|...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,structure,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>Q6311</td>\n",
-       "      <td>Q8104</td>\n",
-       "      <td>stellar atmosphere</td>\n",
-       "      <td>atmosphere</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>Q1037499</td>\n",
-       "      <td>Q36133</td>\n",
-       "      <td>lunar soil</td>\n",
-       "      <td>soil</td>\n",
-       "      <td>LOCATION</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q8104:atmosphere|Q47495022:shell of an astrono...</td>\n",
-       "      <td>Q36133:soil|Q1155083:granular material|Q214609...</td>\n",
-       "      <td>entity,object,physical object</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>Q426</td>\n",
-       "      <td>Q2386606</td>\n",
-       "      <td>animal rights</td>\n",
-       "      <td>right</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2845837</td>\n",
-       "      <td>Q11210</td>\n",
-       "      <td>tripolar coordinates</td>\n",
-       "      <td>coordinate system</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q2386606:right|Q7748:law|Q1151067:rule|Q268459...</td>\n",
-       "      <td>Q11210:coordinate system|Q184876:frame of refe...</td>\n",
-       "      <td>entity,object,abstract object</td>\n",
-       "      <td>entity,object,abstract object,structure,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>Q305</td>\n",
-       "      <td>Q186361</td>\n",
-       "      <td>corporal punishment</td>\n",
-       "      <td>punishment</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q3436733</td>\n",
-       "      <td>Q50000</td>\n",
-       "      <td>decisive victory</td>\n",
-       "      <td>victory</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q186361:punishment|Q2995644:result|Q733541:con...</td>\n",
-       "      <td>Q50000:victory|Q2995644:result|Q733541:consequ...</td>\n",
-       "      <td>entity,action</td>\n",
-       "      <td>entity,property</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>Q4812</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>day sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>Q9182675</td>\n",
-       "      <td>Q1107656</td>\n",
-       "      <td>perennial garden</td>\n",
-       "      <td>garden</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q527:sky|Q107:space|Q3054889:measurable set|Q3...</td>\n",
-       "      <td>Q1107656:garden|Q35145743:human-made landform|...</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "      <td>entity,object,physical object,abstract object,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>Q318887</td>\n",
-       "      <td>Q527</td>\n",
-       "      <td>evening sky</td>\n",
-       "      <td>sky</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>Q18354756</td>\n",
-       "      <td>Q344</td>\n",
-       "      <td>far future</td>\n",
-       "      <td>future</td>\n",
-       "      <td>TEMPORAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q527:sky|Q107:space|Q3054889:measurable set|Q3...</td>\n",
-       "      <td>Q344:future|Q11471:time|Q3054889:measurable se...</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "      <td>entity,object,abstract object,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>Q104857623</td>\n",
-       "      <td>Q374</td>\n",
-       "      <td>raspberry vodka</td>\n",
-       "      <td>vodka</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>Q1757352</td>\n",
-       "      <td>Q13317</td>\n",
-       "      <td>soy yogurt</td>\n",
-       "      <td>yogurt</td>\n",
-       "      <td>MATERIAL</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q374:vodka|Q56139:liquor|Q17562878:spirit drin...</td>\n",
-       "      <td>Q281:whisky|Q1479546:grain spirit|Q56139:liquo...</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "      <td>entity,object,physical object,product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q7076</td>\n",
-       "      <td>Q6199</td>\n",
-       "      <td>individualist anarchism</td>\n",
-       "      <td>anarchism</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q2300258</td>\n",
-       "      <td>Q200726</td>\n",
-       "      <td>conditional probability distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q380528</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>color charge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q184550</td>\n",
-       "      <td>Q11379</td>\n",
-       "      <td>mechanical energy</td>\n",
-       "      <td>energy</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q73792:charge|Q4373292:physical property|Q9372...</td>\n",
-       "      <td>Q11379:energy|Q107715:physical quantity|Q30931...</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>Q1364131</td>\n",
-       "      <td>Q73792</td>\n",
-       "      <td>weak hypercharge</td>\n",
-       "      <td>charge</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>Q240105</td>\n",
-       "      <td>Q11465</td>\n",
-       "      <td>radial velocity</td>\n",
-       "      <td>velocity</td>\n",
-       "      <td>PROPERTY</td>\n",
-       "      <td>POS_SAME_ANC</td>\n",
-       "      <td>VERIFIED</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q73792:charge|Q4373292:physical property|Q9372...</td>\n",
-       "      <td>Q11465:velocity|Q107715:physical quantity|Q309...</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "      <td>entity,object,abstract object,property,relation</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         node1      node2              node1_label node2_label   relation  \\\n",
-       "75        Q250   Q1921606        computer keyboard    keyboard    PURPOSE   \n",
-       "76       Q7991       Q336          natural science     science   PROPERTY   \n",
-       "77      Q16387       Q336      information science     science   PROPERTY   \n",
-       "78       Q9192      Q7850         Mandarin Chinese     Chinese   LOCATION   \n",
-       "79      Q56479      Q7850              Jin Chinese     Chinese   LOCATION   \n",
-       "80        Q561       Q553              human tooth       tooth  OWNERSHIP   \n",
-       "81       Q5856       Q161          synthetic fiber       fiber   MATERIAL   \n",
-       "82       Q4543      Q1734         beach volleyball  volleyball   LOCATION   \n",
-       "83   Q15728017      Q1734          snow volleyball  volleyball   LOCATION   \n",
-       "84       Q9778       Q638         electronic music       music   PROPERTY   \n",
-       "85    Q1045756       Q638         microtonal music       music   PROPERTY   \n",
-       "86        Q699  Q17991521               fairy tale        tale   PROPERTY   \n",
-       "87       Q9585       Q432               Shia Islam       Islam   INSTANCE   \n",
-       "88       Q6786      Q6718             bottom quark       quark   PROPERTY   \n",
-       "89       Q7141       Q420             cell biology     biology   PROPERTY   \n",
-       "90       Q5860       Q161            carbon fibers       fiber   MATERIAL   \n",
-       "91       Q6311      Q8104       stellar atmosphere  atmosphere   LOCATION   \n",
-       "92        Q426   Q2386606            animal rights       right   PROPERTY   \n",
-       "93        Q305    Q186361      corporal punishment  punishment   PROPERTY   \n",
-       "94       Q4812       Q527                  day sky         sky   TEMPORAL   \n",
-       "95     Q318887       Q527              evening sky         sky   TEMPORAL   \n",
-       "96  Q104857623       Q374          raspberry vodka       vodka   MATERIAL   \n",
-       "97       Q7076      Q6199  individualist anarchism   anarchism   PROPERTY   \n",
-       "98     Q380528     Q73792             color charge      charge   PROPERTY   \n",
-       "99    Q1364131     Q73792         weak hypercharge      charge   PROPERTY   \n",
-       "\n",
-       "   other_node1 other_node2                     other_node1_label  \\\n",
-       "75    Q7708416     Q327065                          Text display   \n",
-       "76    Q1140700      Q11635               improvisational theatre   \n",
-       "77  Q107181871      Q11032                  manuscript newspaper   \n",
-       "78    Q1122452      Q29536                     Germanic paganism   \n",
-       "79     Q702065       Q9134                      Korean mythology   \n",
-       "80  Q106907987  Q106907993                      mammalian oocyte   \n",
-       "81   Q16676963    Q6584340                            mint syrup   \n",
-       "82      Q55491      Q55488           underground railway station   \n",
-       "83   Q99689267    Q3305213                      leather painting   \n",
-       "84   Q20358335       Q8261                       political novel   \n",
-       "85    Q5707594     Q191067                          news article   \n",
-       "86   Q58079064       Q2977                  protestant cathedral   \n",
-       "87    Q4775051     Q132203                     Antiochian Greeks   \n",
-       "88   Q55511397     Q107968                      monoatomic anion   \n",
-       "89      Q81197        Q413                      nuclear physics    \n",
-       "90    Q5567094      Q80228                          glass bottle   \n",
-       "91    Q1037499      Q36133                            lunar soil   \n",
-       "92    Q2845837      Q11210                  tripolar coordinates   \n",
-       "93    Q3436733      Q50000                      decisive victory   \n",
-       "94    Q9182675    Q1107656                      perennial garden   \n",
-       "95   Q18354756        Q344                            far future   \n",
-       "96    Q1757352      Q13317                            soy yogurt   \n",
-       "97    Q2300258     Q200726  conditional probability distribution   \n",
-       "98     Q184550      Q11379                     mechanical energy   \n",
-       "99     Q240105      Q11465                       radial velocity   \n",
-       "\n",
-       "           other_node2_label other_relation          type  Verified comment  \\\n",
-       "75            display device        PURPOSE  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "76                   theatre       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "77                 newspaper       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "78                  paganism       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "79                 mythology       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "80         vertebrate oocyte      OWNERSHIP  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "81                     syrup       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "82           railway station       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "83                  painting       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "84                     novel       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "85                   article       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "86                 cathedral       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "87            Greek diaspora       INSTANCE  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "88                     anion       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "89                   physics       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "90                    bottle       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "91                      soil       LOCATION  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "92         coordinate system       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "93                   victory       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "94                    garden       TEMPORAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "95                    future       TEMPORAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "96                    yogurt       MATERIAL  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "97  probability distribution       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "98                    energy       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "99                  velocity       PROPERTY  POS_SAME_ANC  VERIFIED     NaN   \n",
-       "\n",
-       "   par_node par_label                                     pair1_ancestor  \\\n",
-       "75      NaN       NaN  Q1921606:keyboard|Q864114:input device|Q47146:...   \n",
-       "76      NaN       NaN  Q336:science|Q11862829:academic discipline|Q10...   \n",
-       "77      NaN       NaN  Q336:science|Q11862829:academic discipline|Q10...   \n",
-       "78      NaN       NaN  Q7850:Chinese|Q33857:Sinitic languages|Q45961:...   \n",
-       "79      NaN       NaN  Q7850:Chinese|Q33857:Sinitic languages|Q45961:...   \n",
-       "80      NaN       NaN                                                NaN   \n",
-       "81      NaN       NaN       Q161:fiber|Q214609:material|Q2424752:product   \n",
-       "82      NaN       NaN  Q1734:volleyball|Q877517:ball game|Q4438147:sp...   \n",
-       "83      NaN       NaN  Q1734:volleyball|Q877517:ball game|Q4438147:sp...   \n",
-       "84      NaN       NaN  Q638:music|Q735:art|Q2018526:arts|Q80083:human...   \n",
-       "85      NaN       NaN  Q638:music|Q735:art|Q2018526:arts|Q80083:human...   \n",
-       "86      NaN       NaN  Q17991521:tale|Q1318295:narrative|Q17537576:cr...   \n",
-       "87      NaN       NaN                                                NaN   \n",
-       "88      NaN       NaN  Q6718:quark|Q52234516:elementary fermion|Q4311...   \n",
-       "89      NaN       NaN  Q420:biology|Q7991:natural science|Q336:scienc...   \n",
-       "90      NaN       NaN       Q161:fiber|Q214609:material|Q2424752:product   \n",
-       "91      NaN       NaN  Q8104:atmosphere|Q47495022:shell of an astrono...   \n",
-       "92      NaN       NaN  Q2386606:right|Q7748:law|Q1151067:rule|Q268459...   \n",
-       "93      NaN       NaN  Q186361:punishment|Q2995644:result|Q733541:con...   \n",
-       "94      NaN       NaN  Q527:sky|Q107:space|Q3054889:measurable set|Q3...   \n",
-       "95      NaN       NaN  Q527:sky|Q107:space|Q3054889:measurable set|Q3...   \n",
-       "96      NaN       NaN  Q374:vodka|Q56139:liquor|Q17562878:spirit drin...   \n",
-       "97      NaN       NaN                                                NaN   \n",
-       "98      NaN       NaN  Q73792:charge|Q4373292:physical property|Q9372...   \n",
-       "99      NaN       NaN  Q73792:charge|Q4373292:physical property|Q9372...   \n",
-       "\n",
-       "                                       pair2_ancestor  \\\n",
-       "75  Q327065:display device|Q778637:output device|Q...   \n",
-       "76  Q11635:theater|Q184485:performing arts|Q201852...   \n",
-       "77  Q11032:newspaper|Q1002697:periodical|Q234460:t...   \n",
-       "78  Q29536:paganism|Q9174:religion|Q3702971:person...   \n",
-       "79  Q9134:mythology|Q49447:world view|Q20978643:po...   \n",
-       "80                                                NaN   \n",
-       "81  Q6584340:syrup|Q40050:drink|Q2095:food|Q119405...   \n",
-       "82  Q55488:railway station|Q1318558:operation poin...   \n",
-       "83  Q3305213:painting|Q4502142:visual artwork|Q838...   \n",
-       "84  Q8261:novel|Q571:book|Q49848:document|Q234460:...   \n",
-       "85  Q191067:article|Q234460:text|Q11028:informatio...   \n",
-       "86  Q2977:cathedral|Q16970:church building|Q137059...   \n",
-       "87                                                NaN   \n",
-       "88  Q107968:anion|Q36496:ion|Q2393187:molecular en...   \n",
-       "89  Q413:physics|Q14632398:physical sciences|Q7991...   \n",
-       "90  Q80228:bottle|Q16896124:bin|Q987767:container|...   \n",
-       "91  Q36133:soil|Q1155083:granular material|Q214609...   \n",
-       "92  Q11210:coordinate system|Q184876:frame of refe...   \n",
-       "93  Q50000:victory|Q2995644:result|Q733541:consequ...   \n",
-       "94  Q1107656:garden|Q35145743:human-made landform|...   \n",
-       "95  Q344:future|Q11471:time|Q3054889:measurable se...   \n",
-       "96  Q281:whisky|Q1479546:grain spirit|Q56139:liquo...   \n",
-       "97                                                NaN   \n",
-       "98  Q11379:energy|Q107715:physical quantity|Q30931...   \n",
-       "99  Q11465:velocity|Q107715:physical quantity|Q309...   \n",
-       "\n",
-       "                                        top_pars_list  \\\n",
-       "75  entity,object,physical object,abstract object,...   \n",
-       "76                      entity,object,abstract object   \n",
-       "77                      entity,object,abstract object   \n",
-       "78             entity,object,abstract object,property   \n",
-       "79             entity,object,abstract object,property   \n",
-       "80                      entity,object,physical object   \n",
-       "81              entity,object,physical object,product   \n",
-       "82                    entity,object,action,phenomenon   \n",
-       "83                    entity,object,action,phenomenon   \n",
-       "84     entity,object,abstract object,structure,action   \n",
-       "85     entity,object,abstract object,structure,action   \n",
-       "86                                      entity,object   \n",
-       "87  entity,object,abstract object,structure,proper...   \n",
-       "88             entity,object,physical object,particle   \n",
-       "89                      entity,object,abstract object   \n",
-       "90              entity,object,physical object,product   \n",
-       "91                      entity,object,physical object   \n",
-       "92                      entity,object,abstract object   \n",
-       "93                                      entity,action   \n",
-       "94             entity,object,abstract object,relation   \n",
-       "95             entity,object,abstract object,relation   \n",
-       "96              entity,object,physical object,product   \n",
-       "97                                                NaN   \n",
-       "98    entity,object,abstract object,property,relation   \n",
-       "99    entity,object,abstract object,property,relation   \n",
-       "\n",
-       "                                          Unnamed: 18  \n",
-       "75  entity,object,physical object,abstract object,...  \n",
-       "76            entity,object,abstract object,structure  \n",
-       "77  entity,object,physical object,abstract object,...  \n",
-       "78  entity,object,abstract object,structure,proper...  \n",
-       "79  entity,object,abstract object,property,relatio...  \n",
-       "80                                                NaN  \n",
-       "81              entity,object,physical object,product  \n",
-       "82            entity,object,physical object,structure  \n",
-       "83                      entity,object,physical object  \n",
-       "84  entity,object,physical object,abstract object,...  \n",
-       "85                      entity,object,abstract object  \n",
-       "86            entity,object,physical object,structure  \n",
-       "87                                                NaN  \n",
-       "88             entity,object,physical object,particle  \n",
-       "89                      entity,object,abstract object  \n",
-       "90    entity,object,physical object,structure,product  \n",
-       "91              entity,object,physical object,product  \n",
-       "92   entity,object,abstract object,structure,relation  \n",
-       "93                                    entity,property  \n",
-       "94  entity,object,physical object,abstract object,...  \n",
-       "95             entity,object,abstract object,relation  \n",
-       "96              entity,object,physical object,product  \n",
-       "97    entity,object,abstract object,property,relation  \n",
-       "98    entity,object,abstract object,property,relation  \n",
-       "99    entity,object,abstract object,property,relation  "
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingDFSubset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "running-conspiracy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a178a238123f40b19e775fa110bcaaa6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "subset_nodes = set(trainingDFSubset.node1.to_list() + trainingDFSubset.node2.to_list() + trainingDFSubset.other_node1.to_list() + trainingDFSubset.other_node2.to_list())\n",
-    "parent_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279star.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        if line_s[0] in subset_nodes:\n",
-    "            parent_dict[line_s[0]].add(line_s[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "private-catch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_nodes = {'Q35120': 'entity', 'Q488383': 'object', 'Q223557': 'physical object', 'Q7184903': 'abstract object',\n",
-    "             'Q6671777': 'structure', 'Q219858': 'zone', 'Q1621273': 'particle', 'Q174211': 'organic compound', \n",
-    "             'Q4026292': 'action', 'Q937228': 'property', 'Q2424752': 'product', 'Q215627': 'person', 'Q4897819': 'role',\n",
-    "             'Q7239': 'organism', 'Q930933': 'relation', 'Q16722960': 'phenomenon'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "prepared-senegal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entity',\n",
-       " 'object',\n",
-       " 'physical object',\n",
-       " 'abstract object',\n",
-       " 'structure',\n",
-       " 'zone',\n",
-       " 'particle',\n",
-       " 'organic compound',\n",
-       " 'action',\n",
-       " 'property',\n",
-       " 'product',\n",
-       " 'person',\n",
-       " 'role',\n",
-       " 'organism',\n",
-       " 'relation',\n",
-       " 'phenomenon']"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(top_nodes.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detailed-painting",
-   "metadata": {},
-   "source": [
-    "# Pairwise Top Parent Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "brief-postcard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "369de020404143008c60621efca55090",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "child_parent_dict = defaultdict(set)\n",
-    "parent_child_dict = defaultdict(set)\n",
-    "with gzip.open('../../wd-correctness/wikidata-20210215/derived.P279.tsv.gz') as fIN:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(fIN):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line_s = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "        child_parent_dict[line_s[1]].add(line_s[3])\n",
-    "        parent_child_dict[line_s[3]].add(line_s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "careful-divorce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_parent_path(curr, top_parent, path):\n",
-    "    if curr == top_parent:\n",
-    "        return path + [top_parent]\n",
-    "    elif curr not in child_parent_dict:\n",
-    "        return False\n",
-    "    \n",
-    "    for par in child_parent_dict[curr]:\n",
-    "        new_path = find_parent_path(par, top_parent, path + [curr])\n",
-    "        if new_path:\n",
-    "            return new_path\n",
-    "\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "id": "atlantic-cornwall",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_common_top_parents(node1, node2):\n",
-    "    pars1, pars2 = set(), set()\n",
-    "    for top_node in top_nodes:\n",
-    "        if top_node in parent_dict[node1]:\n",
-    "            pars1.add(top_node)\n",
-    "        if top_node in parent_dict[node2]:\n",
-    "            pars2.add(top_node)\n",
-    "    return pars1 & pars2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "black-confidentiality",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shortest_common_path(node1, node2):\n",
-    "    node1_shortest, node2_shortest = [], []\n",
-    "    shortest_len = float('inf')\n",
-    "    for top_par in find_common_top_parents(node1, node2):\n",
-    "        node1_path = find_parent_path(node1, top_par, [])\n",
-    "        node2_path = find_parent_path(node2, top_par, [])\n",
-    "        \n",
-    "        if node1_path and node2_path and max(len(node1_path), len(node2_path)) < shortest_len:\n",
-    "            node1_shortest = node1_path\n",
-    "            node2_shortest = node2_path\n",
-    "            shortest_len = max(len(node1_path), len(node2_path))\n",
-    "    node1_shortest = \"|\".join([node + ':' + fetch_label(node) for node in node1_shortest])\n",
-    "    node2_shortest = \"|\".join([node + ':' + fetch_label(node) for node in node2_shortest])\n",
-    "    pars1 = {}\n",
-    "    pars2 = {}\n",
-    "    for top_node in top_nodes:\n",
-    "        if top_node in parent_dict[row['node2']]:\n",
-    "            pars1[top_node] = top_nodes[top_node]\n",
-    "        if top_node in parent_dict[row['other_node2']]:\n",
-    "            pars2[top_node] = top_nodes[top_node]\n",
-    "    return node1_shortest + \";\" + node2_shortest + ';' + \",\".join(list(pars1.values())) + ';' + \",\".join(list(pars2.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "laden-charter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_label(qnode):\n",
-    "    try:\n",
-    "        return requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['labels']['en'][0]\n",
-    "    except:\n",
-    "        return str(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "material-interpretation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e73c4a3b211f45ec9d1ebc8d6895bbae",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths = []\n",
-    "for _,row in tqdm(trainingDFSubset.iterrows()):\n",
-    "    trainingSubsetCommonPaths.append(shortest_common_path(row['node2'], row['other_node2']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "id": "promotional-diamond",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Q1921606:keyboard|Q864114:input device|Q47146:user interface|Q23808:interface|Q230629:connection|Q930933:relation;Q327065:display device|Q778637:output device|Q47146:user interface|Q23808:interface|Q230629:connection|Q930933:relation;entity,object,physical object,abstract object,product,person,relation;entity,object,physical object,abstract object,product,relation',\n",
-       " 'Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q11635:theater|Q184485:performing arts|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object,structure',\n",
-       " 'Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q11032:newspaper|Q1002697:periodical|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,physical object,abstract object,structure,action',\n",
-       " 'Q7850:Chinese|Q33857:Sinitic languages|Q45961:Sino-Tibetan languages|Q7524744:Sino-Austronesian languages|Q20162172:human language|Q315:language|Q17376908:languoid|Q7184903:abstract object;Q29536:paganism|Q9174:religion|Q3702971:personal data|Q61151961:individual data|Q42848:data|Q11028:information|Q7184903:abstract object;entity,object,abstract object,property;entity,object,abstract object,structure,property,relation',\n",
-       " 'Q7850:Chinese|Q33857:Sinitic languages|Q45961:Sino-Tibetan languages|Q7524744:Sino-Austronesian languages|Q20162172:human language|Q315:language|Q17376908:languoid|Q7184903:abstract object;Q9134:mythology|Q49447:world view|Q20978643:point of view|Q595523:notion|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object,property;entity,object,abstract object,property,relation,phenomenon',\n",
-       " ';;entity,object,physical object;',\n",
-       " 'Q161:fiber|Q214609:material|Q2424752:product;Q6584340:syrup|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;entity,object,physical object,product;entity,object,physical object,product',\n",
-       " 'Q1734:volleyball|Q877517:ball game|Q4438147:sport games|Q11410:game|Q17538258:recreative work|Q386724:work|Q15401930:product|Q488383:object;Q55488:railway station|Q1318558:operation point|Q800279:railway facility|Q27096235:artificial geographic entity|Q8205328:artificial physical object|Q223557:physical object|Q4406616:concrete object|Q488383:object;entity,object,action,phenomenon;entity,object,physical object,structure',\n",
-       " 'Q1734:volleyball|Q877517:ball game|Q4438147:sport games|Q11410:game|Q17538258:recreative work|Q386724:work|Q15401930:product|Q488383:object;Q3305213:painting|Q4502142:visual artwork|Q838948:work of art|Q28877:goods|Q337060:perceptible object|Q223557:physical object|Q4406616:concrete object|Q488383:object;entity,object,action,phenomenon;entity,object,physical object',\n",
-       " 'Q638:music|Q735:art|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q8261:novel|Q571:book|Q49848:document|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object,structure,action;entity,object,physical object,abstract object,structure,action',\n",
-       " 'Q638:music|Q735:art|Q2018526:arts|Q80083:humanities|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q191067:article|Q234460:text|Q11028:information|Q7184903:abstract object;entity,object,abstract object,structure,action;entity,object,abstract object',\n",
-       " 'Q17991521:tale|Q1318295:narrative|Q17537576:creative work|Q15621286:intellectual work|Q386724:work|Q15401930:product|Q488383:object|Q35120:entity;Q2977:cathedral|Q16970:church building|Q1370598:place of worship|Q811979:architectural structure|Q811430:human-made geographic feature|Q35145743:human-made landform|Q27096235:artificial geographic entity|Q27096213:geographic entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object;entity,object,physical object,structure',\n",
-       " ';;entity,object,abstract object,structure,property,relation;',\n",
-       " 'Q6718:quark|Q52234516:elementary fermion|Q43116:elementary particle|Q63243922:elementary object|Q488383:object|Q35120:entity;Q107968:anion|Q36496:ion|Q2393187:molecular entity|Q43460564:chemical entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object,physical object,particle;entity,object,physical object,particle',\n",
-       " 'Q420:biology|Q7991:natural science|Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;Q413:physics|Q14632398:physical sciences|Q7991:natural science|Q336:science|Q11862829:academic discipline|Q1047113:specialty|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object',\n",
-       " 'Q161:fiber|Q214609:material|Q2424752:product;Q80228:bottle|Q16896124:bin|Q987767:container|Q39546:tool|Q16798631:equipment|Q2424752:product;entity,object,physical object,product;entity,object,physical object,structure,product',\n",
-       " 'Q8104:atmosphere|Q47495022:shell of an astronomical object|Q23497974:covering|Q23497981:layer|Q223557:physical object;Q36133:soil|Q1155083:granular material|Q214609:material|Q2424752:product|Q28877:goods|Q337060:perceptible object|Q223557:physical object;entity,object,physical object;entity,object,physical object,product',\n",
-       " 'Q2386606:right|Q7748:law|Q1151067:rule|Q2684591:statement|Q108163:proposition|Q474328:declarative sentence|Q41796:sentence|Q9380010:denunciation|Q628523:message|Q11028:information|Q7184903:abstract object;Q11210:coordinate system|Q184876:frame of reference|Q20978643:point of view|Q595523:notion|Q9081:knowledge|Q104127086:memory|Q11028:information|Q7184903:abstract object;entity,object,abstract object;entity,object,abstract object,structure,relation',\n",
-       " 'Q186361:punishment|Q2995644:result|Q733541:consequence|Q408386:inference|Q3249551:process|Q20937557:series|Q16887380:group|Q28813620:set|Q99527517:collection entity|Q35120:entity;Q50000:victory|Q2995644:result|Q733541:consequence|Q408386:inference|Q3249551:process|Q20937557:series|Q16887380:group|Q28813620:set|Q99527517:collection entity|Q35120:entity;entity,action;entity,property',\n",
-       " 'Q527:sky|Q107:space|Q3054889:measurable set|Q36161:set|Q217594:class|Q16889133:class|Q99527517:collection entity|Q35120:entity;Q1107656:garden|Q35145743:human-made landform|Q27096235:artificial geographic entity|Q27096213:geographic entity|Q58416391:spatial entity|Q58415929:spatio-temporal entity|Q35120:entity;entity,object,abstract object,relation;entity,object,physical object,abstract object,structure,relation',\n",
-       " 'Q527:sky|Q107:space|Q3054889:measurable set|Q36161:set|Q217594:class|Q246672:mathematical object|Q7184903:abstract object;Q344:future|Q11471:time|Q3054889:measurable set|Q36161:set|Q217594:class|Q246672:mathematical object|Q7184903:abstract object;entity,object,abstract object,relation;entity,object,abstract object,relation',\n",
-       " 'Q374:vodka|Q56139:liquor|Q17562878:spirit drink|Q154:alcoholic beverage|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;Q13317:yogurt|Q26868224:fermented milk products, other than sour cream and cottage cheese|Q3506176:fermented milk product|Q147538:soft drink|Q2647467:non-alcoholic beverage|Q40050:drink|Q2095:food|Q1194058:disposable product|Q2424752:product;entity,object,physical object,product;entity,object,physical object,structure,action,property,product,phenomenon',\n",
-       " ';;;entity,object,abstract object,property,relation',\n",
-       " 'Q73792:charge|Q4373292:physical property|Q937228:property;Q11379:energy|Q107715:physical quantity|Q309314:quantity|Q937228:property;entity,object,abstract object,property,relation;entity,object,abstract object,property,relation',\n",
-       " 'Q73792:charge|Q4373292:physical property|Q937228:property;Q11465:velocity|Q107715:physical quantity|Q309314:quantity|Q937228:property;entity,object,abstract object,property,relation;entity,object,abstract object,property,relation']"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moderate-roller",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "diverse-clinton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchDescription(qnode):\n",
-    "    try:\n",
-    "        return requests.get('http://ckg07:9200/wikidatadwd-augmented/_doc/'+qnode).json()['_source']['descriptions']['en'][0]\n",
-    "    except:\n",
-    "        return str(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "painful-craft",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0eaf93e5d0947d9ae34fd4739b07cdd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths = []\n",
-    "for _,row in tqdm(trainingDF.iterrows()):\n",
-    "    trainingSubsetCommonPaths.append((fetchDescription(row['node1']) + '&' + fetchDescription(row['node2']) + '&' + \\\n",
-    "                                     fetchDescription(row['other_node1']) + '&' + fetchDescription(row['other_node2'])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "id": "front-consortium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['device comprising an arrangement of buttons or keys used to input text in computers&data input device&keyboard of a typewriter&data input device',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&language family&group of languages that belongs to the Sino-Tibetan family',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&group of Chinese languages primarily spoken in the whole city of Shanghai, Zhejiang province and the southern half of Jiangsu province, as well as bordering areas.&group of languages that belongs to the Sino-Tibetan family',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&any fibre derived from natural sources such as plants, animals or minerals&natural or synthetic substance made of long, thin filaments',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&music performed in front of an audience&art form, and cultural activity, whose medium is sound',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&music genre&art form, and cultural activity, whose medium is sound',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&variante of tale that pertains to urbanity&prose fiction that can be either rooted in oral tradition or created by an author',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths',\n",
-       " 'type of quark&class of fundamental particles&type of quark&class of fundamental particles',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&academic discipline&science that studies living beings',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&atmosphere of astronomical objects outside our solar system&layer of gases surrounding a material body',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&right to use the dining facilities offered to the members (and possibly their guests, when accompanied by a member) of certain organisations such as universities, clubs, colleges and bodies such as the House of Lords, and the Hawks\\\\ Club&legal position allowing a person to require a certain thing from another person or other persons',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&False&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&the study of the recording and retrieval of information&systematic enterprise that builds and organizes knowledge',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&movement in political theology and political philosophy&political philosophy and movement',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&keypad that appears on a \\\\Touch Tone\\\\ telephone&computer input device',\n",
-       " 'keyboard of a typewriter&data input device&electronic device that can read and output printed barcodes to a computer&device that optically scans images, printed text, handwriting, or an object, and converts it to a digital image',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&political position based on the interests and perspectives of social groups with which people identify&practice and theory of influencing other people, as in government',\n",
-       " 'applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge&False&set of beliefs and practices for a group or community',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&therapeutic method of treatment&health profession that aims to address the illnesses or injuries that limit a person\\\\s abilities to function in everyday lives',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&field of accounting concerned with the summary, analysis and reporting of financial transactions related to a business&measurement, processing and communication of financial information about economic entities',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&False&East Asian language',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&dialect of English spoken in Nigeria&West Germanic language originating in England',\n",
-       " 'human organ&organ for the circulation of blood in animal circulatory systems&False&rigid organ that constitute part of the endoskeleton of vertebrates',\n",
-       " 'feature of the face&organ that smells and facilitates breathing&organ that serves as the center of the nervous system in apes and monkeys&organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals',\n",
-       " 'False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&essential respiration organ in many air-breathing animals',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&soft wood&fibrous material from trees or other plants',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&metal resistant to corrosion and oxidation in moist air, e.g. ruthenium, rhodium, palladium, silver, osmium, iridium, platinum, and gold&element, compound or alloy that is a good conductor of both electricity and heat',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&water sport&team sport played on a court with baskets on either end',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&game derived from association football adapted for play in an indoor arena&team sports that involve, to varying degrees, kicking a ball to score a goal',\n",
-       " 'team sport featuring constant motion, communication and bursts of explosiveness.&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&informal ad hoc variant of the game of cricket, played by people of all genders and all ages in gardens, back yards, on the street, in parks, carparks, beaches and any area not specifically intended for the purpose&team sport played with bats and balls',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&tennis variant&racket sport played on a court bisected by a net',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&literary genre&written art form',\n",
-       " 'circulation of literature beyond its country of origin&written art form&dance performed by a single dancer&rhythmic movement of the body',\n",
-       " 'False&creative work in which pictures and text convey information such as narratives&False&visual artwork, surface artistically covered with paint',\n",
-       " 'music genre&art form, and cultural activity, whose medium is sound&use of digital technology to distribute or project motion pictures as opposed to the historical use of reels of motion picture film&art of film making and projection',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&literary genre&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story',\n",
-       " 'False&publication type&blog on fashion&discussion or informational site published on the World Wide Web',\n",
-       " 'False&work comprising an ordered list of items&article that is published in news media&text that forms an independent part of a publication',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Christian doctrine professed by the Roman Catholic Church&monotheistic religious group based on the belief of Jesus being the Son of God',\n",
-       " 'denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&ethnic religion of the Yao people in China, heavily based on Taoism since the 13th century, similar to the communitarian Taoism in the earliest Taoist movements in China proper&religious or philosophical tradition of Chinese origin',\n",
-       " 'False&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&modernized form of Buddhism&religion founded by the Buddha',\n",
-       " 'type of quark&class of fundamental particles&ion having a negative charge&atom or molecule in which the total number of electrons is not equal to the total number of protons, giving the atom a net positive or negative electrical charge',\n",
-       " 'type of quark&class of fundamental particles&class of ions&atom or molecule in which the total number of electrons is not equal to the total number of protons, giving the atom a net positive or negative electrical charge',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&scientific study of minerals&study of the composition, structure, physical properties, and history of Earth\\\\s components, and the processes by which they are shaped',\n",
-       " 'field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force&subdiscipline within chemistry involving the scientific study of carbon-based compounds, hydrocarbons, and their derivatives&branch of physical science concerned with the composition, structure and properties of matter',\n",
-       " 'medical speciality dealing with the diseases of animals, animal welfare, etc.&field of study for diagnosing, treating and preventing disease&branch of psychology focused on personality&The scientific study of the way the human mind works and how it influences behaviour, or the influence of a particular person\\\\s character on their behaviour',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&wire made of iron&single, usually cylindrical, flexible strand or rod of metal',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&paint type&colored composition applied over a surface that dries as a solid film',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&atmospheric layer of Earth&atmospheric layer of Earth',\n",
-       " 'water-filled depression between coastal sand dunes&land area that is permanently or seasonally saturated with water&sub-continental land that is surrounded by water&contiguous area of land surrounded by ocean; large continuous area of land, either surrounded by sea or contiguous with another landmass',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&Ttx on shares transferred electronically without using a stock transfer form&method to impose financial charge or other levy upon a taxpayer by a government or functional equivalent',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&award for authors and literary associations&something given to a person or a group of people to recognize their merit or excellence',\n",
-       " 'land areas near the shore that are frequently or permanently covered by water&land area that is permanently or seasonally saturated with water&space separate from the rest of the residential garden&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'rock-like reefs, composed of dense aggregations of oysters&feature lying beneath the surface of the water&garden where flowers are grown and displayed&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&kind of beer&alcoholic drink',\n",
-       " 'wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes&Wikimedia disambiguation page&alcoholic drink',\n",
-       " 'False&distilled beverage&type of vodka flavored with raspberries&distilled alcoholic beverage',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&political ideology&political philosophy focused on retaining traditional social institutions',\n",
-       " 'movement in political theology and political philosophy&political philosophy and movement&nationalism based on the its activity on the internet&ideology and movement that promotes the interests of a particular nation (as in a group of people) especially with the aim of gaining and maintaining the nation\\\\s sovereignty (self-governance) over its homeland',\n",
-       " 'form of anarchism&political philosophy and movement&False&belief or attitude that some individuals, who form an elite, are those whose influence or authority is greater than that of others',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&third in a set of four quantum numbers, distinguishes the orbitals available within a subshell, and is used to calculate the azimuthal component of the orientation of orbital in space&notation for conserved quantities in physics and chemistry',\n",
-       " 'type of frequency&inverse of the period duration&Force that makes a moving body follow a curved path&influence that causes an object to change motion',\n",
-       " 'rate at which electric energy is transferred by an electric circuit&rate at which energy is transferred, used, or transformed&False&mass per volume',\n",
-       " 'average velocity attained by charged particles in a material due to electric field&rate of change of the position of an object as a function of time, and the direction of that change&potential and kinetic energy associated with the position and movement of a physical object&quantitative physical property transferred to objects to perform heating or work on them',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&False&output device for presentation of information in visual form',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&theatrical genre&collaborative form of performing and fine art',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&False&scheduled publication containing news of events, articles, features, editorials, and advertising',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&ethnic religion practiced by the Germanic peoples from the Iron Age until Christianisation&non-Abrahamic religion, or modern religious movement such as nature worship',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&Mythology that existed in the Korean Peninsula from ancient times.&stories of gods and fairies and fantastic creatures',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&False&False',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&railway station located below grade&railway facility where trains regularly stop to load or unload passengers and/or freight',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&type of artwork&visual artwork, surface artistically covered with paint',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&literary genre&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&article that is published in news media&text that forms an independent part of a publication',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&church that is the seat of a bishop of any of the Protestant churches&Christian church, which is seat of a bishop',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Christian ethnoreligious Group&diaspora',\n",
-       " 'type of quark&class of fundamental particles&anion consisting of one atom&ion having a negative charge',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&lidded container made from glass&cylindrical container',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&finely grained regolith on the Moon&natural body consisting of layers that are primarily composed of minerals',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&given a reference triangle, a coordinate system whose three coordinates are distances to each of the three vertices of the triangle&System for determining the position of a point by a tuple of scalars',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&A military victory determines the undisputed outcome of a war&above a gate is the word Invictus',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&False&planned space set aside for the display, cultivation, and enjoyment of plants',\n",
-       " 'view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth&time beyond 10 000 AD&time which has yet to occur',\n",
-       " 'type of vodka flavored with raspberries&distilled alcoholic beverage&False&dairy product',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&mathematical function that describes the probability of occurrence of different possible outcomes in an experiment',\n",
-       " 'property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&potential and kinetic energy associated with the position and movement of a physical object&quantitative physical property transferred to objects to perform heating or work on them',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&subclass of velocity&rate of change of the position of an object as a function of time, and the direction of that change',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&paint that air dries to a hard, usually glossy, finish, used for coating surfaces that are outdoors or otherwise subject to hard wear or variations in temperature&colored composition applied over a surface that dries as a solid film',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&deep blue colored glass with cobalt&amorphous solid that exhibits a glass transition when heated towards the liquid state',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&organization or political movement leading a rebellion, or a non-violent social movement, against a colonial power or national government&type of group action',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&computer whose batteries use solar power&foldable portable personal computer for mobile use',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&False&material produced by twining, weaving, felting, knotting, or otherwise processing natural or synthetic fibers',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&narrative text, normally of a substantial length and in the form of prose describing a fictional and sequential story&a written text that can be published in printed or electronic form',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&Temporary or emergency government body/authority created when none exists&system or group of people governing an organized community, often a state',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&form of dictatorial military rule&autocratic form of government which is ruled by a sole leader',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&stage of the English language from about the 12th through 15th centuries&West Germanic language originating in England',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&body exerting the executive power in France&system or group of people governing an organized community, often a state',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&False&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&type of wood&fibrous material from trees or other plants',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&music that developed in literate cultures, replacing prehistoric music&art form, and cultural activity, whose medium is sound',\n",
-       " 'type of quark&class of fundamental particles&civilization in control of a planetary system&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&social movement seeking to change anti-fat bias in social attitudes&type of group action',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&type of financial institution&financial institution',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&False&material or object that produces a magnetic field',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&Wikimedia list article&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&lands originally owned by the monarchy of the United Kingdom&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&collection of songs, poetry, dances etc. of China&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&cultural movement&cultural movement that spanned the period roughly from the 14th to the 17th century',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&dialect&West Germanic language originating in England',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&society\\\\s way of life within anthropology',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&ritual and ceremonial mask of Sub-Saharan Africa&any full or partial face covering, whether ceremonial, protective, decorative, or used as disguise',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&flora and fauna of the Philippines&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&False&investigation and reporting of events, issues and trends to a broad audience',\n",
-       " 'keyboard of a typewriter&data input device&ancient Egyptian funerary text&a written text that can be published in printed or electronic form',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&Expressive body of culture shared by a particular group.&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'applied science devoted to the study of food&systematic enterprise that builds and organizes knowledge&False&cultural movement that spanned the period roughly from the 14th to the 17th century',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&infusion made from roasted barley grains&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'study of the philosophical, biological, social, and cultural aspects of human life.&systematic enterprise that builds and organizes knowledge&False&system or group of people governing an organized community, often a state',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&wildlife that can live or thrive in urban environments&undomesticated organisms that grow or live wild in an area without being introduced by humans',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&music genre&art form, and cultural activity, whose medium is sound',\n",
-       " 'human organ&organ for the circulation of blood in animal circulatory systems&current issue that has to be reported&communication of selected information on current events',\n",
-       " 'feature of the face&organ that smells and facilitates breathing&drink made from infusing fruit and other material in hot water&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'False&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&reference book containing an alphabetical list of slang&collection of words and their meanings',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&New model of banks initiative by Reserve Bank of India&financial institution',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&False&cultural expressions of a particular group of people, consisting of legends, music, oral history, proverbs, jokes, popular beliefs, fairy tales, etc.',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&False&interaction among people in which they create, share, and/or exchange information and ideas in virtual communities and networks',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&economy with an increased emphasis on informational activities and information industry&area of the production, distribution and trade, as well as consumption of goods and services by different agents',\n",
-       " 'team sport featuring constant motion, communication and bursts of explosiveness.&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&drink made from sepals of Hibiscus sabdariffa&drink made from infusing boiling water with the leaves of the tea plant',\n",
-       " 'False&sport in which players attempt to hit a ball with a club into a goal using a minimum number of shots&False&collection of words and their meanings',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&syrup usually made from the xylem sap of sugar maple, red maple, or black maple trees&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'circulation of literature beyond its country of origin&written art form&birds that live on or around water&class of tetrapods',\n",
-       " 'False&creative work in which pictures and text convey information such as narratives&type of garbage&unwanted or unusable material',\n",
-       " 'music genre&art form, and cultural activity, whose medium is sound&painted decoration or scene applied to a prepared wall or ceiling surface&visual artwork, surface artistically covered with paint',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&coffee brewing method originating from Turkey&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'False&publication type&aggregate of past events from the beginning of recorded human history to the Early Middle Ages&past events and their tracks or records, studied by various branches of human sciences of history',\n",
-       " 'False&work comprising an ordered list of items&continental geography&science that studies the terrestrial surface, the societies that inhabit it and the territories, landscapes, places or regions that form it when interacting with each other',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&method of brewing coffee&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'denomination of Islam&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&creation and maintenance of colonies by people from another territory&creation of an unequal relationship between states through domination',\n",
-       " 'False&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&type of writing tool&writing and drawing implement using liquid or paste ink',\n",
-       " 'type of quark&class of fundamental particles&insect which lives in the air or on land and breeds in water&class of arthropods',\n",
-       " 'type of quark&class of fundamental particles&bank that specializes in originating and/or servicing mortgage loans&financial institution',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&box containing cigars&container (usually cuboidal) with at least one roughly rectangular cross-section',\n",
-       " 'field of physics that deals with the structure and behavior of atomic nuclei&study of matter and its motion, along with related concepts such as energy and force&Variety of coffee plant&brewed beverage prepared from roasted coffee seeds',\n",
-       " 'medical speciality dealing with the diseases of animals, animal welfare, etc.&field of study for diagnosing, treating and preventing disease&chocolate-flavored condiment&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&An economic system combining public and private production&area of the production, distribution and trade, as well as consumption of goods and services by different agents',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&fire lit at a campsite&rapid oxidation of a material',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&False&writing and drawing implement using liquid or paste ink',\n",
-       " 'water-filled depression between coastal sand dunes&land area that is permanently or seasonally saturated with water&False&science that studies the terrestrial surface, the societies that inhabit it and the territories, landscapes, places or regions that form it when interacting with each other',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&physical money&object or record accepted as payment',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&a pencil whose lead contains a dye so that its stroke is indelible&writing implement',\n",
-       " 'land areas near the shore that are frequently or permanently covered by water&land area that is permanently or seasonally saturated with water&type of private company&financial institution',\n",
-       " 'rock-like reefs, composed of dense aggregations of oysters&feature lying beneath the surface of the water&clean subterran source of water, rich in minerals and trace elements&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'wine made from dark-colored grape varieties with coloring from the skin&alcoholic drink typically made from grapes&pattern of human activity and associated with the minority peoples of Russia&society\\\\s way of life within anthropology',\n",
-       " 'wine that is fermented without grape skin, with a yellowish color&alcoholic drink typically made from grapes&thin grade of paper used for printing books which have many pages&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'False&distilled beverage&material discarded as a byproduct of agriculture&unwanted or unusable material',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&perennial woody plant',\n",
-       " 'movement in political theology and political philosophy&political philosophy and movement&cellulose-based paper that is used in baking&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'form of anarchism&political philosophy and movement&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'physical property that quantifies an object\\\\s interaction with electric fields&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&head of state and head of government of the Republic of Cyprus&leader of a country or part of a country, usually in republics',\n",
-       " 'type of frequency&inverse of the period duration&a volcano considered unlikely to erupt again&natural, surface vent or fissure usually in a mountainous form',\n",
-       " 'rate at which electric energy is transferred by an electric circuit&rate at which energy is transferred, used, or transformed&False&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'average velocity attained by charged particles in a material due to electric field&rate of change of the position of an object as a function of time, and the direction of that change&False&thick, viscous liquid consisting primarily of a solution of sugar in water',\n",
-       " 'device comprising an arrangement of buttons or keys used to input text in computers&data input device&subdiscipline within chemistry involving the scientific study of carbon-based compounds, hydrocarbons, and their derivatives&branch of physical science concerned with the composition, structure and properties of matter',\n",
-       " 'branch of science about the natural world&systematic enterprise that builds and organizes knowledge&False&writing implement',\n",
-       " 'field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information&systematic enterprise that builds and organizes knowledge&water on the continents surface, rather than underground&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'group of related varieties or dialects spoken across most of Northern and Southwestern China&group of languages that belongs to the Sino-Tibetan family&False&outline of a strategy for achievement of an objective',\n",
-       " 'language&group of languages that belongs to the Sino-Tibetan family&branch of physiology and neuroscience&science of the function of living systems',\n",
-       " 'calcified whitish structure in humans\\\\ mouths used to break down food&hard, calcified structure found in the jaws (or mouths) of many vertebrates and used to break down food&positionرئيس الجمهوريه اليمنية&leader of a country or part of a country, usually in republics',\n",
-       " 'artificially manufactured fibres, often based on polymers&natural or synthetic substance made of long, thin filaments&electronically coded still image in video technology&data transmission unit',\n",
-       " 'team sport played by two teams of two players on a sand court divided by a net&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&level of psychological well-being, or an absence of a mental disorder&state of physical, mental and social well-being in the absence of disease and infirmity',\n",
-       " 'sport&ballgame and team sport in which two teams compete to ground the ball on their opponents\\\\ side of the net&a recession that affects many countries around the world&business cycle contraction;\\xa0general slowdown in economic activity',\n",
-       " 'music that employs electronic musical instruments, digital instruments and circuitry-based music technology&art form, and cultural activity, whose medium is sound&tinplate container&container (usually cuboidal) with at least one roughly rectangular cross-section',\n",
-       " 'use in music of microtones (intervals smaller than a semitone)&art form, and cultural activity, whose medium is sound&compensation paid by an offender (usually a murderer) or their family to kin of the victim&object or record accepted as payment',\n",
-       " 'fictional story typically featuring folkloric fantasy characters and magic&prose fiction that can be either rooted in oral tradition or created by an author&pencil with lead of finely ground, highly compacted charcoal&writing implement',\n",
-       " 'group of denominations of Islam which holds that Muhammad designated Ali as his successor and leader (imam), whose adherents form the majority of the population in Iran, Iraq, Azerbaijan, and Bahrain&monotheistic religion, founded by Muhammad in the 7th century, based on the teachings of the Quran and the hadiths&Overall health of the population of Denmark&state of physical, mental and social well-being in the absence of disease and infirmity',\n",
-       " 'type of quark&class of fundamental particles&Paper with security features&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'scientific discipline that studies cells&science that studies living beings&False&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'material fibers about 5–10 μm in diameter composed of carbon&natural or synthetic substance made of long, thin filaments&recreational visitor attraction&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'outer region of the volume of a star&layer of gases surrounding a material body&article in an academic publication, usually peer reviewed&text that forms an independent part of a publication',\n",
-       " 'idea that the needs of non-human animals should be afforded the same consideration as those of human beings&legal position allowing a person to require a certain thing from another person or other persons&water frozen into the solid state&chemical compound; main constituent of the fluids of most living organisms',\n",
-       " 'form of physical punishment that involves pain&imposition of an undesirable or unpleasant outcome as a response and deterrent to an action or behaviour that is deemed undesirable or unacceptable&False&organized cultural society that encounters many communities, on a scale of a nation or human, as well as a system of development',\n",
-       " 'designation for the sky in day&everything that is above the surface of the Earth&money given in a bribe&object or record accepted as payment',\n",
-       " 'view of the starry sky to a respective season between twilight and a few hours after sunset&everything that is above the surface of the Earth&culture of an area&society\\\\s way of life within anthropology',\n",
-       " 'type of vodka flavored with raspberries&distilled alcoholic beverage&False&permanently dedicated recreation area, often open to the public and generally characterized by natural, historic, or landscape features',\n",
-       " 'several traditions of thought within the anarchist movement&political philosophy and movement&False&type of rotor craft in which lift and thrust are supplied by rotors',\n",
-       " 'property of quarks and gluons that is related to the particles\\\\ strong interactions in the theory of quantum chromodynamics&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&type of paper&thin, flexible material mainly used for writing upon, printing upon, drawing or for packaging',\n",
-       " 'False&generalization of electric charge (EM) adding color charge (QCD), mass-energy (gravitation), etc.; sometimes considered same as its charge quantum number&False&writing implement']"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainingSubsetCommonPaths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "upset-introduction",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blank-weekend",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "alpine-cooperation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recent-algorithm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cordless-subdivision",
-   "metadata": {},
-   "source": [
-    "# Find Candidates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "id": "historical-maryland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'False'"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fetchDescription('Q1757352')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "id": "composite-vienna",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "556d68601f09422292148ac709006b9a",
-       "version_major": 2,
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-      "text/plain": [
-       "  0%|          | 0/39 [00:00<?, ?it/s]"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "data": {
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-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q3711805</td>\n",
-       "      <td>singular distribution</td>\n",
-       "      <td>distribution concentrated on a set of measure ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q7893853</td>\n",
-       "      <td>univariate distribution</td>\n",
-       "      <td>probability distribution of only one random va...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q3258332</td>\n",
-       "      <td>Kent distribution</td>\n",
-       "      <td>probability distribution on the two-dimensiona...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q16000505</td>\n",
-       "      <td>symmetric probability distribution</td>\n",
-       "      <td>probability distribution which features a dens...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q670653</td>\n",
-       "      <td>marginal distribution</td>\n",
-       "      <td>probability distribution of a subset (the \\mar...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q4889567</td>\n",
-       "      <td>Benktander Weibull distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q7049201</td>\n",
-       "      <td>noncentral F-distribution</td>\n",
-       "      <td>continuous probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Q21051291</td>\n",
-       "      <td>joint distribution of random variables</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q3258268</td>\n",
-       "      <td>noncentral beta distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Q1472677</td>\n",
-       "      <td>heavy-tailed distribution</td>\n",
-       "      <td>probability distributions whose tails are not ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Q6664772</td>\n",
-       "      <td>location-scale family</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Q7857491</td>\n",
-       "      <td>Tweedie distribution</td>\n",
-       "      <td>family of probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q917918</td>\n",
-       "      <td>continuous probability distribution</td>\n",
-       "      <td>probability distribution which admits a cumula...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Q2671311</td>\n",
-       "      <td>Rice distribution</td>\n",
-       "      <td>probability distribution of the magnitude of a...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q85900768</td>\n",
-       "      <td>multimodal distribution</td>\n",
-       "      <td>probability distribution whose density has two...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Q3204440</td>\n",
-       "      <td>relativistic Breit–Wigner distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Q3711786</td>\n",
-       "      <td>compound Poisson distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Q17157111</td>\n",
-       "      <td>mixture distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Q7247759</td>\n",
-       "      <td>product distribution</td>\n",
-       "      <td>probability distribution, in mathematics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Q98098110</td>\n",
-       "      <td>board game tile distribution</td>\n",
-       "      <td>a distribution of game tiles and their scores ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Q1188504</td>\n",
-       "      <td>exponential family</td>\n",
-       "      <td>probablility distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Q5421539</td>\n",
-       "      <td>exponentiated Weibull distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Q1417619</td>\n",
-       "      <td>Voigt profile</td>\n",
-       "      <td>probability distribution often used in spectro...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>Q7604398</td>\n",
-       "      <td>statistical interference</td>\n",
-       "      <td>overlap of multiple probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Q7051758</td>\n",
-       "      <td>normal-inverse-gamma distribution</td>\n",
-       "      <td>four-parameter family of multivariate continuo...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Q2421579</td>\n",
-       "      <td>log-Laplace distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Q26898117</td>\n",
-       "      <td>discrete probability distribution</td>\n",
-       "      <td>probability distribution whose mass lies entir...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>Q5527846</td>\n",
-       "      <td>Gaussian q-distribution</td>\n",
-       "      <td>family of probability distributions</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q3258303</td>\n",
-       "      <td>Benktander Gibrat distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Q3773214</td>\n",
-       "      <td>sampling distribution</td>\n",
-       "      <td>probability distribution of the possible sampl...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Q12345341</td>\n",
-       "      <td>Asymptotic distribution</td>\n",
-       "      <td>probability distribution to which random varia...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Q59394126</td>\n",
-       "      <td>Uniform distribution</td>\n",
-       "      <td>term in probability theory</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Q1333358</td>\n",
-       "      <td>joint probability distribution</td>\n",
-       "      <td>probability distribution which can feature mor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>Q1934245</td>\n",
-       "      <td>stable distribution</td>\n",
-       "      <td>distribution of variables which satisfies a st...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Q5156926</td>\n",
-       "      <td>compound probability distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Q576072</td>\n",
-       "      <td>Student\\s t-distribution</td>\n",
-       "      <td>probability distribution</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>Q7051757</td>\n",
-       "      <td>Normal-inverse-Wishart distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>Q255598</td>\n",
-       "      <td>bimodal distribution</td>\n",
-       "      <td>probability distribution whose density has two...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Q2300258</td>\n",
-       "      <td>conditional probability distribution</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0                                       1  \\\n",
-       "0    Q3711805                   singular distribution   \n",
-       "1    Q7893853                 univariate distribution   \n",
-       "2    Q3258332                       Kent distribution   \n",
-       "3   Q16000505      symmetric probability distribution   \n",
-       "4     Q670653                   marginal distribution   \n",
-       "5    Q4889567         Benktander Weibull distribution   \n",
-       "6    Q7049201               noncentral F-distribution   \n",
-       "7   Q21051291  joint distribution of random variables   \n",
-       "8    Q3258268            noncentral beta distribution   \n",
-       "9    Q1472677               heavy-tailed distribution   \n",
-       "10   Q6664772                   location-scale family   \n",
-       "11   Q7857491                    Tweedie distribution   \n",
-       "12    Q917918     continuous probability distribution   \n",
-       "13   Q2671311                       Rice distribution   \n",
-       "14  Q85900768                 multimodal distribution   \n",
-       "15   Q3204440  relativistic Breit–Wigner distribution   \n",
-       "16   Q3711786           compound Poisson distribution   \n",
-       "17  Q17157111                    mixture distribution   \n",
-       "18   Q7247759                    product distribution   \n",
-       "19  Q98098110            board game tile distribution   \n",
-       "20   Q1188504                      exponential family   \n",
-       "21   Q5421539      exponentiated Weibull distribution   \n",
-       "22   Q1417619                           Voigt profile   \n",
-       "23   Q7604398                statistical interference   \n",
-       "24   Q7051758       normal-inverse-gamma distribution   \n",
-       "25   Q2421579                log-Laplace distribution   \n",
-       "26  Q26898117       discrete probability distribution   \n",
-       "27   Q5527846                 Gaussian q-distribution   \n",
-       "28   Q3258303          Benktander Gibrat distribution   \n",
-       "29   Q3773214                   sampling distribution   \n",
-       "30  Q12345341                 Asymptotic distribution   \n",
-       "31  Q59394126                    Uniform distribution   \n",
-       "32   Q1333358          joint probability distribution   \n",
-       "33   Q1934245                     stable distribution   \n",
-       "34   Q5156926       compound probability distribution   \n",
-       "35    Q576072                Student\\s t-distribution   \n",
-       "36   Q7051757     Normal-inverse-Wishart distribution   \n",
-       "37    Q255598                    bimodal distribution   \n",
-       "38   Q2300258    conditional probability distribution   \n",
-       "\n",
-       "                                                    2  \n",
-       "0   distribution concentrated on a set of measure ...  \n",
-       "1   probability distribution of only one random va...  \n",
-       "2   probability distribution on the two-dimensiona...  \n",
-       "3   probability distribution which features a dens...  \n",
-       "4   probability distribution of a subset (the \\mar...  \n",
-       "5                                               False  \n",
-       "6                 continuous probability distribution  \n",
-       "7                                               False  \n",
-       "8                                               False  \n",
-       "9   probability distributions whose tails are not ...  \n",
-       "10                                              False  \n",
-       "11                family of probability distributions  \n",
-       "12  probability distribution which admits a cumula...  \n",
-       "13  probability distribution of the magnitude of a...  \n",
-       "14  probability distribution whose density has two...  \n",
-       "15                           probability distribution  \n",
-       "16                                              False  \n",
-       "17                           probability distribution  \n",
-       "18           probability distribution, in mathematics  \n",
-       "19  a distribution of game tiles and their scores ...  \n",
-       "20                          probablility distribution  \n",
-       "21                                              False  \n",
-       "22  probability distribution often used in spectro...  \n",
-       "23      overlap of multiple probability distributions  \n",
-       "24  four-parameter family of multivariate continuo...  \n",
-       "25                           probability distribution  \n",
-       "26  probability distribution whose mass lies entir...  \n",
-       "27                family of probability distributions  \n",
-       "28                                              False  \n",
-       "29  probability distribution of the possible sampl...  \n",
-       "30  probability distribution to which random varia...  \n",
-       "31                         term in probability theory  \n",
-       "32  probability distribution which can feature mor...  \n",
-       "33  distribution of variables which satisfies a st...  \n",
-       "34                                              False  \n",
-       "35                           probability distribution  \n",
-       "36                                              False  \n",
-       "37  probability distribution whose density has two...  \n",
-       "38                                              False  "
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "par_qnode = 'Q200726'\n",
-    "results = []\n",
-    "for child in tqdm(parent_child_dict[par_qnode]):\n",
-    "    results.append((child, fetch_label(child), fetchDescription(child)))\n",
-    "pd.DataFrame(results).head(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "atlantic-milton",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opposed-regulation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "liable-lincoln",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "encouraging-legend",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
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-   "types_to_exclude": [
-    "module",
-    "function",
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-    "instance",
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-   ],
-   "window_display": false
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- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis Files Analyser.ipynb b/Analysis Files Analyser.ipynb
deleted file mode 100644
index 2223351..0000000
--- a/Analysis Files Analyser.ipynb	
+++ /dev/null
@@ -1,1783 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "roman-insulation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "thick-station",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New Acc']\n",
-    "        oldAcc = row['Old Acc']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New Acc'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "drawn-crisis",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
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-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57.267442</td>\n",
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-       "      <td>0.000000</td>\n",
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-       "      <td>0.325000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.210526</td>\n",
-       "      <td>0.038835</td>\n",
-       "      <td>0.065574</td>\n",
-       "      <td>12.082148</td>\n",
-       "      <td>0</td>\n",
-       "      <td>57.267442</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.686047</td>\n",
-       "      <td>56.104651</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.292683</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.288889</td>\n",
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-       "      <td>-0.581395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>67.151163</td>\n",
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-       "      <td>0.923077</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.727273</td>\n",
-       "      <td>0.505747</td>\n",
-       "      <td>0.427184</td>\n",
-       "      <td>0.463158</td>\n",
-       "      <td>18.160508</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>7.848837</td>\n",
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-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63.662791</td>\n",
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-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.812500</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.722222</td>\n",
-       "      <td>0.511628</td>\n",
-       "      <td>0.213592</td>\n",
-       "      <td>0.301370</td>\n",
-       "      <td>3.838469</td>\n",
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-       "      <td>4.069767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>64.244186</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.038835</td>\n",
-       "      <td>0.073394</td>\n",
-       "      <td>4.033524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>63.953488</td>\n",
-       "      <td>0.290698</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>65.697674</td>\n",
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-       "      <td>0.000000</td>\n",
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-       "      <td>0.500000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.520000</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.577778</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.123894</td>\n",
-       "      <td>19.390705</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    0                   1   2     3   4          5   \\\n",
-       "0         text_7_props        19k_childPar   2  True   1  57.267442   \n",
-       "1         text_2_props         19k_2_class   2  True   1  56.686047   \n",
-       "2              complex        19k_childPar   1  True   3  67.151163   \n",
-       "3               transe  19k_class_childPar   1  True   1  63.662791   \n",
-       "4             abstract  19k_class_childPar   2  True   1  63.953488   \n",
-       "5  abstract_first_sent  19k_class_siblings   2  True   1  65.697674   \n",
-       "\n",
-       "          6         7    8         9   ...        15    16        17  \\\n",
-       "0  57.267442  0.000000  344  0.325000  ...  0.333333  0.75  0.461538   \n",
-       "1  56.104651 -0.581395  344  0.292683  ...  0.288889  0.65  0.400000   \n",
-       "2  68.023256  0.872093  344  0.916667  ...  0.923077  0.60  0.727273   \n",
-       "3  67.732558  4.069767  344  0.900000  ...  0.812500  0.65  0.722222   \n",
-       "4  64.244186  0.290698  344  0.461538  ...  0.468750  0.75  0.576923   \n",
-       "5  65.697674  0.000000  344  0.500000  ...  0.520000  0.65  0.577778   \n",
-       "\n",
-       "         18        19        20         21  22         23        24  \n",
-       "0  0.210526  0.038835  0.065574  12.082148   0  57.267442  0.000000  \n",
-       "1  0.210526  0.038835  0.065574  87.920485   0  56.686047 -0.581395  \n",
-       "2  0.505747  0.427184  0.463158  18.160508   0  60.174419  7.848837  \n",
-       "3  0.511628  0.213592  0.301370   3.838469   0  63.662791  4.069767  \n",
-       "4  0.666667  0.038835  0.073394   4.033524   0  63.953488  0.290698  \n",
-       "5  0.700000  0.067961  0.123894  19.390705   0  65.697674  0.000000  \n",
-       "\n",
-       "[6 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findMaximals(unsupSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "random-river",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findPrecRecIMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New I F1-Score']\n",
-    "        oldAcc = row['Old I F1-Score']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list()[:5], embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New I F1-Score'], embDict[emb]['maxRow']['New I F1-Score'] - embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Old I Precision'], embDict[emb]['maxRow']['New I Precision'], embDict[emb]['maxRow']['Old I Recall'], embDict[emb]['maxRow']['New I Recall'], ])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "large-cruise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>0</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
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-       "      <td>0.035417</td>\n",
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-       "      <td>0.65</td>\n",
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-       "      <td>text_2_props</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.393443</td>\n",
-       "      <td>0.430769</td>\n",
-       "      <td>0.037327</td>\n",
-       "      <td>0.295455</td>\n",
-       "      <td>0.311111</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.727273</td>\n",
-       "      <td>0.106583</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.756757</td>\n",
-       "      <td>0.156757</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.823529</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.521739</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.055184</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.523810</td>\n",
-       "      <td>0.627451</td>\n",
-       "      <td>0.103641</td>\n",
-       "      <td>0.466667</td>\n",
-       "      <td>0.516129</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "</div>"
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-      "text/plain": [
-       "                    0                   1   2     3   4         5         6   \\\n",
-       "0         text_7_props  19k_class_childPar   2  True   1  0.433333  0.468750   \n",
-       "1         text_2_props               19k_2   2  True   3  0.393443  0.430769   \n",
-       "2              complex  19k_class_childPar   1  True   3  0.620690  0.727273   \n",
-       "3               transe        19k_childPar   1  True   2  0.600000  0.756757   \n",
-       "4             abstract  19k_class_childPar   2  True   1  0.521739  0.576923   \n",
-       "5  abstract_first_sent        19k_childPar   2  True   3  0.523810  0.627451   \n",
-       "\n",
-       "         7         8         9     10    11  \n",
-       "0  0.035417  0.325000  0.340909  0.65  0.75  \n",
-       "1  0.037327  0.295455  0.311111  0.65  0.70  \n",
-       "2  0.106583  0.916667  0.923077  0.55  0.60  \n",
-       "3  0.156757  0.846154  0.823529  0.55  0.70  \n",
-       "4  0.055184  0.461538  0.468750  0.60  0.75  \n",
-       "5  0.103641  0.466667  0.516129  0.70  0.80  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findPrecRecIMaximals(unsupSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "viral-warrant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findPrecRecUMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['Embedding']\n",
-    "        basis = row['Basis']\n",
-    "        newAcc = row['New U F1-Score']\n",
-    "        oldAcc = row['Old U F1-Score']\n",
-    "        iteration = row['Iteration Num']\n",
-    "        if emb not in embDict:\n",
-    "            embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': -1}\n",
-    "        else:\n",
-    "            if newAcc > embDict[emb]['maxNewAcc'] or (newAcc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                embDict[emb] = {'maxNewAcc': newAcc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "        if iteration == 1:\n",
-    "            embDict[emb]['oldAcc'] = oldAcc\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list()[:5], embDict[emb]['oldAcc'], embDict[emb]['maxRow']['New U F1-Score'], embDict[emb]['maxRow']['New U F1-Score'] - embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Old U Precision'], embDict[emb]['maxRow']['New U Precision'], embDict[emb]['maxRow']['Old U Recall'], embDict[emb]['maxRow']['New U Recall'], ])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "norwegian-potato",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.536232</td>\n",
-       "      <td>0.542636</td>\n",
-       "      <td>0.006404</td>\n",
-       "      <td>0.438272</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.689320</td>\n",
-       "      <td>0.679612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.366667</td>\n",
-       "      <td>0.379310</td>\n",
-       "      <td>0.012644</td>\n",
-       "      <td>0.434211</td>\n",
-       "      <td>0.464789</td>\n",
-       "      <td>0.320388</td>\n",
-       "      <td>0.320388</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.089286</td>\n",
-       "      <td>0.090909</td>\n",
-       "      <td>0.001623</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.048544</td>\n",
-       "      <td>0.048544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.155172</td>\n",
-       "      <td>0.123894</td>\n",
-       "      <td>-0.031279</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.067961</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    0                   1   2     3   4         5         6   \\\n",
-       "0         text_7_props         19k_2_class   2  True   3  0.065574  0.065574   \n",
-       "1         text_2_props         19k_2_class   2  True   1  0.065574  0.065574   \n",
-       "2              complex        19k_childPar   2  True   2  0.536232  0.542636   \n",
-       "3               transe         19k_2_class   2  True   3  0.366667  0.379310   \n",
-       "4             abstract         19k_2_class   2  True   2  0.089286  0.090909   \n",
-       "5  abstract_first_sent  19k_class_siblings   2  True   2  0.155172  0.123894   \n",
-       "\n",
-       "         7         8         9         10        11  \n",
-       "0  0.000000  0.210526  0.210526  0.038835  0.038835  \n",
-       "1  0.000000  0.210526  0.210526  0.038835  0.038835  \n",
-       "2  0.006404  0.438272  0.451613  0.689320  0.679612  \n",
-       "3  0.012644  0.434211  0.464789  0.320388  0.320388  \n",
-       "4  0.001623  0.625000  0.714286  0.048544  0.048544  \n",
-       "5 -0.031279  0.700000  0.700000  0.067961  0.067961  "
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unsupSumm = pd.read_csv('../data/retrofitting/retro_test_Sept_21_2021.csv')\n",
-    "findPrecRecUMaximals(unsupSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "international-asbestos",
-   "metadata": {},
-   "outputs": [
-    {
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-     "execution_count": 39,
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-    "concatSumm = pd.read_csv('../data/retrofitting/retro_concat_Sep_21_2021.csv')\n",
-    "findMaximals(concatSumm)"
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-   "cell_type": "code",
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-    "concatSumm = pd.read_csv('../data/retrofitting/retro_WikiCS_Sep_27_2021.csv')\n",
-    "findMaximals(concatSumm)"
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-   "cell_type": "code",
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-   "id": "regional-teens",
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-       "                    0            1   2     3   4          5          6   \\\n",
-       "0         text_7_props  19k_2_class   1  True   1  18.991529  19.007664   \n",
-       "1         text_2_props  19k_2_class   2  True   3  18.991529  19.007664   \n",
-       "2              complex        19k_2   1  True   3  81.661960  81.920129   \n",
-       "3               transe        19k_2   1  True   3  82.412263  82.476805   \n",
-       "4             abstract  19k_2_class   2  True   3  18.112142  18.112142   \n",
-       "5  abstract_first_sent  19k_2_class   2  True   3  18.104074  18.112142   \n",
-       "\n",
-       "         7    8   9   ...  19  20        21        22        23        24  \\\n",
-       "0  0.016136  599 NaN  ... NaN NaN  0.984828  0.985747  0.020744  0.020744   \n",
-       "1  0.016136  599 NaN  ... NaN NaN  0.984828  0.985747  0.020744  0.020744   \n",
-       "2  0.258169  595 NaN  ... NaN NaN  0.092874  0.092874  0.970646  0.973777   \n",
-       "3  0.064542  595 NaN  ... NaN NaN  0.092874  0.093793  0.979746  0.980333   \n",
-       "4  0.000000  297 NaN  ... NaN NaN  0.991724  0.991724  0.008611  0.008611   \n",
-       "5  0.008068  297 NaN  ... NaN NaN  0.991724  0.991724  0.008513  0.008611   \n",
-       "\n",
-       "           25  26         27        28  \n",
-       "0   45.268861   0  18.991529  0.016136  \n",
-       "1  281.146679   0  18.991529  0.016136  \n",
-       "2  152.884296   0  81.339250  0.580879  \n",
-       "3  152.751550   0  82.129891  0.346914  \n",
-       "4   72.580956   1  18.112142  0.000000  \n",
-       "5   71.931216   0  18.087939  0.024203  \n",
-       "\n",
-       "[6 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatSumm = pd.read_csv('../data/retrofitting/retro_conceptNet_Sep_27_2021.csv')\n",
-    "findMaximals(concatSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "geographic-shadow",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "shaped-cargo",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "double-egyptian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findSVMMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['emb']\n",
-    "        basis = row['basis']\n",
-    "        acc = row['Acc']\n",
-    "        iteration = row['iteration']\n",
-    "        if iteration == 0:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': -1, 'maxRow': row, 'oldAcc': acc}\n",
-    "            else:\n",
-    "                embDict[emb] = {'maxNewAcc': embDict[emb]['maxNewAcc'], 'maxRow': embDict[emb]['maxRow'], 'oldAcc': acc}\n",
-    "        else:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': -1}\n",
-    "            else:\n",
-    "                if acc > embDict[emb]['maxNewAcc'] or (acc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                    embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['Acc'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "departmental-contractor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>text_7_props_19k_class_childPar_1_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.694370</td>\n",
-       "      <td>0.680000</td>\n",
-       "      <td>0.014370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>text_2_props_19k_class_childPar_2_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.694622</td>\n",
-       "      <td>0.677311</td>\n",
-       "      <td>0.017311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_19k_2_class_2_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>complex</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668487</td>\n",
-       "      <td>0.674538</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668403</td>\n",
-       "      <td>0.674454</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.712017</td>\n",
-       "      <td>0.683193</td>\n",
-       "      <td>0.028824</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent_19k_2_2_weighted</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.711681</td>\n",
-       "      <td>0.714622</td>\n",
-       "      <td>-0.002941</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            0                   1  \\\n",
-       "0  text_7_props_19k_class_childPar_1_weighted  19k_class_childPar   \n",
-       "1  text_2_props_19k_class_childPar_2_weighted  19k_class_childPar   \n",
-       "2              complex_19k_2_class_2_weighted         19k_2_class   \n",
-       "3               transe_19k_2_class_1_weighted         19k_2_class   \n",
-       "4      abstract_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "5        abstract_first_sent_19k_2_2_weighted               19k_2   \n",
-       "\n",
-       "                     2     3  4  5      6         7         8         9  \n",
-       "0         text_7_props  True  2  1  score  0.694370  0.680000  0.014370  \n",
-       "1         text_2_props  True  2  2  score  0.694622  0.677311  0.017311  \n",
-       "2              complex  True  2  2  score  0.668487  0.674538 -0.006050  \n",
-       "3               transe  True  2  1  score  0.668403  0.674454 -0.006050  \n",
-       "4             abstract  True  2  2  score  0.712017  0.683193  0.028824  \n",
-       "5  abstract_first_sent  True  1  2  score  0.711681  0.714622 -0.002941  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmSumm = pd.read_csv('../data/retrofitting/retro_SVM_Reg_test_Sept_21_2021.csv')\n",
-    "findSVMMaximals(svmSumm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "metallic-alberta",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findSVMCombMaximals(df):\n",
-    "    embDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        emb = row['emb_comb']\n",
-    "        basis = row['basis']\n",
-    "        acc = row['accuracy']\n",
-    "        iteration = row['iteration']\n",
-    "        if iteration == 0:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': -1, 'maxRow': row, 'oldAcc': acc}\n",
-    "            else:\n",
-    "                embDict[emb] = {'maxNewAcc': embDict[emb]['maxNewAcc'], 'maxRow': embDict[emb]['maxRow'], 'oldAcc': acc}\n",
-    "        else:\n",
-    "            if emb not in embDict:\n",
-    "                embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': -1}\n",
-    "            else:\n",
-    "                if acc > embDict[emb]['maxNewAcc'] or (acc == embDict[emb]['maxNewAcc'] and 'class' in basis):\n",
-    "                    embDict[emb] = {'maxNewAcc': acc, 'maxRow': row, 'oldAcc': embDict[emb]['oldAcc']}\n",
-    "    finalRows = []\n",
-    "    for emb in embDict:\n",
-    "        finalRows.append([*embDict[emb]['maxRow'].to_list(), embDict[emb]['oldAcc'], embDict[emb]['maxRow']['accuracy'] - embDict[emb]['oldAcc']])\n",
-    "    return pd.DataFrame(finalRows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "arabic-pointer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>abstract_first_sent_19k_2_2_weighted</td>\n",
-       "      <td>19k_2</td>\n",
-       "      <td>text_2_props &amp; abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.726555</td>\n",
-       "      <td>0.717815</td>\n",
-       "      <td>0.008739</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>abstract_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>complex &amp; transe &amp; abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.726303</td>\n",
-       "      <td>0.679916</td>\n",
-       "      <td>0.046387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>abstract_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>text_7_props &amp; text_2_props &amp; transe &amp; abstract</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.724034</td>\n",
-       "      <td>0.697395</td>\n",
-       "      <td>0.026639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>abstract_first_sent_19k_class_childPar_2_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_7_props &amp; text_2_props &amp; transe &amp; abstrac...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.721092</td>\n",
-       "      <td>0.714790</td>\n",
-       "      <td>0.006303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>abstract_first_sent_19k_childPar_1_weighted</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>text_7_props &amp; complex &amp; abstract &amp; abstract_f...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.720924</td>\n",
-       "      <td>0.665294</td>\n",
-       "      <td>0.055630</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>text_2_props_19k_class_childPar_1_weighted</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>text_7_props &amp; text_2_props</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.694370</td>\n",
-       "      <td>0.674286</td>\n",
-       "      <td>0.020084</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>abstract_first_sent_19k_class_siblings_2_weighted</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>abstract &amp; abstract_first_sent</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.691429</td>\n",
-       "      <td>0.682857</td>\n",
-       "      <td>0.008571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>transe_19k_siblings_2_weighted</td>\n",
-       "      <td>19k_siblings</td>\n",
-       "      <td>complex &amp; transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668655</td>\n",
-       "      <td>0.662689</td>\n",
-       "      <td>0.005966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_19k_2_class_2_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>complex</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668487</td>\n",
-       "      <td>0.674538</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe_19k_2_class_1_weighted</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>transe</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>score</td>\n",
-       "      <td>0.668403</td>\n",
-       "      <td>0.674454</td>\n",
-       "      <td>-0.006050</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>63 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    0                   1  \\\n",
-       "14               abstract_first_sent_19k_2_2_weighted               19k_2   \n",
-       "37             abstract_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "44                    abstract_19k_2_class_1_weighted         19k_2_class   \n",
-       "45  abstract_first_sent_19k_class_childPar_2_weighted  19k_class_childPar   \n",
-       "49        abstract_first_sent_19k_childPar_1_weighted        19k_childPar   \n",
-       "..                                                ...                 ...   \n",
-       "6          text_2_props_19k_class_childPar_1_weighted  19k_class_childPar   \n",
-       "20  abstract_first_sent_19k_class_siblings_2_weighted  19k_class_siblings   \n",
-       "15                     transe_19k_siblings_2_weighted        19k_siblings   \n",
-       "2                      complex_19k_2_class_2_weighted         19k_2_class   \n",
-       "3                       transe_19k_2_class_1_weighted         19k_2_class   \n",
-       "\n",
-       "                                                    2     3  4  5      6  \\\n",
-       "14                 text_2_props & abstract_first_sent  True  1  2  score   \n",
-       "37                        complex & transe & abstract  True  3  2  score   \n",
-       "44    text_7_props & text_2_props & transe & abstract  True  3  1  score   \n",
-       "45  text_7_props & text_2_props & transe & abstrac...  True  3  2  score   \n",
-       "49  text_7_props & complex & abstract & abstract_f...  True  1  1  score   \n",
-       "..                                                ...   ... .. ..    ...   \n",
-       "6                         text_7_props & text_2_props  True  3  1  score   \n",
-       "20                     abstract & abstract_first_sent  True  3  2  score   \n",
-       "15                                   complex & transe  True  1  2  score   \n",
-       "2                                             complex  True  3  2  score   \n",
-       "3                                              transe  True  3  1  score   \n",
-       "\n",
-       "           7         8         9  \n",
-       "14  0.726555  0.717815  0.008739  \n",
-       "37  0.726303  0.679916  0.046387  \n",
-       "44  0.724034  0.697395  0.026639  \n",
-       "45  0.721092  0.714790  0.006303  \n",
-       "49  0.720924  0.665294  0.055630  \n",
-       "..       ...       ...       ...  \n",
-       "6   0.694370  0.674286  0.020084  \n",
-       "20  0.691429  0.682857  0.008571  \n",
-       "15  0.668655  0.662689  0.005966  \n",
-       "2   0.668487  0.674538 -0.006050  \n",
-       "3   0.668403  0.674454 -0.006050  \n",
-       "\n",
-       "[63 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmCombSumm = pd.read_csv('../data/retrofitting/retro_SVM_Reg_comb_test_Sept_21_2021.csv')\n",
-    "findSVMCombMaximals(svmCombSumm).sort_values(by=[7], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "stone-secretary",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
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diff --git a/Archived/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC-checkpoint.ipynb b/Archived/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC-checkpoint.ipynb
deleted file mode 100644
index 30bb697..0000000
--- a/Archived/.ipynb_checkpoints/Retrofitting using 19k + 19k dataset REEXEC-checkpoint.ipynb	
+++ /dev/null
@@ -1,14914 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "strange-saturn",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "random-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "incorporated-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'id', 'node1_label', 'label', 'node2_label', 'sent',\n",
-       "       'node1_emb', 'node2_emb', 'bert2SentSim', 'abstract',\n",
-       "       'abstract_firstSent', 'BERT_abstract_emb',\n",
-       "       'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'textEmb_7props_left',\n",
-       "       'textEmb_7props_right', 'textEmb_2props_left', 'textEmb_2props_right',\n",
-       "       'textEmb_7props_cosSim', 'textEmb_2props_cosSim',\n",
-       "       'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.772184\n",
-       "std          0.122326\n",
-       "min          0.120480\n",
-       "25%          0.684141\n",
-       "50%          0.793718\n",
-       "75%          0.874284\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "under-rabbit",
-   "metadata": {},
-   "source": [
-    "# Embeddings Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "annoying-intake",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Complex, Transe Embeddings Datasets generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "tamil-activity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "immune-nashville",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "residential-cleaners",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "current-great",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id  \n",
-       "0        Q454  \n",
-       "1   Q13648784  \n",
-       "2     Q178022  \n",
-       "3   Q21004260  \n",
-       "4      Q49112  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "valued-progressive",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# wordSimSet = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "different-vancouver",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list() + p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list() + wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-lewis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19166"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "collected-regular",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1499717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4765290\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16000518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6423382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2177259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q100448831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2857578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13577338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q96158854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7892\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7675683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2380954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21083881\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q211521\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16023742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q97627995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16721350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q32979618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4553362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q98232491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66194218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5372\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25481995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61466331\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6983403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84105477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30107768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56753514\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91927989\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12047900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11698973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q517386\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17097928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19838691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q101541623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17376918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23013268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66363580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55693905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68131879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85803433\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21406831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29957548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78710574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6462051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1042920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7925\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28324850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2849391\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "elect-gothic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(compEmbeddings.items(), columns=['node', 'complex_embedding']).to_csv('../data/wordsim353_complex_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "combined-theme",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(transeEmbeddings.items(), columns=['node', 'transe_embedding']).to_csv('../data/wordsim353_transe_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-extraction",
-   "metadata": {},
-   "source": [
-    "# Target Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hairy-tuition",
-   "metadata": {},
-   "source": [
-    "## Probase Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "opened-quarter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = pd.read_csv('../data/probase/probase_WQnodes_subset_and_sim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "major-peter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1_qnode</th>\n",
-       "      <th>node2_qnode</th>\n",
-       "      <th>n1_final_qnode</th>\n",
-       "      <th>n2_final_qnode</th>\n",
-       "      <th>sim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>gender</td>\n",
-       "      <td>14230</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>0.913568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>temperature</td>\n",
-       "      <td>13660</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>P2076</td>\n",
-       "      <td>0.909663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>7</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>copper</td>\n",
-       "      <td>11142</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>Q15830500</td>\n",
-       "      <td>0.890199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>variable</td>\n",
-       "      <td>age</td>\n",
-       "      <td>9375</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q10954303</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>0.873703</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0 node1_label  node2_label  no_of_relations node1_qnode  \\\n",
-       "0           0      factor          age            35167         NaN   \n",
-       "1           5      factor       gender            14230         NaN   \n",
-       "2           6      factor  temperature            13660         NaN   \n",
-       "3           7       metal       copper            11142      Q11426   \n",
-       "4           9    variable          age             9375         NaN   \n",
-       "\n",
-       "  node2_qnode n1_final_qnode n2_final_qnode       sim  \n",
-       "0         NaN      Q15061738     Q100343219  1.000000  \n",
-       "1      Q48277      Q15061738         Q48277  0.913568  \n",
-       "2         NaN      Q15061738          P2076  0.909663  \n",
-       "3         NaN         Q11426      Q15830500  0.890199  \n",
-       "4         NaN      Q10954303     Q100343219  0.873703  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "photographic-character",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes1_subset.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'bert2SentSim'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "russian-proceeding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'] = 0.5 + 0.5 * probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "painted-selection",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.600841e+06\n",
-       "mean     5.429549e-01\n",
-       "std      5.363352e-02\n",
-       "min      5.000000e-01\n",
-       "25%      5.000000e-01\n",
-       "50%      5.331083e-01\n",
-       "75%      5.662167e-01\n",
-       "max      1.000000e+00\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "consolidated-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "irish-disposal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "            sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-milan",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
-   "source": [
-    "## Sample attempt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "substantial-aggregate",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text_sample = p279WordSimSeededDF_wabs_text[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "analyzed-passion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "japanese-defense",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dying-nigeria",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "reduced-avenue",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1034"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "advised-hollywood",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "authentic-greene",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1034.000000\n",
-       "mean        0.281990\n",
-       "std         0.054759\n",
-       "min         0.069792\n",
-       "25%         0.244698\n",
-       "50%         0.278019\n",
-       "75%         0.319656\n",
-       "max         0.450444\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "variable-transportation",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
-   "source": [
-    "## ChildPar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "central-spyware",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "white-genesis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "registered-original",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "experienced-conspiracy",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "every-interface",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "specified-berry",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.283712\n",
-       "std          0.056573\n",
-       "min          0.050719\n",
-       "25%          0.246137\n",
-       "50%          0.281897\n",
-       "75%          0.319687\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incoming-calvin",
-   "metadata": {},
-   "source": [
-    "# Correlation Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "answering-exhaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "    print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "    print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "    print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    return wordSimMissingSet, cm_old, cm_new"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "premier-crisis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "confusionMatrixMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scientific-heading",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "stretch-elizabeth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "sapphire-bulgarian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "rising-lawyer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "closed-charles",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.271898\n",
-      "std          0.057984\n",
-      "min          0.000000\n",
-      "25%          0.231530\n",
-      "50%          0.268294\n",
-      "75%          0.308696\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3876948460943732, pvalue=2.944054929116869e-23)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41910186320873755, pvalue=2.2966918648680457e-29)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5153374233128835\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.193850\n",
-      "std          0.113418\n",
-      "min          0.000000\n",
-      "25%          0.086588\n",
-      "50%          0.192671\n",
-      "75%          0.291258\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3896871945725784, pvalue=1.7567320499364868e-23)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.429300613416863, pvalue=9.889526081071043e-31)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5920245398773006\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.325848\n",
-      "std          0.071942\n",
-      "min          0.000000\n",
-      "25%          0.277981\n",
-      "50%          0.321763\n",
-      "75%          0.370070\n",
-      "max          0.648638\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.380602867202403, pvalue=1.7935601808141945e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3995893685604078, pvalue=7.608607437786723e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.39570552147239263\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_7props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['text_7props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_7props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accessory-isolation",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings - 2 props | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "bored-sweden",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "speaking-sharing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "addressed-march",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "latter-spelling",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.270895\n",
-      "std          0.059990\n",
-      "min          0.000000\n",
-      "25%          0.228547\n",
-      "50%          0.266816\n",
-      "75%          0.309132\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3793643502923977, pvalue=2.45298661169592e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40586371991954756, pvalue=1.2142237724855381e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.49079754601226994\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.194648\n",
-      "std          0.115900\n",
-      "min          0.000000\n",
-      "25%          0.083470\n",
-      "50%          0.191821\n",
-      "75%          0.294976\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.36958425592956784, pvalue=2.7849781516020684e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39348934596160956, pvalue=4.414924758703321e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5766871165644172\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.323673\n",
-      "std          0.069955\n",
-      "min          0.000000\n",
-      "25%          0.277272\n",
-      "50%          0.319275\n",
-      "75%          0.365774\n",
-      "max          0.677490\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3746327832763941, pvalue=8.004468522493588e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39398189437020814, pvalue=3.8343633718362455e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.3834355828220859\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_2props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_2props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['text_2props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_2props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "promising-estonia",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "flexible-creature",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "sustainable-nurse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "amino-there",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "yellow-estate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "necessary-house",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316720\n",
-      "std          0.076381\n",
-      "min          0.000000\n",
-      "25%          0.261478\n",
-      "50%          0.313781\n",
-      "75%          0.370851\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3168500395328192, pvalue=7.486323665386832e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27202499209162273, pvalue=6.770258859650813e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.232133\n",
-      "std          0.142269\n",
-      "min          0.000000\n",
-      "25%          0.089938\n",
-      "50%          0.228553\n",
-      "75%          0.361251\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3361212489826738, pvalue=9.03613034020374e-19)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.29248433284517855, pvalue=7.890529591252537e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6618075801749271\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.375082\n",
-      "std          0.073982\n",
-      "min          0.000000\n",
-      "25%          0.330119\n",
-      "50%          0.377307\n",
-      "75%          0.421860\n",
-      "max          0.841769\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.26508277549218445, pvalue=3.0194195465523546e-12)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21338136285025483, pvalue=4.17252744630606e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5830903790087464\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['complex_19k_old_'+str(weightCase)], confusionMatrixMaster['complex_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "racial-tutorial",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "dominant-youth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "complicated-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "interstate-worst",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "color-newspaper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "sealed-undergraduate",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327802\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23996930346932127, pvalue=2.6942170209534906e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21449104599327523, pvalue=3.4760310637715348e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237343\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24439786532398405, pvalue=1.2600161404554345e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22590625729631375, pvalue=4.917533132159808e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.394277\n",
-      "std          0.086197\n",
-      "min          0.000000\n",
-      "25%          0.339162\n",
-      "50%          0.397921\n",
-      "75%          0.452267\n",
-      "max          0.850905\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24274131207452157, pvalue=1.6736422306859635e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.201734473314897, pvalue=2.7559659468673134e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amber-priority",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "coastal-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "familiar-springer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "duplicate-storm",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "unsigned-hungarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "mental-coaching",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.887688e-02\n",
-      "std      1.377091e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.314421e-01\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31031745482774675, pvalue=3.150145092040765e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.26161279947706156, pvalue=5.774092784287413e-13)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6268221574344023\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.252887e-02\n",
-      "std      8.773144e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.094678e-03\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.35020973149202944, pvalue=3.059720074110226e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3013432304449195, pvalue=1.0451590470362559e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6588921282798834\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.251606e-01\n",
-      "std      2.222320e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.940167e-01\n",
-      "max      8.900593e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.14149430916593933, pvalue=0.00019608082976567604)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14687524939411561, pvalue=5.22383486460822e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.1457725947521866\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sticky-light",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "normal-house",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "reserved-sport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "norman-graph",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "identical-amsterdam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "knowing-register",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "inside-opportunity",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     8.215694e-02\n",
-      "std      1.439020e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.305726e-01\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22012582471779607, pvalue=6.9001945281841384e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.19941082640413085, pvalue=3.969975785916229e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6472303206997084\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.399453e-02\n",
-      "std      9.216743e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.230265e-03\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24783606125619878, pvalue=6.903890636652704e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22349921038500295, pvalue=7.473348989752392e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.302927e-01\n",
-      "std      2.320257e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.960976e-01\n",
-      "max      9.949517e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12855892543728606, pvalue=0.0007155142288469726)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.13749877727529278, pvalue=0.00015240104075019828)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.17784256559766765\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "listed-parent",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "sustained-benchmark",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "qualified-chase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "occupational-survival",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "intermediate-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "piano-delta",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.302721\n",
-      "std          0.067184\n",
-      "min          0.000000\n",
-      "25%          0.259615\n",
-      "50%          0.294771\n",
-      "75%          0.342928\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31582754528140355, pvalue=9.373921755437325e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27369250100948833, pvalue=4.756656623381545e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6326530612244898\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.178804\n",
-      "std          0.139703\n",
-      "min          0.000000\n",
-      "25%          0.054379\n",
-      "50%          0.137749\n",
-      "75%          0.307442\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3464370368444909, pvalue=7.656876025494246e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3011763909407297, pvalue=1.0831987277611373e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.402505\n",
-      "std          0.098028\n",
-      "min          0.000000\n",
-      "25%          0.339741\n",
-      "50%          0.393894\n",
-      "75%          0.452606\n",
-      "max          0.889403\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12191472957942284, pvalue=0.0013338871853105486)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14137053781971248, pvalue=9.875366583327447e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16034985422740525\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "square-outreach",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "powered-kidney",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "eligible-mercury",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "passing-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "catholic-flexibility",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "every-transmission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "floral-union",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316457\n",
-      "std          0.074755\n",
-      "min          0.000000\n",
-      "25%          0.265029\n",
-      "50%          0.307908\n",
-      "75%          0.361417\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22203711859153544, pvalue=5.1043215609543335e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.20512238050033801, pvalue=1.6083667850582075e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6443148688046647\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.186190\n",
-      "std          0.146220\n",
-      "min          0.000000\n",
-      "25%          0.056864\n",
-      "50%          0.144985\n",
-      "75%          0.307240\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2479273527617227, pvalue=6.816190052635217e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2263056114290315, pvalue=4.5887579264134097e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.597667638483965\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.421187\n",
-      "std          0.108116\n",
-      "min          0.000000\n",
-      "25%          0.350740\n",
-      "50%          0.416101\n",
-      "75%          0.480072\n",
-      "max          0.972091\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12731453008255086, pvalue=0.0008059106457544173)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1476648434866464, pvalue=4.7610094969077205e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.2594752186588921\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "organic-earth",
-   "metadata": {},
-   "source": [
-    "### Abstract Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "royal-update",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "straight-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "pursuant-compact",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "australian-legend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "advanced-pattern",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "funded-third",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "identical-aspect",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "specific-namibia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "affected-consolidation",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.3354244232177734 s\n",
-      "0.11755561828613281 s\n",
-      "0.11744570732116699 s\n",
-      "0.1175832748413086 s\n",
-      "0.11750054359436035 s\n",
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-      "0.11713695526123047 s\n",
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-      "0.11743450164794922 s\n",
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-      "0.11734700202941895 s\n",
-      "0.11752867698669434 s\n",
-      "0.11753010749816895 s\n",
-      "0.11727476119995117 s\n",
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-      "0.11744475364685059 s\n",
-      "0.1178896427154541 s\n",
-      "0.11759018898010254 s\n",
-      "0.11737680435180664 s\n",
-      "0.11777734756469727 s\n",
-      "0.11771225929260254 s\n",
-      "0.11792659759521484 s\n",
-      "0.1174314022064209 s\n",
-      "0.1175081729888916 s\n",
-      "0.11757636070251465 s\n",
-      "0.11760926246643066 s\n",
-      "0.11748123168945312 s\n",
-      "0.11753368377685547 s\n",
-      "0.11712884902954102 s\n",
-      "0.11767840385437012 s\n",
-      "0.11764121055603027 s\n",
-      "0.11763525009155273 s\n",
-      "0.11754012107849121 s\n",
-      "0.11885714530944824 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "fuzzy-attack",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "hawaiian-portal",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.336606\n",
-      "std          0.081060\n",
-      "min          0.000000\n",
-      "25%          0.278247\n",
-      "50%          0.326289\n",
-      "75%          0.388139\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3471431474655109, pvalue=5.660815639939061e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.35383380764979844, pvalue=1.6754686847593527e-22)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5697674418604651\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244583\n",
-      "std          0.150115\n",
-      "min          0.000000\n",
-      "25%          0.103316\n",
-      "50%          0.233314\n",
-      "75%          0.372639\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.34735894519691607, pvalue=5.347633678079643e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3462632061224712, pvalue=1.2793035570514484e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.400089\n",
-      "std          0.083229\n",
-      "min          0.000000\n",
-      "25%          0.344853\n",
-      "50%          0.397861\n",
-      "75%          0.451782\n",
-      "max          0.835283\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.32390174521065745, pvalue=1.3650315049340146e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3325159043509967, pvalue=4.65056862032923e-20)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.47093023255813954\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['abstract_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "central-valley",
-   "metadata": {},
-   "source": [
-    "### Abstract 1st Sentence Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "occupied-emission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "sexual-press",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "cognitive-import",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "irish-north",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "neural-communications",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "apart-church",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "monthly-enough",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "varying-plaza",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "changing-cycle",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11401677131652832 s\n",
-      "0.11957383155822754 s\n",
-      "0.09752821922302246 s\n",
-      "0.128997802734375 s\n",
-      "0.11990189552307129 s\n",
-      "0.11844491958618164 s\n",
-      "0.11752724647521973 s\n",
-      "0.11877274513244629 s\n",
-      "0.11719059944152832 s\n",
-      "0.11760425567626953 s\n",
-      "0.11708760261535645 s\n",
-      "0.09807181358337402 s\n",
-      "0.0916135311126709 s\n",
-      "0.09286952018737793 s\n",
-      "0.0907738208770752 s\n",
-      "0.0915842056274414 s\n",
-      "0.09347748756408691 s\n",
-      "0.08993768692016602 s\n",
-      "0.10148978233337402 s\n",
-      "0.11854410171508789 s\n",
-      "0.12027955055236816 s\n",
-      "0.13148927688598633 s\n",
-      "0.11908864974975586 s\n",
-      "0.13748478889465332 s\n",
-      "0.13097643852233887 s\n",
-      "0.11984562873840332 s\n",
-      "0.12159109115600586 s\n",
-      "0.12565040588378906 s\n",
-      "0.1192171573638916 s\n",
-      "0.14190435409545898 s\n",
-      "0.128098726272583 s\n",
-      "0.1173861026763916 s\n",
-      "0.11848211288452148 s\n",
-      "0.11741471290588379 s\n",
-      "0.11730766296386719 s\n",
-      "0.12353205680847168 s\n",
-      "0.11716032028198242 s\n",
-      "0.11889147758483887 s\n",
-      "0.11724376678466797 s\n",
-      "0.11714792251586914 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "documentary-midnight",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "sixth-paradise",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.339064\n",
-      "std          0.072734\n",
-      "min          0.000000\n",
-      "25%          0.287403\n",
-      "50%          0.334725\n",
-      "75%          0.388172\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3571490077264898, pvalue=4.7415066582386246e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3713906787854944, pvalue=1.253257701630852e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6017441860465116\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244476\n",
-      "std          0.145834\n",
-      "min          0.000000\n",
-      "25%          0.104152\n",
-      "50%          0.234678\n",
-      "75%          0.376765\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.37186923032141733, pvalue=1.0985961673989065e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.37051869405377086, pvalue=1.6123151472069758e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6540697674418605\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.404541\n",
-      "std          0.081848\n",
-      "min          0.000000\n",
-      "25%          0.351736\n",
-      "50%          0.401690\n",
-      "75%          0.453416\n",
-      "max          0.872588\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31762341611315487, pvalue=5.624902334302071e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.33907069116618244, pvalue=8.52432972070843e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.4941860465116279\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_firstSent_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_firstSent_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['abstract_firstSent_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_firstSent_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-cameroon",
-   "metadata": {},
-   "source": [
-    "### Combination of all old embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "smooth-playlist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', 'complex_probase', 'transe_probase', 'complex_probase_19k', 'transe_probase_19k', 'abstract_19k', 'abstract_firstSent_19k'])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "material-design",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k_1', 'text_7props_19k_2', 'text_7props_19k_0.5', 'text_2props_19k_1', 'text_2props_19k_2', 'text_2props_19k_0.5', 'complex_19k_1', 'complex_19k_2', 'complex_19k_0.5', 'transe_19k_1', 'transe_19k_2', 'transe_19k_0.5', 'complex_probase_1', 'complex_probase_2', 'complex_probase_0.5', 'transe_probase_1', 'transe_probase_2', 'transe_probase_0.5', 'complex_probase_19k_1', 'complex_probase_19k_2', 'complex_probase_19k_0.5', 'transe_probase_19k_1', 'transe_probase_19k_2', 'transe_probase_19k_0.5', 'abstract_19k_1', 'abstract_19k_2', 'abstract_19k_0.5', 'abstract_firstSent_19k_1', 'abstract_firstSent_19k_2', 'abstract_firstSent_19k_0.5'])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "verbal-frederick",
-   "metadata": {},
-   "source": [
-    "#### Compl + Transe + Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "removed-conditions",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['complex_19k', 'transe_19k', \n",
-    "            'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "indie-washington",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 968\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "caring-wellington",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 968:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "twelve-tribute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19101.0\n",
-       "mean       968.0\n",
-       "std          0.0\n",
-       "min        968.0\n",
-       "25%        968.0\n",
-       "50%        968.0\n",
-       "75%        968.0\n",
-       "max        968.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "animated-weight",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.336903\n",
-      "std          0.071873\n",
-      "min          0.000000\n",
-      "25%          0.284286\n",
-      "50%          0.332284\n",
-      "75%          0.389200\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31344285039654224, pvalue=1.587990989990702e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2983652058391759, pvalue=2.0692364624319403e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6676384839650146\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.245075\n",
-      "std          0.146398\n",
-      "min          0.000000\n",
-      "25%          0.099960\n",
-      "50%          0.239725\n",
-      "75%          0.380577\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3270255968193857, pvalue=7.482780885295614e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3144288703997577, pvalue=4.696112489737555e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6880466472303207\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.400303\n",
-      "std          0.071506\n",
-      "min          0.000000\n",
-      "25%          0.357059\n",
-      "50%          0.402058\n",
-      "75%          0.445388\n",
-      "max          0.813749\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2967991230842074, pvalue=5.639053240442108e-15)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2739615906247492, pvalue=4.489403776140874e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "suspected-footage",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.321205\n",
-      "std          0.063429\n",
-      "min          0.000000\n",
-      "25%          0.277493\n",
-      "50%          0.311720\n",
-      "75%          0.359268\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2891828519936888, pvalue=2.717366700252014e-14)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27713398557337643, pvalue=2.2907553462981174e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6501457725947521\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.188928\n",
-      "std          0.144828\n",
-      "min          0.000000\n",
-      "25%          0.059667\n",
-      "50%          0.147733\n",
-      "75%          0.327197\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.33562487778756755, pvalue=1.0136749729444057e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.31665315908408964, pvalue=2.7384643013776528e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.427234\n",
-      "std          0.094690\n",
-      "min          0.000000\n",
-      "25%          0.367149\n",
-      "50%          0.418971\n",
-      "75%          0.473102\n",
-      "max          0.895330\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.13050673469358315, pvalue=0.0005927381978235836)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1636905372538761, pvalue=6.524566396115546e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.22157434402332363\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k__v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "second-norway",
-   "metadata": {},
-   "source": [
-    "#### Text_7props + Text_2props + Complex + Transe + Abstract + Abstract_FirstSent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "guided-accounting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', \n",
-    "            'abstract_19k', 'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "creative-advice",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 3784\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "apart-storm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 3784:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "foreign-merchant",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19090.0\n",
-       "mean      3784.0\n",
-       "std          0.0\n",
-       "min       3784.0\n",
-       "25%       3784.0\n",
-       "50%       3784.0\n",
-       "75%       3784.0\n",
-       "max       3784.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "aquatic-harbor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.318414\n",
-      "std          0.060577\n",
-      "min          0.000000\n",
-      "25%          0.273273\n",
-      "50%          0.313881\n",
-      "75%          0.362231\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40347307747318084, pvalue=5.629213176377413e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40536224043013125, pvalue=1.6868252763103453e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6307692307692307\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.230697\n",
-      "std          0.135101\n",
-      "min          0.000000\n",
-      "25%          0.097307\n",
-      "50%          0.223935\n",
-      "75%          0.356438\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40442451164800897, pvalue=4.371701842100601e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.4101503518414275, pvalue=4.1055209935697305e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.378997\n",
-      "std          0.061137\n",
-      "min          0.000000\n",
-      "25%          0.342927\n",
-      "50%          0.379359\n",
-      "75%          0.416067\n",
-      "max          0.760023\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3828276431671376, pvalue=1.2015280268194575e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36987136293537864, pvalue=3.618695156207996e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5415384615384615\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "behavioral-brooks",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.304466\n",
-      "std          0.054097\n",
-      "min          0.000000\n",
-      "25%          0.266260\n",
-      "50%          0.295631\n",
-      "75%          0.337646\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3563958420194592, pvalue=7.705212357023742e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36941390801600477, pvalue=4.091096997249633e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6092307692307692\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.178573\n",
-      "std          0.134771\n",
-      "min          0.000000\n",
-      "25%          0.057617\n",
-      "50%          0.140175\n",
-      "75%          0.314839\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.4167472980442226, pvalue=1.5499840293424274e-26)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41238438242524034, pvalue=2.1128593107239733e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6553846153846153\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.405175\n",
-      "std          0.085240\n",
-      "min          0.000000\n",
-      "25%          0.352177\n",
-      "50%          0.394099\n",
-      "75%          0.443428\n",
-      "max          0.797620\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.1299009113611685, pvalue=0.0008902251998159792)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1761010833372823, pvalue=2.3561612238907513e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16923076923076924\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "quality-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ideal-pipeline",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "organized-student",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alternative-cloud",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings | 19k + 19k dataset unweighted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "id": "dangerous-carrier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "fossil-exemption",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "antique-purchase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "id": "automatic-monitor",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-192-4bfdddd84fe6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0membedDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetchEmbeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextEmb_7props_DF\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0membedDictMaster\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text_7props_19k_uw'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedDict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mneighDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetchNeighbours\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp279WordSimSeededDF_wabs_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp279Seeded_SiblingsDF3_wabs_text\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"No. of keys in embedDict: {len(embedDict.keys())}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-16-fcd3022d5c0a>\u001b[0m in \u001b[0;36mfetchNeighbours\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfetchNeighbours\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mneighboursDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighboursDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mneighboursDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36miterrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1069\u001b[0m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   5482\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5483\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5484\u001b[0;31m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5485\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mname\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    536\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m         \u001b[0mvalidate_all_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"{type(self).__name__}.name\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mvalidate_all_hashable\u001b[0;34m(error_name, *args)\u001b[0m\n\u001b[1;32m   1760\u001b[0m     \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \"\"\"\n\u001b[0;32m-> 1762\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{error_name} must be a hashable type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1760\u001b[0m     \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \"\"\"\n\u001b[0;32m-> 1762\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{error_name} must be a hashable type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/inference.py\u001b[0m in \u001b[0;36mis_hashable\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m         \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props_19k_uw'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_7props_19k_uw_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['text_7props_19k_uw_old_'+str(weightCase)], confusionMatrixMaster['text_7props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "horizontal-repair",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings - 2 props | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "vital-royal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "adopted-edwards",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "equipped-vaccine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "fourth-drawing",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.270895\n",
-      "std          0.059990\n",
-      "min          0.000000\n",
-      "25%          0.228547\n",
-      "50%          0.266816\n",
-      "75%          0.309132\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3793643502923977, pvalue=2.45298661169592e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40586371991954756, pvalue=1.2142237724855381e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.49079754601226994\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.194648\n",
-      "std          0.115900\n",
-      "min          0.000000\n",
-      "25%          0.083470\n",
-      "50%          0.191821\n",
-      "75%          0.294976\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.36958425592956784, pvalue=2.7849781516020684e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39348934596160956, pvalue=4.414924758703321e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5766871165644172\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.323673\n",
-      "std          0.069955\n",
-      "min          0.000000\n",
-      "25%          0.277272\n",
-      "50%          0.319275\n",
-      "75%          0.365774\n",
-      "max          0.677490\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3746327832763941, pvalue=8.004468522493588e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39398189437020814, pvalue=3.8343633718362455e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.3834355828220859\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_2props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_2props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['text_2props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_2props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mobile-paradise",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "allied-newcastle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "abstract-survey",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "wanted-parade",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "considered-azerbaijan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "composite-shield",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316720\n",
-      "std          0.076381\n",
-      "min          0.000000\n",
-      "25%          0.261478\n",
-      "50%          0.313781\n",
-      "75%          0.370851\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3168500395328192, pvalue=7.486323665386832e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27202499209162273, pvalue=6.770258859650813e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.232133\n",
-      "std          0.142269\n",
-      "min          0.000000\n",
-      "25%          0.089938\n",
-      "50%          0.228553\n",
-      "75%          0.361251\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3361212489826738, pvalue=9.03613034020374e-19)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.29248433284517855, pvalue=7.890529591252537e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6618075801749271\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.375082\n",
-      "std          0.073982\n",
-      "min          0.000000\n",
-      "25%          0.330119\n",
-      "50%          0.377307\n",
-      "75%          0.421860\n",
-      "max          0.841769\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.26508277549218445, pvalue=3.0194195465523546e-12)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21338136285025483, pvalue=4.17252744630606e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5830903790087464\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['complex_19k_old_'+str(weightCase)], confusionMatrixMaster['complex_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "crucial-translation",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "selected-gentleman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "ranking-characterization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "fewer-average",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "rational-present",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "interstate-episode",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327802\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23996930346932127, pvalue=2.6942170209534906e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21449104599327523, pvalue=3.4760310637715348e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237343\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24439786532398405, pvalue=1.2600161404554345e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22590625729631375, pvalue=4.917533132159808e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.394277\n",
-      "std          0.086197\n",
-      "min          0.000000\n",
-      "25%          0.339162\n",
-      "50%          0.397921\n",
-      "75%          0.452267\n",
-      "max          0.850905\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24274131207452157, pvalue=1.6736422306859635e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.201734473314897, pvalue=2.7559659468673134e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "professional-cutting",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "bridal-scope",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "premium-manufacturer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "organic-above",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "removable-steps",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "rubber-principal",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.887688e-02\n",
-      "std      1.377091e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.314421e-01\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31031745482774675, pvalue=3.150145092040765e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.26161279947706156, pvalue=5.774092784287413e-13)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6268221574344023\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.252887e-02\n",
-      "std      8.773144e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.094678e-03\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.35020973149202944, pvalue=3.059720074110226e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3013432304449195, pvalue=1.0451590470362559e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6588921282798834\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.251606e-01\n",
-      "std      2.222320e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.940167e-01\n",
-      "max      8.900593e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.14149430916593933, pvalue=0.00019608082976567604)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14687524939411561, pvalue=5.22383486460822e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.1457725947521866\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "congressional-teaching",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "medieval-retail",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "artificial-dispatch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "satellite-liquid",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "muslim-gospel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "fuzzy-seven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "sapphire-prescription",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     8.215694e-02\n",
-      "std      1.439020e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.305726e-01\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22012582471779607, pvalue=6.9001945281841384e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.19941082640413085, pvalue=3.969975785916229e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6472303206997084\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.399453e-02\n",
-      "std      9.216743e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.230265e-03\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24783606125619878, pvalue=6.903890636652704e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22349921038500295, pvalue=7.473348989752392e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.302927e-01\n",
-      "std      2.320257e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.960976e-01\n",
-      "max      9.949517e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12855892543728606, pvalue=0.0007155142288469726)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.13749877727529278, pvalue=0.00015240104075019828)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.17784256559766765\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spiritual-johnson",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "monthly-mexican",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "equipped-structure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "natural-integer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "joint-revision",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "rocky-announcement",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.302721\n",
-      "std          0.067184\n",
-      "min          0.000000\n",
-      "25%          0.259615\n",
-      "50%          0.294771\n",
-      "75%          0.342928\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31582754528140355, pvalue=9.373921755437325e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27369250100948833, pvalue=4.756656623381545e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6326530612244898\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.178804\n",
-      "std          0.139703\n",
-      "min          0.000000\n",
-      "25%          0.054379\n",
-      "50%          0.137749\n",
-      "75%          0.307442\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3464370368444909, pvalue=7.656876025494246e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3011763909407297, pvalue=1.0831987277611373e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.402505\n",
-      "std          0.098028\n",
-      "min          0.000000\n",
-      "25%          0.339741\n",
-      "50%          0.393894\n",
-      "75%          0.452606\n",
-      "max          0.889403\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12191472957942284, pvalue=0.0013338871853105486)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14137053781971248, pvalue=9.875366583327447e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16034985422740525\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "damaged-future",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "stable-front",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "sublime-cleaner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "opposite-destiny",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "defined-disney",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "simplified-honolulu",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "suitable-theory",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316457\n",
-      "std          0.074755\n",
-      "min          0.000000\n",
-      "25%          0.265029\n",
-      "50%          0.307908\n",
-      "75%          0.361417\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22203711859153544, pvalue=5.1043215609543335e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.20512238050033801, pvalue=1.6083667850582075e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6443148688046647\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.186190\n",
-      "std          0.146220\n",
-      "min          0.000000\n",
-      "25%          0.056864\n",
-      "50%          0.144985\n",
-      "75%          0.307240\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2479273527617227, pvalue=6.816190052635217e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2263056114290315, pvalue=4.5887579264134097e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.597667638483965\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.421187\n",
-      "std          0.108116\n",
-      "min          0.000000\n",
-      "25%          0.350740\n",
-      "50%          0.416101\n",
-      "75%          0.480072\n",
-      "max          0.972091\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12731453008255086, pvalue=0.0008059106457544173)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1476648434866464, pvalue=4.7610094969077205e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.2594752186588921\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chemical-ministry",
-   "metadata": {},
-   "source": [
-    "### Abstract Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "unusual-wesley",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "numerous-timer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "detected-royalty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "familiar-cabinet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "overhead-intellectual",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "fifth-campbell",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "systematic-specialist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "warming-medicare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "convenient-honduras",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.3354244232177734 s\n",
-      "0.11755561828613281 s\n",
-      "0.11744570732116699 s\n",
-      "0.1175832748413086 s\n",
-      "0.11750054359436035 s\n",
-      "0.11730837821960449 s\n",
-      "0.11754989624023438 s\n",
-      "0.11766695976257324 s\n",
-      "0.11713695526123047 s\n",
-      "0.11739706993103027 s\n",
-      "0.12069988250732422 s\n",
-      "0.11744093894958496 s\n",
-      "0.11738061904907227 s\n",
-      "0.11722540855407715 s\n",
-      "0.11743450164794922 s\n",
-      "0.11770343780517578 s\n",
-      "0.11734700202941895 s\n",
-      "0.11752867698669434 s\n",
-      "0.11753010749816895 s\n",
-      "0.11727476119995117 s\n",
-      "0.11757302284240723 s\n",
-      "0.11744475364685059 s\n",
-      "0.1178896427154541 s\n",
-      "0.11759018898010254 s\n",
-      "0.11737680435180664 s\n",
-      "0.11777734756469727 s\n",
-      "0.11771225929260254 s\n",
-      "0.11792659759521484 s\n",
-      "0.1174314022064209 s\n",
-      "0.1175081729888916 s\n",
-      "0.11757636070251465 s\n",
-      "0.11760926246643066 s\n",
-      "0.11748123168945312 s\n",
-      "0.11753368377685547 s\n",
-      "0.11712884902954102 s\n",
-      "0.11767840385437012 s\n",
-      "0.11764121055603027 s\n",
-      "0.11763525009155273 s\n",
-      "0.11754012107849121 s\n",
-      "0.11885714530944824 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "documentary-puppy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "rural-spyware",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.336606\n",
-      "std          0.081060\n",
-      "min          0.000000\n",
-      "25%          0.278247\n",
-      "50%          0.326289\n",
-      "75%          0.388139\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3471431474655109, pvalue=5.660815639939061e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.35383380764979844, pvalue=1.6754686847593527e-22)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5697674418604651\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244583\n",
-      "std          0.150115\n",
-      "min          0.000000\n",
-      "25%          0.103316\n",
-      "50%          0.233314\n",
-      "75%          0.372639\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.34735894519691607, pvalue=5.347633678079643e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3462632061224712, pvalue=1.2793035570514484e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.400089\n",
-      "std          0.083229\n",
-      "min          0.000000\n",
-      "25%          0.344853\n",
-      "50%          0.397861\n",
-      "75%          0.451782\n",
-      "max          0.835283\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.32390174521065745, pvalue=1.3650315049340146e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3325159043509967, pvalue=4.65056862032923e-20)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.47093023255813954\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['abstract_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "angry-ladder",
-   "metadata": {},
-   "source": [
-    "### Abstract 1st Sentence Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "missing-velvet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "increasing-ability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "quarterly-collector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "collaborative-yield",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "associate-seminar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "falling-dublin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "north-interpretation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "willing-hanging",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "functional-mattress",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11401677131652832 s\n",
-      "0.11957383155822754 s\n",
-      "0.09752821922302246 s\n",
-      "0.128997802734375 s\n",
-      "0.11990189552307129 s\n",
-      "0.11844491958618164 s\n",
-      "0.11752724647521973 s\n",
-      "0.11877274513244629 s\n",
-      "0.11719059944152832 s\n",
-      "0.11760425567626953 s\n",
-      "0.11708760261535645 s\n",
-      "0.09807181358337402 s\n",
-      "0.0916135311126709 s\n",
-      "0.09286952018737793 s\n",
-      "0.0907738208770752 s\n",
-      "0.0915842056274414 s\n",
-      "0.09347748756408691 s\n",
-      "0.08993768692016602 s\n",
-      "0.10148978233337402 s\n",
-      "0.11854410171508789 s\n",
-      "0.12027955055236816 s\n",
-      "0.13148927688598633 s\n",
-      "0.11908864974975586 s\n",
-      "0.13748478889465332 s\n",
-      "0.13097643852233887 s\n",
-      "0.11984562873840332 s\n",
-      "0.12159109115600586 s\n",
-      "0.12565040588378906 s\n",
-      "0.1192171573638916 s\n",
-      "0.14190435409545898 s\n",
-      "0.128098726272583 s\n",
-      "0.1173861026763916 s\n",
-      "0.11848211288452148 s\n",
-      "0.11741471290588379 s\n",
-      "0.11730766296386719 s\n",
-      "0.12353205680847168 s\n",
-      "0.11716032028198242 s\n",
-      "0.11889147758483887 s\n",
-      "0.11724376678466797 s\n",
-      "0.11714792251586914 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "macro-headline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "provincial-matter",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.339064\n",
-      "std          0.072734\n",
-      "min          0.000000\n",
-      "25%          0.287403\n",
-      "50%          0.334725\n",
-      "75%          0.388172\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3571490077264898, pvalue=4.7415066582386246e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3713906787854944, pvalue=1.253257701630852e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6017441860465116\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244476\n",
-      "std          0.145834\n",
-      "min          0.000000\n",
-      "25%          0.104152\n",
-      "50%          0.234678\n",
-      "75%          0.376765\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.37186923032141733, pvalue=1.0985961673989065e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.37051869405377086, pvalue=1.6123151472069758e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6540697674418605\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.404541\n",
-      "std          0.081848\n",
-      "min          0.000000\n",
-      "25%          0.351736\n",
-      "50%          0.401690\n",
-      "75%          0.453416\n",
-      "max          0.872588\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31762341611315487, pvalue=5.624902334302071e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.33907069116618244, pvalue=8.52432972070843e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.4941860465116279\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_firstSent_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_firstSent_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['abstract_firstSent_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_firstSent_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "commercial-uzbekistan",
-   "metadata": {},
-   "source": [
-    "### Combination of all old embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "selected-grain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', 'complex_probase', 'transe_probase', 'complex_probase_19k', 'transe_probase_19k', 'abstract_19k', 'abstract_firstSent_19k'])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "remarkable-river",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k_1', 'text_7props_19k_2', 'text_7props_19k_0.5', 'text_2props_19k_1', 'text_2props_19k_2', 'text_2props_19k_0.5', 'complex_19k_1', 'complex_19k_2', 'complex_19k_0.5', 'transe_19k_1', 'transe_19k_2', 'transe_19k_0.5', 'complex_probase_1', 'complex_probase_2', 'complex_probase_0.5', 'transe_probase_1', 'transe_probase_2', 'transe_probase_0.5', 'complex_probase_19k_1', 'complex_probase_19k_2', 'complex_probase_19k_0.5', 'transe_probase_19k_1', 'transe_probase_19k_2', 'transe_probase_19k_0.5', 'abstract_19k_1', 'abstract_19k_2', 'abstract_19k_0.5', 'abstract_firstSent_19k_1', 'abstract_firstSent_19k_2', 'abstract_firstSent_19k_0.5'])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "after-wallace",
-   "metadata": {},
-   "source": [
-    "#### Compl + Transe + Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "manual-twist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['complex_19k', 'transe_19k', \n",
-    "            'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "according-identity",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 968\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "noble-fisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 968:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "liquid-spray",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19101.0\n",
-       "mean       968.0\n",
-       "std          0.0\n",
-       "min        968.0\n",
-       "25%        968.0\n",
-       "50%        968.0\n",
-       "75%        968.0\n",
-       "max        968.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "designed-killing",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.336903\n",
-      "std          0.071873\n",
-      "min          0.000000\n",
-      "25%          0.284286\n",
-      "50%          0.332284\n",
-      "75%          0.389200\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31344285039654224, pvalue=1.587990989990702e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2983652058391759, pvalue=2.0692364624319403e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6676384839650146\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.245075\n",
-      "std          0.146398\n",
-      "min          0.000000\n",
-      "25%          0.099960\n",
-      "50%          0.239725\n",
-      "75%          0.380577\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3270255968193857, pvalue=7.482780885295614e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3144288703997577, pvalue=4.696112489737555e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6880466472303207\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.400303\n",
-      "std          0.071506\n",
-      "min          0.000000\n",
-      "25%          0.357059\n",
-      "50%          0.402058\n",
-      "75%          0.445388\n",
-      "max          0.813749\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2967991230842074, pvalue=5.639053240442108e-15)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2739615906247492, pvalue=4.489403776140874e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "suburban-legislature",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.321205\n",
-      "std          0.063429\n",
-      "min          0.000000\n",
-      "25%          0.277493\n",
-      "50%          0.311720\n",
-      "75%          0.359268\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2891828519936888, pvalue=2.717366700252014e-14)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27713398557337643, pvalue=2.2907553462981174e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6501457725947521\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.188928\n",
-      "std          0.144828\n",
-      "min          0.000000\n",
-      "25%          0.059667\n",
-      "50%          0.147733\n",
-      "75%          0.327197\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.33562487778756755, pvalue=1.0136749729444057e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.31665315908408964, pvalue=2.7384643013776528e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.427234\n",
-      "std          0.094690\n",
-      "min          0.000000\n",
-      "25%          0.367149\n",
-      "50%          0.418971\n",
-      "75%          0.473102\n",
-      "max          0.895330\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.13050673469358315, pvalue=0.0005927381978235836)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1636905372538761, pvalue=6.524566396115546e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.22157434402332363\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k__v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interstate-tribute",
-   "metadata": {},
-   "source": [
-    "#### Text_7props + Text_2props + Complex + Transe + Abstract + Abstract_FirstSent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "crucial-italian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', \n",
-    "            'abstract_19k', 'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "fatal-robert",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 3784\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "inside-offset",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 3784:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "colored-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19090.0\n",
-       "mean      3784.0\n",
-       "std          0.0\n",
-       "min       3784.0\n",
-       "25%       3784.0\n",
-       "50%       3784.0\n",
-       "75%       3784.0\n",
-       "max       3784.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "promising-marijuana",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.318414\n",
-      "std          0.060577\n",
-      "min          0.000000\n",
-      "25%          0.273273\n",
-      "50%          0.313881\n",
-      "75%          0.362231\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40347307747318084, pvalue=5.629213176377413e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40536224043013125, pvalue=1.6868252763103453e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6307692307692307\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.230697\n",
-      "std          0.135101\n",
-      "min          0.000000\n",
-      "25%          0.097307\n",
-      "50%          0.223935\n",
-      "75%          0.356438\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40442451164800897, pvalue=4.371701842100601e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.4101503518414275, pvalue=4.1055209935697305e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.378997\n",
-      "std          0.061137\n",
-      "min          0.000000\n",
-      "25%          0.342927\n",
-      "50%          0.379359\n",
-      "75%          0.416067\n",
-      "max          0.760023\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3828276431671376, pvalue=1.2015280268194575e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36987136293537864, pvalue=3.618695156207996e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5415384615384615\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "piano-mortality",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.304466\n",
-      "std          0.054097\n",
-      "min          0.000000\n",
-      "25%          0.266260\n",
-      "50%          0.295631\n",
-      "75%          0.337646\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3563958420194592, pvalue=7.705212357023742e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36941390801600477, pvalue=4.091096997249633e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6092307692307692\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.178573\n",
-      "std          0.134771\n",
-      "min          0.000000\n",
-      "25%          0.057617\n",
-      "50%          0.140175\n",
-      "75%          0.314839\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.4167472980442226, pvalue=1.5499840293424274e-26)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41238438242524034, pvalue=2.1128593107239733e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6553846153846153\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.405175\n",
-      "std          0.085240\n",
-      "min          0.000000\n",
-      "25%          0.352177\n",
-      "50%          0.394099\n",
-      "75%          0.443428\n",
-      "max          0.797620\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.1299009113611685, pvalue=0.0008902251998159792)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1761010833372823, pvalue=2.3561612238907513e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16923076923076924\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "extensive-forty",
-   "metadata": {},
-   "source": [
-    "### Confusion Matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "maritime-director",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusionMatrixMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qualified-empty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = embedDictMaster['text_7props_19k']\n",
-    "newEmbedDict = newEmbedDictMaster['text_7props_19k_2']\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "\n",
-    "print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "distinct-timothy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    210\n",
-       "U    101\n",
-       "I     20\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['category'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "antique-infrastructure",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(figsize=(20,20))\n",
-    "for num, key1 in enumerate(['text_7props_19k_old_2', 'text_7props_19k_new_2', 'text_2props_19k_old_2', 'text_2props_19k_new_2', \n",
-    "             'transe_19k_old_1', 'transe_19k_new_1', 'complex_19k_old_1', 'complex_19k_new_1']):\n",
-    "    plt.subplot(4, 2, num+1)\n",
-    "    sns.heatmap(pd.DataFrame(confusionMatrixMaster[key1], LABELS, LABELS), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "micro-phrase",
-   "metadata": {},
-   "source": [
-    "# Save all embeddings and similarity scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "boring-campus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster['concat_probase_19k_v1_2'] = newEmbedDictMaster['concat_probase_19k__v1_2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "automatic-update",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d97162bbb58542999eb317ede34a3300",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "\n",
-    "for key1, key2 in tqdm(emb_pairs):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "related-opposition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "allied-sunrise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004515859562266559, -0.011723668837741234,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.384407</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.0050116109222357935, -0.013383541509188317...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004971298302113362, -0.013254267455943004,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.463734</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004862257036458548, 0.014077170447753274, ...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.005805280378137928, 0.015115419540555773, ...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004862255010517295, 0.01407716458226321, 0...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.01523113313292473, 0.005271417432183582, -0...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.850218</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013922101038066187, 0.0023911841962956006, ...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.01381456413075283, 0.0024352598782685863, -...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.895993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.013016090555331788, 0.01114268765690899, -0...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.962306</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013274960537868754, 0.010965634834463656, -...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013242882478240212, 0.010953106140234146, -...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.993596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626153814759, 0.005047111609608307, -0...</td>\n",
-       "      <td>[0.010410829355084429, 0.014776997941327313, -...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "1  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "2  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "3  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "4  ...  [0.01322626153814759, 0.005047111609608307, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859562266559, -0.011723668837741234,...                 3.468370   \n",
-       "1  [-0.004862257036458548, 0.014077170447753274, ...                 3.035534   \n",
-       "2  [0.01523113313292473, 0.005271417432183582, -0...                 2.902356   \n",
-       "3  [0.013016090555331788, 0.01114268765690899, -0...                 2.995206   \n",
-       "4  [0.010410829355084429, 0.014776997941327313, -...                 1.689315   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.384407   \n",
-       "1                    2.991152   \n",
-       "2                    2.850218   \n",
-       "3                    2.962306   \n",
-       "4                    1.689315   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.0050116109222357935, -0.013383541509188317...   \n",
-       "1  [-0.005805280378137928, 0.015115419540555773, ...   \n",
-       "2  [0.013922101038066187, 0.0023911841962956006, ...   \n",
-       "3  [0.013274960537868754, 0.010965634834463656, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.004971298302113362, -0.013254267455943004,...   \n",
-       "1  [-0.004862255010517295, 0.01407716458226321, 0...   \n",
-       "2  [0.01381456413075283, 0.0024352598782685863, -...   \n",
-       "3  [0.013242882478240212, 0.010953106140234146, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.468370                           3.463734  \n",
-       "1                         3.035534                           2.991152  \n",
-       "2                         2.902356                           2.895993  \n",
-       "3                         2.995206                           2.993596  \n",
-       "4                         1.689315                           1.689315  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "connected-limit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cons_ann_3 = pd.read_csv('../data/consolidated_annotation_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "id": "trying-authorization",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tool</td>\n",
-       "      <td>implement</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>5.313</td>\n",
-       "      <td>4.688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>arrangement</td>\n",
-       "      <td>accommodation</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.304</td>\n",
-       "      <td>5.125</td>\n",
-       "      <td>4.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>l</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>7.938</td>\n",
-       "      <td>2.063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>benchmark</td>\n",
-       "      <td>index</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>5.500</td>\n",
-       "      <td>4.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>practice</td>\n",
-       "      <td>institution</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L,I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.447</td>\n",
-       "      <td>3.563</td>\n",
-       "      <td>6.438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Word 1         Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0         tool      implement      1   NaN      3     U      1   NaN      2   \n",
-       "1  arrangement  accommodation      1   NaN      4   NaN      2     I      1   \n",
-       "2         wood         forest      3     H      1   NaN      1   NaN      1   \n",
-       "3    benchmark          index      2     I      2     I      2     I      4   \n",
-       "4     practice    institution      2     I      3   L,I      3     I      3   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg  Stdev  H_orig  H_reversed  \n",
-       "0     i      1   NaN  1.6  0.894   5.313       4.688  \n",
-       "1   NaN      1   NaN  1.8  1.304   5.125       4.875  \n",
-       "2   NaN      3     l  1.8  1.095   7.938       2.063  \n",
-       "3   NaN      4   NaN  2.8  1.095   5.500       4.500  \n",
-       "4     i      3     i  2.8  0.447   3.563       6.438  "
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cons_ann_3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "id": "political-laptop",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(cons_ann_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "id": "improved-relaxation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ca_cols = cons_ann_3.columns\n",
-    "for _,row in cons_ann_3.iterrows():\n",
-    "#     if ((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum() == 2:\n",
-    "#         print(row['Word 1'], row['Word 2'])\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2']))])\n",
-    "#     print (((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum())\n",
-    "    for col in ca_cols:\n",
-    "        if col in ['Word 1', 'Word 2']:\n",
-    "            continue\n",
-    "        \n",
-    "        wordSim353AnnotDF_New_Merged_DF.at[wordSim353AnnotDF_New_Merged_DF.index[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])], col] = row[col]\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])][col])\n",
-    "#     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "id": "relevant-dodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[~wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2']].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outside-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "pretty-scholar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "gorgeous-syndrome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'ID',\n",
-    " 'H_Sim',\n",
-    " 'H_Dim',\n",
-    " 'F_Sim',\n",
-    " 'F_Dim',\n",
-    " 'N_Sim',\n",
-    " 'N_Dim',\n",
-    " 'D_Sim',\n",
-    " 'D_Dim',\n",
-    " 'P_Sim',\n",
-    " 'P_Dim',\n",
-    " 'Avg',\n",
-    " 'Stdev',\n",
-    " 'H_orig',\n",
-    " 'H_reversed',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].to_csv('../data/wordsim353_all_embeddings_with_retrofits_scores_only_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ahead-syndication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "racial-behalf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.580594\n",
-       "std        0.457960\n",
-       "min        0.000000\n",
-       "25%        0.222619\n",
-       "50%        0.495179\n",
-       "75%        0.852326\n",
-       "max        5.000000\n",
-       "Name: transe_19k_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_old_cosSim_Diff'],ascending=False)['transe_19k_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.645644\n",
-       "std        0.526932\n",
-       "min        0.000000\n",
-       "25%        0.250461\n",
-       "50%        0.562808\n",
-       "75%        0.927608\n",
-       "max        5.000000\n",
-       "Name: transe_19k_1_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_1_new_cosSim_Diff'],ascending=False)['transe_19k_1_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.495179:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.562808:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'transe_19k_old_cosSim', 'transe_19k_1_new_cosSim', 'transe_19k_old_cosSim_Diff', 'transe_19k_1_new_cosSim_Diff', 'transe_19k_old_cosSim_Diff_Cat', 'transe_19k_1_new_cosSim_Diff_Cat']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff_Cat</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>0.523697</td>\n",
-       "      <td>0.122335</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>0.496332</td>\n",
-       "      <td>0.647819</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>0.688666</td>\n",
-       "      <td>0.473791</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>0.118237</td>\n",
-       "      <td>0.056250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>0.064958</td>\n",
-       "      <td>0.119978</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.807539</td>\n",
-       "      <td>3.161937</td>\n",
-       "      <td>0.192461</td>\n",
-       "      <td>0.161937</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.310261</td>\n",
-       "      <td>2.793832</td>\n",
-       "      <td>0.489739</td>\n",
-       "      <td>1.006168</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.449602</td>\n",
-       "      <td>2.739280</td>\n",
-       "      <td>0.550398</td>\n",
-       "      <td>0.260720</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.931610</td>\n",
-       "      <td>3.541754</td>\n",
-       "      <td>1.131610</td>\n",
-       "      <td>1.741754</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.648427</td>\n",
-       "      <td>2.758156</td>\n",
-       "      <td>1.101573</td>\n",
-       "      <td>0.991844</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  transe_19k_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60               3.076303   \n",
-       "1     Arafat         terror  3.60               3.103668   \n",
-       "2        FBI    fingerprint  3.60               2.911334   \n",
-       "3        FBI  investigation  3.00               2.881763   \n",
-       "4    Harvard           Yale  2.20               2.135042   \n",
-       "..       ...            ...   ...                    ...   \n",
-       "339      war         troops  3.00               2.807539   \n",
-       "340   weapon         secret  3.80               3.310261   \n",
-       "341  weather       forecast  3.00               2.449602   \n",
-       "342     wood         forest  1.80               2.931610   \n",
-       "343     word     similarity  3.75               2.648427   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim  transe_19k_old_cosSim_Diff  \\\n",
-       "0                   3.477665                    0.523697   \n",
-       "1                   2.952181                    0.496332   \n",
-       "2                   3.126209                    0.688666   \n",
-       "3                   2.943750                    0.118237   \n",
-       "4                   2.080022                    0.064958   \n",
-       "..                       ...                         ...   \n",
-       "339                 3.161937                    0.192461   \n",
-       "340                 2.793832                    0.489739   \n",
-       "341                 2.739280                    0.550398   \n",
-       "342                 3.541754                    1.131610   \n",
-       "343                 2.758156                    1.101573   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff  transe_19k_old_cosSim_Diff_Cat  \\\n",
-       "0                        0.122335                               1   \n",
-       "1                        0.647819                               1   \n",
-       "2                        0.473791                               1   \n",
-       "3                        0.056250                               0   \n",
-       "4                        0.119978                               0   \n",
-       "..                            ...                             ...   \n",
-       "339                      0.161937                               0   \n",
-       "340                      1.006168                               0   \n",
-       "341                      0.260720                               1   \n",
-       "342                      1.741754                               1   \n",
-       "343                      0.991844                               1   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff_Cat  \n",
-       "0                                   0  \n",
-       "1                                   1  \n",
-       "2                                   0  \n",
-       "3                                   0  \n",
-       "4                                   0  \n",
-       "..                                ...  \n",
-       "339                                 0  \n",
-       "340                                 1  \n",
-       "341                                 0  \n",
-       "342                                 1  \n",
-       "343                                 1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "compressed-emergency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['old_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "alert-holmes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['new_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "psychological-walter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['cat'] = wordSim353AnnotDF_New_Merged_DF['Avg'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badBefore.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badAfter.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consistent-difference",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "unknown-techno",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "false-status",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "varying-bones",
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-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
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-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "employed-clearing",
-   "metadata": {},
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
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-       "      <th>transe_19k_1_new_cosSim</th>\n",
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-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
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-       "      <th>count</th>\n",
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-       "      <td>344.000000</td>\n",
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-       "      <td>344.000000</td>\n",
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-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "creative-interview",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "noticed-andrew",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[col].apply(labelSamples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(labelSamples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
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-       "12350 -1.470588  8      \n",
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-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
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-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
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-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
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-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
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-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
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-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
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-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
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-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
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-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
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-       "1035  2.613445  4      \n",
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-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
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-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
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-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
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-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
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-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
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-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
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-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
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-     },
-     "execution_count": 259,
-     "metadata": {},
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-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>7</td>\n",
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-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
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-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
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-     "metadata": {},
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-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
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-   "execution_count": 261,
-   "id": "binary-committee",
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-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Archived/Master P279 Emb Generation.ipynb b/Archived/Master P279 Emb Generation.ipynb
deleted file mode 100644
index 4f1e932..0000000
--- a/Archived/Master P279 Emb Generation.ipynb	
+++ /dev/null
@@ -1,4516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "incorrect-routine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "import os\n",
-    "import h5py\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "previous-cutting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# DWD V2 files\n",
-    "SITELINKS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/sitelinks.en.tsv.gz\"\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "anonymous-psychology",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "everyday-colleague",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ABSTRACT Embedding Related Files\n",
-    "DBPEDIA_SHORT_ABSTRACTS_TTL = \"../data/evaluation/source_files/short-abstracts_lang=en.ttl\"\n",
-    "DBPEDIA_SHORT_ABSTRACTS_CSV = \"../data/evaluation/source_files/short-abstracts_lang=en.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "noble-draft",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Common Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "broadband-background",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "275927\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_all_nodes():\n",
-    "    \"\"\"\n",
-    "    This function generates the set of all nodes needed for execution\n",
-    "    \"\"\"\n",
-    "    p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "    wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "    concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "driven-yeast",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Complex + Transe Embeddings Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "brief-timer",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "238889\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "print(len(set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list())))\n",
-    "\n",
-    "# Load complex, transe embedding files and entity names file\n",
-    "compf = h5py.File('../data/complTrans/complEx.h5','r')\n",
-    "transf = h5py.File('../data/complTrans/transE.h5','r')\n",
-    "ent_names = json.load(open('../data/complTrans/entity_names_all_0.json'))\n",
-    "allNodes = get_all_nodes()\n",
-    "# json.dump(list(p279QnodesList), open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json', 'w'))\n",
-    "\n",
-    "complexEmb = {qnode: emb for emb, qnode in zip(compf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "transeEmb = {qnode: emb for emb, qnode in zip(transf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "print(f\"Out of {len(ent_names)} embeddings, retaining {len(transeEmb)} embeddings\")\n",
-    "\n",
-    "def serialize_embedding_dict(embed_dict):\n",
-    "    for key2 in embed_dict.keys():\n",
-    "        embed_dict[key2] = embed_dict[key2].tolist() if type(embed_dict[key2]) != list else embed_dict[key2]\n",
-    "    return embed_dict\n",
-    "\n",
-    "json.dump(serialize_embedding_dict(complexEmb),open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json','w'))\n",
-    "json.dump(serialize_embedding_dict(transeEmb),open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json','w'))\n",
-    "# complexEmb = json.load(open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json'))\n",
-    "# transeEmb = json.load(open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continued-locking",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Text Embedding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "neural-gibson",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "p279QnodesList = list(set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "instructional-weather",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "missingNodes = allNodes - set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "specified-clear",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37038"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingNodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "conditional-brooks",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Split main file into sub-files for groups of properties for multi-processing\n",
-    "\n",
-    "# bsize = len(p279QnodesList) // 250\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(p279QnodesList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(p279QnodesList[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "# #     print(len(q1))\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "utility-fleet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "for cnt in range(290,503):\n",
-    "    os.system(\"rm ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "alleged-strength",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Split main file into sub-files for groups of properties for multi-processing\n",
-    "# missingNodes = list(missingNodes)\n",
-    "# bsize = 1000\n",
-    "# cnt = 252\n",
-    "# for i in tqdm(range(0, len(missingNodes), bsize)):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(missingNodes[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "# #     print(len(q1))\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "signal-island",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def checkIfFileContainsLines(file):\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "fatal-broadway",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def countFileLines(file):\n",
-    "    count = 0\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            count += 1\n",
-    "    return count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "separate-satin",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "runCommCnt = 1\n",
-    "# 252\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 = \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(11*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "surprising-burning",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "for cnt in tqdm(range(1,290)):\n",
-    "    if countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") != 4097:\n",
-    "        print(cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bottom-lodge",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "# roberta-large-nli-mean-tokens\n",
-    "runCommCnt = 0\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 += \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(13*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "damaged-browse",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "collective-april",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-1.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "decent-yorkshire",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.74755263,1.6350263,-0.73952675,1.0463063,-0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>night shift, work shift during nighttime hours...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.25261465,0.06285462,0.029052094,0.50796187,0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>avenue, thoroughfare named \\\"avenue\\\" is thoro...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98970128</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node            property  \\\n",
-       "0  Q99738027      text_embedding   \n",
-       "1  Q99738027  embedding_sentence   \n",
-       "2  Q99228502      text_embedding   \n",
-       "3  Q99228502  embedding_sentence   \n",
-       "4  Q98970128      text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  0.74755263,1.6350263,-0.73952675,1.0463063,-0....  \n",
-       "1  night shift, work shift during nighttime hours...  \n",
-       "2  0.25261465,0.06285462,0.029052094,0.50796187,0...  \n",
-       "3  avenue, thoroughfare named \\\"avenue\\\" is thoro...  \n",
-       "4  0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# temp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfactory-speech",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89ab35a23d2b4280817c0341f653926b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/289 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text2EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text2EmbArr.append(temp)\n",
-    "text2Emb = pd.concat(text2EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "unavailable-competition",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.013346219,-0.0011667104,-0.011631126,-0.035...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.03083402,0.0063954173,-0.007306752,-0.04110...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98970128</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.002591213,0.020681797,-0.001562099,-0.05354...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q98964</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.04362039,0.01125773,0.022253651,-0.019940976...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q98958281</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.033505574,-0.019156927,-0.02115375,-0.016005...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99738027  text_embedding   \n",
-       "2  Q99228502  text_embedding   \n",
-       "4  Q98970128  text_embedding   \n",
-       "6     Q98964  text_embedding   \n",
-       "8  Q98958281  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  -0.013346219,-0.0011667104,-0.011631126,-0.035...  \n",
-       "2  -0.03083402,0.0063954173,-0.007306752,-0.04110...  \n",
-       "4  -0.002591213,0.020681797,-0.001562099,-0.05354...  \n",
-       "6  0.04362039,0.01125773,0.022253651,-0.019940976...  \n",
-       "8  0.033505574,-0.019156927,-0.02115375,-0.016005...  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text2Emb.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "concerned-april",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "507f79dc16a5412a88ebf38b99be0867",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/289 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text7EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text7EmbArr.append(temp)\n",
-    "text7Emb = pd.concat(text7EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "australian-enforcement",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "text2Emb = text2Emb[text2Emb.node.apply(lambda p: p in allNodes)]\n",
-    "text7Emb = text7Emb[text7Emb.node.apply(lambda p: p in allNodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "closed-treatment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have 2prop text embeddings for 278467 nodes and 7prop for 277587 nodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"We have 2prop text embeddings for {len(text2Emb)} nodes and 7prop for {len(text7Emb)} nodes\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "duplicate-agency",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "text2Emb['value'] = text2Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "text7Emb['value'] = text7Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "framed-third",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "text2EmbDict = {row['node']: row['value'] for _,row in text2Emb.iterrows()}\n",
-    "text7EmbDict = {row['node']: row['value'] for _,row in text7Emb.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "peaceful-andrew",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "json.dump(text2EmbDict, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump(text7EmbDict, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suited-going",
-   "metadata": {},
-   "source": [
-    "# Abstract Embeddings Generation\n",
-    "\n",
-    "Downloaded short abstracts file from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)\n",
-    "\n",
-    "Then, we extract the abstracts file from the bz2 file using: `bzip2 -d short-abstracts_lang=en.ttl.bz2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "unauthorized-russell",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0038713a1604ccb9c2e5499615fbc43",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cnt = 0\n",
-    "p1s = []\n",
-    "p11s = []\n",
-    "p2s = []\n",
-    "lines = []\n",
-    "with open(DBPEDIA_SHORT_ABSTRACTS_TTL, 'r', encoding='utf-8') as f:\n",
-    "    for line in tqdm(f):\n",
-    "        p1 = line[:line.find(\" \")]\n",
-    "        p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "        line = line[line.find(\" \")+1:]\n",
-    "        p2 = line[:line.find(\" \")]\n",
-    "        line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "        p1s.append(p1)\n",
-    "        p11s.append(p11)\n",
-    "        p2s.append(p2)\n",
-    "        lines.append(line)\n",
-    "df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "df1.to_csv(DBPEDIA_SHORT_ABSTRACTS_CSV)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "massive-camera",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(DBPEDIA_SHORT_ABSTRACTS_CSV, skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "df1.loc[df1[df1.index.duplicated()].index]\n",
-    "sitelinksDF = pd.read_csv(SITELINKS_FILE, sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in allNodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "suspected-backing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "traditional-composite",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 122585 Qnodes, there are 5661 sitelink Qnodes which do not have a short abstract i.e 116924 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "engaged-gather",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "appropriate-publicity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "currNodes = set(abstractsDF2.node1.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "frank-gallery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddb1cc12e883480aae823b4f29b5ed7e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/275927 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "correctedRows = []\n",
-    "for key in tqdm(allNodes):\n",
-    "    if key not in currNodes:\n",
-    "        correctedRows.append([None, None, key, None, None, None, None, None, None, labelsDict[key][1:-4] if key in labelsDict else None, descDict[key][1:-4] if key in descDict else None, None])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "developmental-extension",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF3 = pd.concat([abstractsDF2, pd.DataFrame(correctedRows, columns=abstractsDF2.columns)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "genetic-sympathy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] != '' and row['node1_desc'] != '' and not(pd.isna(row['node1_label'])) and not(pd.isna(row['node1_desc'])):\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']\n",
-    "    elif row['node1_label'] != '' and not(pd.isna(row['node1_label'])):\n",
-    "        return row['node1_label']\n",
-    "    else:\n",
-    "        return None\n",
-    "    \n",
-    "abstractsDF3['abstract'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF3['abstract_firstSent'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))\n",
-    "abstractsDF3 = abstractsDF3[~abstractsDF3.abstract.isna()]\n",
-    "abstractsDF3 = abstractsDF3.reset_index()\n",
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0']).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "native-correlation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "275437"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0'])\n",
-    "len(abstractsDF3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "professional-joining",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF3.to_csv('../data/Master_P279_dataset/abstracts.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aggressive-puppy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unnecessary-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "desirable-consistency",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wicked-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "desirable-fifteen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (1,2,4,5,7,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "275437"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2 = pd.read_csv('../data/Master_P279_dataset/abstracts.csv')\n",
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "involved-heather",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2[abstractsDF2.abstract == (abstractsDF2.node1_label +  ' ' + abstractsDF2.node1_desc)].to_csv('../data/Master_P279_dataset/temppppp.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "crude-result",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:1')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "impressive-comedy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "coordinate-assurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba4592dc618443878c658e2e9808509d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/276 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "426.3582227230072 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "496d8cadc8464f5db4934e1630b9b861",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/276 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "354.8075096607208 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "absEmbSeries = getSentEmbeddings(abstractsDF2.abstract, modelName)\n",
-    "absFirstSentEmbSeries = getSentEmbeddings(abstractsDF2.abstract_firstSent, modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "valued-toilet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "absEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absEmbSeries)}\n",
-    "absFirstSentEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absFirstSentEmbSeries)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "refined-belarus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(absEmbDict, open('../data/Master_P279_dataset/embeddings/abstract_orig_embedding_dict.json', 'w'))\n",
-    "json.dump(absFirstSentEmbDict, open('../data/Master_P279_dataset/embeddings/abstract_first_sent_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "knowing-relay",
-   "metadata": {},
-   "source": [
-    "# H,A,S embeddings\n",
-    "\n",
-    "Fetched from `sita` `/data02/profiling/kgtk/entity_profiling/output/wikidata-20210215-dwd/`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "restricted-resolution",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A_SOURCE_FILE = \"../source_dataset_files/A_walks_analysis/a_embeddings_10x10,min_count=0.kv\"\n",
-    "A_OP_FILE = \"../data/embeddings/has_a_orig_embedding_dict.json\"\n",
-    "\n",
-    "H_SOURCE_FILE = \"../data/Master_P279_dataset/embeddings/H_embeddings/h_embeddings_5x8,min_count=21.kv\"\n",
-    "H_OP_FILE = \"../data/Master_P279_dataset/embeddings/has_h_orig_embedding_dict.json\"\n",
-    "\n",
-    "S_SOURCE_FILE = \"../data/Master_P279_dataset/embeddings/S_embeddings/s_embeddings_5x10,min_count=0.kv\"\n",
-    "S_OP_FILE = \"../data/Master_P279_dataset/embeddings/has_s_orig_embedding_dict.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "ideal-accreditation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def store_relevant_embeddings(wvec, fname):\n",
-    "    tempEmb = {key: wvec[key] for key in wvec.index_to_key if key in allNodes}\n",
-    "    print(f\"Original Length: {len(wvec.index_to_key)}, No. of keys stored: {len(tempEmb)}\")\n",
-    "    json.dump(serializeEmbeddingDict(tempEmb),open(fname, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "wireless-playing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gensim.models import KeyedVectors, Word2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "cordless-bachelor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Length: 12106870, No. of keys stored: 33488\n"
-     ]
-    }
-   ],
-   "source": [
-    "a_key_vec = KeyedVectors.load(A_SOURCE_FILE)\n",
-    "store_relevant_embeddings(a_key_vec, A_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "light-discount",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Length: 19593942, No. of keys stored: 196100\n"
-     ]
-    }
-   ],
-   "source": [
-    "h_key_vec = KeyedVectors.load(H_SOURCE_FILE)\n",
-    "store_relevant_embeddings(h_key_vec, H_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "religious-accuracy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Length: 39030788, No. of keys stored: 140016\n"
-     ]
-    }
-   ],
-   "source": [
-    "s_key_vec = KeyedVectors.load(S_SOURCE_FILE)\n",
-    "store_relevant_embeddings(s_key_vec, S_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "standing-prevention",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "paperback-bacteria",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spatial-excerpt",
-   "metadata": {},
-   "source": [
-    "# Summary of embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "wooden-medicare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))\n",
-    "\n",
-    "complexEmb = json.load(open('../data/Master_P279_dataset/masterComplexEmb.json'))\n",
-    "transeEmb = json.load(open('../data/Master_P279_dataset/masterTranseEmb.json'))\n",
-    "\n",
-    "text2Emb = json.load(open('../data/Master_P279_dataset/text2Emb.json'))\n",
-    "text7Emb = json.load(open('../data/Master_P279_dataset/text7Emb.json'))\n",
-    "\n",
-    "abstractEmb = json.load(open('../data/Master_P279_dataset/abstractEmb.json'))\n",
-    "abstractFirstSentEmb = json.load(open('../data/Master_P279_dataset/abstractFirstSentEmb.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "distributed-magazine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump({key:val for key, val in complexEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in transeEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text2Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text7Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractFirstSentEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_first_sent_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "disturbed-better",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "advised-cyprus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summArr = []\n",
-    "cnt = countOverlap(complexEmb, p279QnodesList)\n",
-    "summArr.append(['complex', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(transeEmb, p279QnodesList)\n",
-    "summArr.append(['transe', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text2Emb, p279QnodesList)\n",
-    "summArr.append(['text2', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text7Emb, p279QnodesList)\n",
-    "summArr.append(['text7', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractEmb, p279QnodesList)\n",
-    "summArr.append(['abstract', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractFirstSentEmb, p279QnodesList)\n",
-    "summArr.append(['abstractFirstSent', cnt, cnt / len(p279QnodesList) * 100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "czech-keeping",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "238889"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279QnodesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "employed-christmas",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>text2</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>text7</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstractFirstSent</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           embedding   count  Coverage Percentage\n",
-       "0            complex  238448            99.815395\n",
-       "1             transe  238448            99.815395\n",
-       "2              text2  238889           100.000000\n",
-       "3              text7  238889           100.000000\n",
-       "4           abstract  105828            44.300072\n",
-       "5  abstractFirstSent  105828            44.300072"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(summArr, columns=['embedding', 'count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changing-strategy",
-   "metadata": {},
-   "source": [
-    "# Embeddings correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "purple-raising",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedDictMaster = {}\n",
-    "subsetEmbedDictMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "round-product",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedKeys = ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in masterEmbedKeys:\n",
-    "    masterEmbedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "metallic-insulin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subsetEmbedKeys = ['text_7props', 'text_2props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in subsetEmbedKeys:\n",
-    "    subsetEmbedDictMaster[key1] = json.load(open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "assigned-parameter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "olympic-yemen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_pairs = {(row['word1_kg_id'], row['word2_kg_id']) for _, row in wordSim353AnnotDF_New.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "welcome-disorder",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by text_7props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by text_2props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by complex embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by transe embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by abstract embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by abstract_first_sent embeddings created for 19k retrofitting: 343\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in subsetEmbedKeys:\n",
-    "    print(f\"Pair Coverage by {key1} embeddings created for 19k retrofitting: {sum([row[0] in subsetEmbedDictMaster[key1] and row[1] in subsetEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "northern-psychiatry",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by old text_7_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old text_2_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old complex embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old transe embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old abstract embeddings created for 19k retrofitting: 183\n",
-      "Pair Coverage by old abstract_first_sent embeddings created for 19k retrofitting: 183\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by old {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "contrary-casino",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_set = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "alleged-polish",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "masterEmbCorrections = defaultdict(list)\n",
-    "for node in wordSim353AnnotDF_New_set:\n",
-    "    for i in range(len(masterEmbedKeys)):\n",
-    "        if node not in masterEmbedDictMaster[masterEmbedKeys[i]] and node in wordSim353AnnotDF_New_set:\n",
-    "            masterEmbCorrections[masterEmbedKeys[i]].append(node)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "periodic-buffer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['abstract', 'abstract_first_sent', 'text_7_props', 'text_2_props', 'complex', 'transe'])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "masterEmbCorrections.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "awful-signal",
-   "metadata": {},
-   "source": [
-    "## Complex, Transe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "exceptional-acting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# correctedComplexEmb = {}\n",
-    "# correctedTranseEmb = {}\n",
-    "# for wordID in masterEmbCorrections['complex']:\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "#         correctedComplexEmb[wordID] = [float(p) for p in resp['graph_embedding_complex'].split(',')]\n",
-    "#         correctedTranseEmb[wordID] = [float(p) for p in resp['graph_embeddings_transe'].split(',')]\n",
-    "#     except:\n",
-    "#         print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "assigned-journey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51850ec9544547f293820bd9e94091f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4de00c52596f4ce3b5d17ee9ef73b068",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1h 33min 17s, sys: 2min 38s, total: 1h 35min 56s\n",
-      "Wall time: 1h 35min 28s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "correctedComplexEmb = {qnode: emb for emb, qnode in tqdm(zip(f['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}\n",
-    "correctedTranseEmb = {qnode: emb for emb, qnode in tqdm(zip(transf['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "female-scope",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(list(correctedComplexEmb.items())[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "valuable-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(masterEmbedDictMaster['complex'][list(masterEmbedDictMaster['complex'].keys())[0]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "religious-celebration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for node, emb in correctedComplexEmb.items():\n",
-    "    masterEmbedDictMaster['complex'][node] = emb\n",
-    "for node, emb in correctedTranseEmb.items():\n",
-    "    masterEmbedDictMaster['transe'][node] = emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spatial-writer",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "periodic-laundry",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "motivated-soundtrack",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imported-advance",
-   "metadata": {},
-   "source": [
-    "## Text Embeddings Correction file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "historical-conservative",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(masterEmbCorrections['text_7_props']) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv -v True\"\n",
-    "os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "overhead-commission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "cooked-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "static-failure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv', sep='\\t')\n",
-    "corrected2Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "spare-flexibility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = corrected7Emb[corrected7Emb.property == 'text_embedding']\n",
-    "corrected7Emb['value'] = corrected7Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "\n",
-    "corrected2Emb = corrected2Emb[corrected2Emb.property == 'text_embedding']\n",
-    "corrected2Emb['value'] = corrected2Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "minute-oakland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in corrected7Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_7_props'][row['node']] = row['value']\n",
-    "for _, row in corrected2Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_2_props'][row['node']] = row['value']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documentary-fluid",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "analyzed-naples",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "psychological-brighton",
-   "metadata": {},
-   "source": [
-    "## Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "meaning-spanking",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "masterEmbCorrections_abs_set = set(masterEmbCorrections['abstract'])\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "offensive-enclosure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "better-tuner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 58 Qnodes, there are 16 sitelink Qnodes which do not have a short abstract i.e 42 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "# abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "invalid-fiction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] == \"\" and row['node1_desc'] == \"\":\n",
-    "        return None\n",
-    "    else:\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "opened-drink",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)\n",
-    "abstractsDF2 = abstractsDF2.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "affected-reproduction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['abstract'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "actual-communication",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "quantitative-tumor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "turned-retail",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "heard-freedom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2.drop(columns=['index']).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "forty-southeast",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>level_0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Luxuries</td>\n",
-       "      <td>Q10953913-wikipedia_sitelink-538fe3-0</td>\n",
-       "      <td>Q10953913</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Luxuries</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>luxuryBehavior, expenses or equipment that far...</td>\n",
-       "      <td>luxury</td>\n",
-       "      <td>Behavior, expenses or equipment that far excee...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Potato</td>\n",
-       "      <td>Q10998-wikipedia_sitelink-56b85c-0</td>\n",
-       "      <td>Q10998</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Potato</td>\n",
-       "      <td>10709.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Potato&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "      <td>potato</td>\n",
-       "      <td>species of plant</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>Q111-wikipedia_sitelink-9ff296-0</td>\n",
-       "      <td>Q111</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Mars</td>\n",
-       "      <td>1803088.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Mars&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>fourth planet from the Sun</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Dawn</td>\n",
-       "      <td>Q11326182-wikipedia_sitelink-ae2918-0</td>\n",
-       "      <td>Q11326182</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dawn</td>\n",
-       "      <td>97544.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Dawn&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "      <td>dawn</td>\n",
-       "      <td>time that marks the beginning of the twilight ...</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Change_(philosophy)</td>\n",
-       "      <td>Q1150070-wikipedia_sitelink-81cf5f-0</td>\n",
-       "      <td>Q1150070</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Change_(philosophy)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>changeprocess, event or action that deviates f...</td>\n",
-       "      <td>change</td>\n",
-       "      <td>process, event or action that deviates from th...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level_0  index         trimmedNode2                                     id  \\\n",
-       "0        0      0             Luxuries  Q10953913-wikipedia_sitelink-538fe3-0   \n",
-       "1        1      1               Potato     Q10998-wikipedia_sitelink-56b85c-0   \n",
-       "2        2      2                 Mars       Q111-wikipedia_sitelink-9ff296-0   \n",
-       "3        3      3                 Dawn  Q11326182-wikipedia_sitelink-ae2918-0   \n",
-       "4        4      4  Change_(philosophy)   Q1150070-wikipedia_sitelink-81cf5f-0   \n",
-       "\n",
-       "       node1               label  \\\n",
-       "0  Q10953913  wikipedia_sitelink   \n",
-       "1     Q10998  wikipedia_sitelink   \n",
-       "2       Q111  wikipedia_sitelink   \n",
-       "3  Q11326182  wikipedia_sitelink   \n",
-       "4   Q1150070  wikipedia_sitelink   \n",
-       "\n",
-       "                                              node2     ignore  \\\n",
-       "0             http://en.wikipedia.org/wiki/Luxuries        NaN   \n",
-       "1               http://en.wikipedia.org/wiki/Potato    10709.0   \n",
-       "2                 http://en.wikipedia.org/wiki/Mars  1803088.0   \n",
-       "3                 http://en.wikipedia.org/wiki/Dawn    97544.0   \n",
-       "4  http://en.wikipedia.org/wiki/Change_(philosophy)        NaN   \n",
-       "\n",
-       "                                    url  \\\n",
-       "0                                   NaN   \n",
-       "1  <http://dbpedia.org/resource/Potato>   \n",
-       "2    <http://dbpedia.org/resource/Mars>   \n",
-       "3    <http://dbpedia.org/resource/Dawn>   \n",
-       "4                                   NaN   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "                                            abstract node1_label  \\\n",
-       "0  luxuryBehavior, expenses or equipment that far...      luxury   \n",
-       "1  The potato is a root vegetable native to the A...      potato   \n",
-       "2  Mars is the fourth planet from the Sun and the...        Mars   \n",
-       "3  Dawn is the time that marks the beginning of t...        dawn   \n",
-       "4  changeprocess, event or action that deviates f...      change   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0  Behavior, expenses or equipment that far excee...   \n",
-       "1                                   species of plant   \n",
-       "2                         fourth planet from the Sun   \n",
-       "3  time that marks the beginning of the twilight ...   \n",
-       "4  process, event or action that deviates from th...   \n",
-       "\n",
-       "                                  abstract_firstSent  \n",
-       "0                                                nan  \n",
-       "1  The potato is a root vegetable native to the A...  \n",
-       "2  Mars is the fourth planet from the Sun and the...  \n",
-       "3  Dawn is the time that marks the beginning of t...  \n",
-       "4                                                nan  "
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "multiple-offer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getIndSentEmbeddings(sent, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode([sent])\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "sustainable-breakdown",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.6419482231140137 s\n",
-      "0.5260367393493652 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "abstractsDF2['abs_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract, 'bert-base-nli-mean-tokens')))\n",
-    "abstractsDF2['abs_firstSent_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "usual-selling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in abstractsDF2.iterrows():\n",
-    "    masterEmbedDictMaster['abstract'][row['node1']] = row['abs_emb']\n",
-    "    masterEmbedDictMaster['abstract_first_sent'][row['node1']] = row['abs_firstSent_emb']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "promising-owner",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.37706875801086426 s\n",
-      "0.3001420497894287 s\n",
-      "0.370746374130249 s\n",
-      "0.6896324157714844 s\n",
-      "0.33779358863830566 s\n",
-      "0.3965473175048828 s\n",
-      "0.3200962543487549 s\n",
-      "0.3489806652069092 s\n",
-      "0.3413431644439697 s\n",
-      "0.32114505767822266 s\n",
-      "0.3811838626861572 s\n",
-      "0.34630370140075684 s\n",
-      "0.37790727615356445 s\n",
-      "0.26860570907592773 s\n",
-      "0.3601953983306885 s\n",
-      "0.3713240623474121 s\n",
-      "0.34137582778930664 s\n",
-      "0.33736181259155273 s\n",
-      "0.37023448944091797 s\n",
-      "0.31382036209106445 s\n",
-      "0.35136938095092773 s\n",
-      "0.37309718132019043 s\n",
-      "0.33543896675109863 s\n",
-      "0.38199710845947266 s\n",
-      "0.3740067481994629 s\n",
-      "0.3278031349182129 s\n",
-      "0.32283997535705566 s\n",
-      "0.34000563621520996 s\n",
-      "0.31502628326416016 s\n",
-      "0.34996771812438965 s\n",
-      "0.3871273994445801 s\n",
-      "0.3487060070037842 s\n",
-      "0.35172486305236816 s\n",
-      "0.3280646800994873 s\n",
-      "0.3519773483276367 s\n",
-      "0.3354451656341553 s\n",
-      "0.3633551597595215 s\n",
-      "0.3226644992828369 s\n",
-      "0.33882975578308105 s\n",
-      "0.36072254180908203 s\n",
-      "0.3833494186401367 s\n",
-      "0.2929043769836426 s\n",
-      "0.32875680923461914 s\n",
-      "0.36334872245788574 s\n",
-      "0.34148168563842773 s\n",
-      "0.3569769859313965 s\n",
-      "0.37468576431274414 s\n",
-      "0.399524450302124 s\n",
-      "0.3516504764556885 s\n",
-      "0.333402156829834 s\n",
-      "0.3851203918457031 s\n",
-      "0.34867238998413086 s\n",
-      "0.3607771396636963 s\n",
-      "0.38669753074645996 s\n",
-      "0.33347272872924805 s\n",
-      "0.36278390884399414 s\n",
-      "0.3602781295776367 s\n",
-      "0.3322322368621826 s\n",
-      "0.36807823181152344 s\n",
-      "0.3407411575317383 s\n",
-      "0.3837134838104248 s\n",
-      "0.38958096504211426 s\n",
-      "0.3332521915435791 s\n",
-      "0.3331124782562256 s\n",
-      "0.35001134872436523 s\n",
-      "0.32433485984802246 s\n",
-      "0.36315059661865234 s\n",
-      "0.34323906898498535 s\n",
-      "0.3112339973449707 s\n",
-      "0.30588483810424805 s\n",
-      "0.30704236030578613 s\n",
-      "0.31201720237731934 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "acquired-manitoba",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.32213783264160156 s\n",
-      "0.357776403427124 s\n",
-      "0.37949395179748535 s\n",
-      "0.35210466384887695 s\n",
-      "0.28103041648864746 s\n",
-      "0.3626406192779541 s\n",
-      "0.35109710693359375 s\n",
-      "0.34203338623046875 s\n",
-      "0.32386112213134766 s\n",
-      "0.3354361057281494 s\n",
-      "0.3063056468963623 s\n",
-      "0.3441202640533447 s\n",
-      "0.32869935035705566 s\n",
-      "0.42442989349365234 s\n",
-      "0.37239527702331543 s\n",
-      "0.38650059700012207 s\n",
-      "0.3191685676574707 s\n",
-      "0.3609733581542969 s\n",
-      "0.3115823268890381 s\n",
-      "0.36015963554382324 s\n",
-      "0.3338603973388672 s\n",
-      "0.3487727642059326 s\n",
-      "0.3250617980957031 s\n",
-      "0.35145044326782227 s\n",
-      "0.33944034576416016 s\n",
-      "0.31502413749694824 s\n",
-      "0.3611795902252197 s\n",
-      "0.35285043716430664 s\n",
-      "0.3575010299682617 s\n",
-      "0.304781436920166 s\n",
-      "0.4003562927246094 s\n",
-      "0.3315858840942383 s\n",
-      "0.36008763313293457 s\n",
-      "0.36187100410461426 s\n",
-      "0.32981252670288086 s\n",
-      "0.3378865718841553 s\n",
-      "0.31662964820861816 s\n",
-      "0.32143092155456543 s\n",
-      "0.3152732849121094 s\n",
-      "0.38222813606262207 s\n",
-      "0.3846759796142578 s\n",
-      "0.33153700828552246 s\n",
-      "0.37013936042785645 s\n",
-      "0.33272790908813477 s\n",
-      "0.29526567459106445 s\n",
-      "0.3218040466308594 s\n",
-      "0.3795340061187744 s\n",
-      "0.3576061725616455 s\n",
-      "0.35764193534851074 s\n",
-      "0.36867713928222656 s\n",
-      "0.3807237148284912 s\n",
-      "0.33266758918762207 s\n",
-      "0.33878159523010254 s\n",
-      "0.38289546966552734 s\n",
-      "0.38695788383483887 s\n",
-      "0.33074188232421875 s\n",
-      "0.32749414443969727 s\n",
-      "0.33860039710998535 s\n",
-      "0.36585235595703125 s\n",
-      "0.33011841773986816 s\n",
-      "0.3293156623840332 s\n",
-      "0.3491702079772949 s\n",
-      "0.3720529079437256 s\n",
-      "0.3078622817993164 s\n",
-      "0.3844125270843506 s\n",
-      "0.32468104362487793 s\n",
-      "0.3186354637145996 s\n",
-      "0.3438723087310791 s\n",
-      "0.36643028259277344 s\n",
-      "0.34279680252075195 s\n",
-      "0.3625810146331787 s\n",
-      "0.35865354537963867 s\n",
-      "0.3503103256225586 s\n",
-      "0.37160682678222656 s\n",
-      "0.3268110752105713 s\n",
-      "0.2564544677734375 s\n",
-      "0.37343525886535645 s\n",
-      "0.33298277854919434 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract_first_sent']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "veterinary-thailand",
-   "metadata": {},
-   "source": [
-    "## Updated coverage details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "intimate-campus",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by new text_7_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new text_2_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new complex embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new transe embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new abstract embeddings created for 19k retrofitting: 339\n",
-      "Pair Coverage by new abstract_first_sent embeddings created for 19k retrofitting: 339\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by new {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "lovely-token",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in masterEmbedDictMaster.keys():\n",
-    "    for key2 in masterEmbedDictMaster[key1].keys():\n",
-    "        if type(masterEmbedDictMaster[key1][key2]) != list:\n",
-    "            masterEmbedDictMaster[key1][key2] = masterEmbedDictMaster[key1][key2].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "exact-surfing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in ['complex', 'transe']:\n",
-    "    json.dump(masterEmbedDictMaster[key1], open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "behavioral-spain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt\n",
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "hawaiian-brain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>total count</th>\n",
-       "      <th>overlap count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             embedding  total count  overlap count  Coverage Percentage\n",
-       "0         text_7_props       238930         238889           100.000000\n",
-       "1         text_2_props       238930         238889           100.000000\n",
-       "2              complex       238500         238448            99.815395\n",
-       "3               transe       238500         238448            99.815395\n",
-       "4             abstract       105964         105916            44.336910\n",
-       "5  abstract_first_sent       105964         105916            44.336910"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summArr = []\n",
-    "for key1 in masterEmbedDictMaster:\n",
-    "    cnt = countOverlap(masterEmbedDictMaster[key1], p279QnodesList)\n",
-    "    summArr.append([key1, len(masterEmbedDictMaster[key1]), cnt, cnt / len(p279QnodesList) * 100])\n",
-    "pd.DataFrame(summArr, columns=['embedding', 'total count', 'overlap count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-namibia",
-   "metadata": {},
-   "source": [
-    "# Concatenated Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fifth-associate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "embedDictMaster = {}\n",
-    "for key1 in ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']:\n",
-    "    embedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json'))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "egyptian-sentence",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props :  1024\n",
-      "text_2_props :  1024\n",
-      "complex :  200\n",
-      "transe :  200\n",
-      "abstract :  768\n",
-      "abstract_first_sent :  768\n"
-     ]
-    }
-   ],
-   "source": [
-    "def determineEmbeddingLengths(embedDictMaster):\n",
-    "    for key in embedDictMaster.keys():\n",
-    "        embed_size = len(next(iter(embedDictMaster[key].values())))\n",
-    "        print(key,\": \",embed_size)\n",
-    "determineEmbeddingLengths(embedDictMaster)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "perfect-extension",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fillCoverage(embedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    wordSim353AnnotDF_set = set(wordSim353AnnotDF_New['word1_kg_id'].to_list() + wordSim353AnnotDF_New['word2_kg_id'].to_list())\n",
-    "    embed_size = len(embedDict[next(iter(embedDict))])\n",
-    "#     print(embed_size)\n",
-    "    count = 0\n",
-    "    for word in wordSim353AnnotDF_set:\n",
-    "        if word not in embedDict:\n",
-    "            embedDict[word] = np.zeros((embed_size))\n",
-    "            count += 1\n",
-    "    print(f\"Added {count} corrections\")\n",
-    "    return embedDict\n",
-    "\n",
-    "def deserializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = np.array(embedDict[key2])\n",
-    "    return embedDict\n",
-    "\n",
-    "def serializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = embedDict[key2].tolist() if type(embedDict[key2]) != list else embedDict[key2]\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "removable-point",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Added 11 corrections\n",
-      "Added 11 corrections\n",
-      "Added 0 corrections\n",
-      "Added 0 corrections\n",
-      "Added 4 corrections\n",
-      "Added 4 corrections\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = deserializeEmbeddingDict(embedDictMaster[key1])\n",
-    "# Fill Coverage of embedding dictionaries\n",
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = fillCoverage(embedDictMaster[key1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "productive-indiana",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props 238941\n",
-      "text_2_props 238941\n",
-      "complex 238941\n",
-      "transe 238941\n",
-      "abstract 238941\n",
-      "abstract_first_sent 238941\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    print(key1, len(next(iter(embedDictMaster.values()))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mechanical-retro",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ready-financing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prime-hometown",
-   "metadata": {},
-   "source": [
-    "# Retrofitting sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "tight-civilization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "exciting-circle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase, weightAssignment=False):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            if weightAssignment:\n",
-    "                sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            else:\n",
-    "                sumOfSims = sum([1 for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "                \n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "hollywood-prisoner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    responseDict = {}\n",
-    "    responseDict['wordSimMissingSet'] = wordSimMissingSet\n",
-    "    responseDict['coveredPairs'] = len(wordSim353AnnotDF_New2)\n",
-    "    responseDict['totalPairs'] = len(wordSim353AnnotDF_New)\n",
-    "    \n",
-    "#     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    \n",
-    "    # Logic 1: Scale min,max value to 1,4 strictly\n",
-    "#     min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "#     min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "#     wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    \n",
-    "    # Logic 2: Scale abs value to 1,4 strictly\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * abs(p))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "    \n",
-    "#     print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "#     print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "#     print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    responseDict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['old_acc'] = accuracy_score(wordSim353AnnotDF_New['textOld'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    responseDict['new_acc'] = accuracy_score(wordSim353AnnotDF_New['textNew'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    \n",
-    "    responseDict['class_rep_old'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), output_dict=True)\n",
-    "    responseDict['class_rep_new'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), output_dict=True)\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    responseDict['cm_old'] = cm_old\n",
-    "    responseDict['cm_new'] = cm_new\n",
-    "    \n",
-    "    return responseDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "severe-explosion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDictMaster, embedDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "decreased-syndication",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "39218\n"
-     ]
-    }
-   ],
-   "source": [
-    "neighDictMaster['19k_childPar'] = fetchNeighbours(p279ChildPar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "rocky-criterion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['complex'] = complexEmb\n",
-    "embedDictMaster['transe'] = transeEmb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "apparent-sapphire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for key2 in embedDictMaster[key1].keys():\n",
-    "        embedDictMaster[key1][key2] = np.array(embedDictMaster[key1][key2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "precise-oxygen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embList = list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "identical-keyboard",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basisList = list(neighDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "aging-flavor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['19k_childPar'])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neighDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "amended-remove",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster, responsesDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "surgical-insurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7817a00dcf3c412b92a7c5ac75517168",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "results = []\n",
-    "NUM_ITERS = 10\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            groupResults = []\n",
-    "            for weightCase in [1,2]:\n",
-    "                embedDict = embedDictMaster[emb]\n",
-    "                if weightedNess:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                else:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                for iterNum in range(1,NUM_ITERS+1):\n",
-    "                    newEmbedDict = retrofit(embedDict, neighDictMaster[basis], weightCase, weightedNess)\n",
-    "        #             dists = determineDistances(embedDict, newEmbedDict)\n",
-    "                    responsesDict = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        #                 print(responsesDict.keys())\n",
-    "                    groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                    responsesDict['old_acc']*100, \\\n",
-    "                                    responsesDict['new_acc']*100, \\\n",
-    "                                    (responsesDict['new_acc'] - responsesDict['old_acc'])*100, \\\n",
-    "                                    responsesDict['coveredPairs'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                    ])\n",
-    "                    embedDict = newEmbedDict\n",
-    "\n",
-    "                newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                responsesDictMaster[caseName] = responsesDict\n",
-    "            for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "                results.append(gR+[rank])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "assigned-stations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'Rank'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "built-mumbai",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Embedding</th>\n",
-       "      <th>Basis</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Weightedness</th>\n",
-       "      <th>Iteration Num</th>\n",
-       "      <th>Old Acc</th>\n",
-       "      <th>New Acc</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>Pairs Covered</th>\n",
-       "      <th>Old I Precision</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Old U Precision</th>\n",
-       "      <th>Old U Recall</th>\n",
-       "      <th>Old U F1-Score</th>\n",
-       "      <th>New I Precision</th>\n",
-       "      <th>New I Recall</th>\n",
-       "      <th>New I F1-Score</th>\n",
-       "      <th>New U Precision</th>\n",
-       "      <th>New U Recall</th>\n",
-       "      <th>New U F1-Score</th>\n",
-       "      <th>Rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>3.488372</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>3.197674</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>2.906977</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>1.744186</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.491803</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.535714</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.116667</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.517241</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.545455</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-1.453488</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>0.377778</td>\n",
-       "      <td>0.165049</td>\n",
-       "      <td>0.229730</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-2.034884</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>40 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Embedding         Basis  Weight  Weightedness  Iteration Num    Old Acc  \\\n",
-       "0    complex  19k_childPar       1          True              1  60.755814   \n",
-       "20    transe  19k_childPar       1          True              1  62.500000   \n",
-       "1    complex  19k_childPar       1          True              2  64.244186   \n",
-       "11   complex  19k_childPar       2          True              2  61.918605   \n",
-       "10   complex  19k_childPar       2          True              1  60.755814   \n",
-       "2    complex  19k_childPar       1          True              3  67.151163   \n",
-       "4    complex  19k_childPar       1          True              5  67.151163   \n",
-       "36    transe  19k_childPar       2          True              7  62.500000   \n",
-       "30    transe  19k_childPar       2          True              1  62.500000   \n",
-       "22    transe  19k_childPar       1          True              3  64.825581   \n",
-       "38    transe  19k_childPar       2          True              9  63.081395   \n",
-       "33    transe  19k_childPar       2          True              4  62.790698   \n",
-       "18   complex  19k_childPar       2          True              9  63.081395   \n",
-       "37    transe  19k_childPar       2          True              8  63.081395   \n",
-       "32    transe  19k_childPar       2          True              3  62.790698   \n",
-       "23    transe  19k_childPar       1          True              4  65.406977   \n",
-       "39    transe  19k_childPar       2          True             10  63.372093   \n",
-       "15   complex  19k_childPar       2          True              6  63.662791   \n",
-       "14   complex  19k_childPar       2          True              5  63.662791   \n",
-       "13   complex  19k_childPar       2          True              4  63.662791   \n",
-       "12   complex  19k_childPar       2          True              3  63.662791   \n",
-       "17   complex  19k_childPar       2          True              8  63.372093   \n",
-       "16   complex  19k_childPar       2          True              7  63.662791   \n",
-       "35    transe  19k_childPar       2          True              6  62.790698   \n",
-       "34    transe  19k_childPar       2          True              5  63.081395   \n",
-       "31    transe  19k_childPar       2          True              2  63.081395   \n",
-       "28    transe  19k_childPar       1          True              9  60.755814   \n",
-       "19   complex  19k_childPar       2          True             10  63.081395   \n",
-       "26    transe  19k_childPar       1          True              7  62.209302   \n",
-       "29    transe  19k_childPar       1          True             10  60.465116   \n",
-       "8    complex  19k_childPar       1          True              9  65.116279   \n",
-       "3    complex  19k_childPar       1          True              4  67.732558   \n",
-       "6    complex  19k_childPar       1          True              7  66.569767   \n",
-       "7    complex  19k_childPar       1          True              8  65.988372   \n",
-       "21    transe  19k_childPar       1          True              2  65.697674   \n",
-       "27    transe  19k_childPar       1          True              8  61.627907   \n",
-       "25    transe  19k_childPar       1          True              6  63.372093   \n",
-       "5    complex  19k_childPar       1          True              6  67.732558   \n",
-       "9    complex  19k_childPar       1          True             10  64.534884   \n",
-       "24    transe  19k_childPar       1          True              5  65.406977   \n",
-       "\n",
-       "      New Acc  Increase  Pairs Covered  Old I Precision  ...  Old U Precision  \\\n",
-       "0   64.244186  3.488372            291         1.000000  ...         0.433121   \n",
-       "20  65.697674  3.197674            291         0.888889  ...         0.397059   \n",
-       "1   67.151163  2.906977            291         1.000000  ...         0.463415   \n",
-       "11  63.662791  1.744186            291         1.000000  ...         0.444444   \n",
-       "10  61.918605  1.162791            291         1.000000  ...         0.433121   \n",
-       "2   67.732558  0.581395            291         1.000000  ...         0.495050   \n",
-       "4   67.732558  0.581395            291         0.916667  ...         0.492958   \n",
-       "36  63.081395  0.581395            291         0.846154  ...         0.351852   \n",
-       "30  63.081395  0.581395            291         0.888889  ...         0.397059   \n",
-       "22  65.406977  0.581395            291         0.750000  ...         0.393939   \n",
-       "38  63.372093  0.290698            291         0.785714  ...         0.372549   \n",
-       "33  63.081395  0.290698            291         0.900000  ...         0.383333   \n",
-       "18  63.081395  0.000000            291         0.909091  ...         0.444444   \n",
-       "37  63.081395  0.000000            291         0.846154  ...         0.365385   \n",
-       "32  62.790698  0.000000            291         0.900000  ...         0.387097   \n",
-       "23  65.406977  0.000000            291         0.750000  ...         0.400000   \n",
-       "39  63.372093  0.000000            291         0.785714  ...         0.380000   \n",
-       "15  63.662791  0.000000            291         1.000000  ...         0.455224   \n",
-       "14  63.662791  0.000000            291         1.000000  ...         0.457143   \n",
-       "13  63.662791  0.000000            291         1.000000  ...         0.457746   \n",
-       "12  63.662791  0.000000            291         1.000000  ...         0.458904   \n",
-       "17  63.081395 -0.290698            291         0.909091  ...         0.448819   \n",
-       "16  63.372093 -0.290698            291         1.000000  ...         0.453846   \n",
-       "35  62.500000 -0.290698            291         0.846154  ...         0.363636   \n",
-       "34  62.790698 -0.290698            291         0.846154  ...         0.375000   \n",
-       "31  62.790698 -0.290698            291         0.900000  ...         0.400000   \n",
-       "28  60.465116 -0.290698            291         0.394737  ...         0.368421   \n",
-       "19  62.790698 -0.290698            291         0.909091  ...         0.444444   \n",
-       "26  61.627907 -0.581395            291         0.428571  ...         0.428571   \n",
-       "29  59.883721 -0.581395            291         0.365854  ...         0.388889   \n",
-       "8   64.534884 -0.581395            291         0.785714  ...         0.442308   \n",
-       "3   67.151163 -0.581395            291         0.909091  ...         0.511905   \n",
-       "6   65.988372 -0.581395            291         0.916667  ...         0.475410   \n",
-       "7   65.116279 -0.872093            291         0.846154  ...         0.464286   \n",
-       "21  64.825581 -0.872093            291         0.833333  ...         0.450000   \n",
-       "27  60.755814 -0.872093            291         0.416667  ...         0.400000   \n",
-       "25  62.209302 -1.162791            291         0.468750  ...         0.454545   \n",
-       "5   66.569767 -1.162791            291         0.916667  ...         0.507937   \n",
-       "9   63.081395 -1.453488            291         0.785714  ...         0.416667   \n",
-       "24  63.372093 -2.034884            291         0.652174  ...         0.444444   \n",
-       "\n",
-       "    Old U Recall  Old U F1-Score  New I Precision  New I Recall  \\\n",
-       "0       0.660194        0.523077         1.000000          0.40   \n",
-       "20      0.262136        0.315789         0.833333          0.50   \n",
-       "1       0.553398        0.504425         1.000000          0.50   \n",
-       "11      0.660194        0.531250         1.000000          0.45   \n",
-       "10      0.660194        0.523077         1.000000          0.40   \n",
-       "2       0.485437        0.490196         0.909091          0.50   \n",
-       "4       0.339806        0.402299         0.916667          0.55   \n",
-       "36      0.184466        0.242038         0.846154          0.55   \n",
-       "30      0.262136        0.315789         0.900000          0.45   \n",
-       "22      0.126214        0.191176         0.750000          0.75   \n",
-       "38      0.184466        0.246753         0.785714          0.55   \n",
-       "33      0.223301        0.282209         0.846154          0.55   \n",
-       "18      0.543689        0.489083         0.909091          0.50   \n",
-       "37      0.184466        0.245161         0.785714          0.55   \n",
-       "32      0.233010        0.290909         0.900000          0.45   \n",
-       "23      0.116505        0.180451         0.652174          0.75   \n",
-       "39      0.184466        0.248366         0.785714          0.55   \n",
-       "15      0.592233        0.514768         1.000000          0.45   \n",
-       "14      0.621359        0.526749         1.000000          0.45   \n",
-       "13      0.631068        0.530612         1.000000          0.45   \n",
-       "12      0.650485        0.538153         1.000000          0.45   \n",
-       "17      0.553398        0.495652         0.909091          0.50   \n",
-       "16      0.572816        0.506438         0.909091          0.50   \n",
-       "35      0.194175        0.253165         0.846154          0.55   \n",
-       "34      0.203883        0.264151         0.846154          0.55   \n",
-       "31      0.252427        0.309524         0.900000          0.45   \n",
-       "28      0.067961        0.114754         0.365854          0.75   \n",
-       "19      0.543689        0.489083         0.900000          0.45   \n",
-       "26      0.087379        0.145161         0.416667          0.75   \n",
-       "29      0.067961        0.115702         0.333333          0.75   \n",
-       "8       0.223301        0.296774         0.785714          0.55   \n",
-       "3       0.417476        0.459893         0.916667          0.55   \n",
-       "6       0.281553        0.353659         0.846154          0.55   \n",
-       "7       0.252427        0.327044         0.785714          0.55   \n",
-       "21      0.174757        0.251748         0.750000          0.60   \n",
-       "27      0.077670        0.130081         0.394737          0.75   \n",
-       "25      0.097087        0.160000         0.428571          0.75   \n",
-       "5       0.310680        0.385542         0.916667          0.55   \n",
-       "9       0.194175        0.264901         0.687500          0.55   \n",
-       "24      0.116505        0.184615         0.468750          0.75   \n",
-       "\n",
-       "    New I F1-Score  New U Precision  New U Recall  New U F1-Score  Rank  \n",
-       "0         0.571429         0.463415      0.553398        0.504425     2  \n",
-       "20        0.625000         0.450000      0.174757        0.251748     0  \n",
-       "1         0.666667         0.495050      0.485437        0.490196     4  \n",
-       "11        0.620690         0.458904      0.650485        0.538153    13  \n",
-       "10        0.571429         0.444444      0.660194        0.531250    12  \n",
-       "2         0.645161         0.511905      0.417476        0.459893     1  \n",
-       "4         0.687500         0.507937      0.310680        0.385542     5  \n",
-       "36        0.666667         0.365385      0.184466        0.245161     6  \n",
-       "30        0.600000         0.400000      0.252427        0.309524    17  \n",
-       "22        0.750000         0.400000      0.116505        0.180451     3  \n",
-       "38        0.647059         0.380000      0.184466        0.248366     8  \n",
-       "33        0.666667         0.375000      0.203883        0.264151    14  \n",
-       "18        0.645161         0.444444      0.543689        0.489083    19  \n",
-       "37        0.647059         0.372549      0.184466        0.246753     7  \n",
-       "32        0.600000         0.383333      0.223301        0.282209    12  \n",
-       "23        0.697674         0.444444      0.116505        0.184615     1  \n",
-       "39        0.647059         0.380000      0.184466        0.248366     9  \n",
-       "15        0.620690         0.453846      0.572816        0.506438    17  \n",
-       "14        0.620690         0.455224      0.592233        0.514768    16  \n",
-       "13        0.620690         0.457143      0.621359        0.526749    15  \n",
-       "12        0.620690         0.457746      0.631068        0.530612    14  \n",
-       "17        0.645161         0.444444      0.543689        0.489083    18  \n",
-       "16        0.645161         0.448819      0.553398        0.495652     9  \n",
-       "35        0.666667         0.351852      0.184466        0.242038     5  \n",
-       "34        0.666667         0.363636      0.194175        0.253165    15  \n",
-       "31        0.600000         0.387097      0.233010        0.290909    11  \n",
-       "28        0.491803         0.388889      0.067961        0.115702    13  \n",
-       "19        0.600000         0.444444      0.543689        0.489083    10  \n",
-       "26        0.535714         0.400000      0.077670        0.130081     4  \n",
-       "29        0.461538         0.411765      0.067961        0.116667    16  \n",
-       "8         0.647059         0.416667      0.194175        0.264901     0  \n",
-       "3         0.687500         0.492958      0.339806        0.402299     3  \n",
-       "6         0.666667         0.464286      0.252427        0.327044     7  \n",
-       "7         0.647059         0.442308      0.223301        0.296774     8  \n",
-       "21        0.666667         0.393939      0.126214        0.191176     2  \n",
-       "27        0.517241         0.368421      0.067961        0.114754    10  \n",
-       "25        0.545455         0.428571      0.087379        0.145161    19  \n",
-       "5         0.687500         0.475410      0.281553        0.353659     6  \n",
-       "9         0.611111         0.377778      0.165049        0.229730    11  \n",
-       "24        0.576923         0.454545      0.097087        0.160000    18  \n",
-       "\n",
-       "[40 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resultsDF.sort_values(by=['Increase'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "utility-globe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.to_csv('../data/retrofitting/masterRetro_Aug20_2021.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "crazy-scene",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv2",
-   "language": "python",
-   "name": "kgtkenv2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "288px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Archived/Master P279-Dataset Generation.ipynb b/Archived/Master P279-Dataset Generation.ipynb
deleted file mode 100644
index a479fcd..0000000
--- a/Archived/Master P279-Dataset Generation.ipynb	
+++ /dev/null
@@ -1,5729 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "individual-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import scipy.stats as stats\n",
-    "import json\n",
-    "from tqdm.notebook import tqdm\n",
-    "import gzip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "adequate-steering",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Filepaths\n",
-    "\n",
-    "# DWD Files\n",
-    "# DERIVED_IS_COUNTS_FILE = '../data/derived.dwd_isa_class_count.compact.tsv'\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/descriptions.en.tsv.gz\"\n",
-    "P279_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/derived.P279.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documentary-malpractice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_CHILD_PAR_OP_FILE = \"../data/basis/P279_ChildPar.all-distilroberta-v1.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "funny-florist",
-   "metadata": {},
-   "source": [
-    "# Other Utility Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "genuine-memorabilia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Alt\n",
-    "# !zcat ../data/derived.dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > ../data/class-counts.tsv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "threatened-pacific",
-   "metadata": {},
-   "source": [
-    "# P279 ChildPar Dataset\n",
-    "\n",
-    "Use descriptions wherever available, if not use labels, if not skip rows\n",
-    "\n",
-    "Old Version Details:\n",
-    "[Wikidata OS File (Wikidata 2021-02-15 DWD version)](https://drive.google.com/drive/folders/168j3OfdVGXMTKcs6VyH8rq_p0n6w0GGj?usp=sharing)\n",
-    "\n",
-    "There are 721983 rows in P279 dataset, 606996 unique number of nodes in this dataset, 511841 labels and 299844 descriptions\n",
-    "\n",
-    "There are 373463 rows in P279 dataset s.t. both nodes have either a label or a description.     There are 373463 rows where both nodes have labels.     There are 373463 rows where both nodes have descriptions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "polyphonic-county",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz -p \";P279;\" -o ../data/P279_dataset/wikidata-P279.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "silent-liver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv(P279_EDGES_FILE,sep='\\t')\n",
-    "p279DFNodesSet = set(p279DF.node1.to_list() + p279DF.node2.to_list())\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in p279DFNodesSet)]\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in p279DFNodesSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cubic-taxation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3077831 rows in P279 dataset, 2503943 unique number of nodes in this dataset, 2406580 labels and 1369514 descriptions\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(p279DF)} rows in P279 dataset, {len(p279DFNodesSet)} unique number of nodes in this dataset, {len(labelsDF)} labels and {len(descriptionsDF)} descriptions\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "damaged-driving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DFNew = p279DF\n",
-    "temp1 = p279DFNew.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')\n",
-    "temp1 = temp1[['id','label','node2','node2_label']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]\n",
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()\n",
-    "\n",
-    "p279DFNew = hierDF\n",
-    "temp1 = p279DFNew.set_index('node1').join(descriptionsDF.set_index('node1'), rsuffix='_desc')\n",
-    "temp1 = temp1[['id','label','node2','node1_label','node2_label', 'node2_desc']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(descriptionsDF.set_index('node1'), rsuffix='_desc2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node1_label', 'node2_label', 'node2_desc']].rename(columns={'node2_desc': 'node1_desc', 'node2':'node2_desc'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3['node1_desc'] = temp3['node1_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_desc'] = temp3['node2_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dress-yellow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combineLabDesc(row, nodeNum):\n",
-    "    if type(row['node'+str(nodeNum)+'_desc']) == str and type(row['node'+str(nodeNum)+'_label']) == str :\n",
-    "        return row['node'+str(nodeNum)+'_label'] + ' ' + row['node'+str(nodeNum)+'_desc']\n",
-    "    else:\n",
-    "        return None\n",
-    "hierDF['node1_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(1,))\n",
-    "hierDF['node2_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(2,))\n",
-    "hierDF = hierDF[~hierDF['node1_labDesc'].isna()]\n",
-    "hierDF = hierDF[~hierDF['node2_labDesc'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adaptive-measure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"There are {len(hierDF)} rows in P279 dataset s.t. both nodes have either a label or a description. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_label.isna()) & (~hierDF.node2_label.isna())])} rows where both nodes have labels. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_desc.isna()) & (~hierDF.node2_desc.isna())])} rows where both nodes have descriptions.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "disturbed-adaptation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dominant-crowd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "robust-translator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "difficult-applicant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = hierDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "objective-cowboy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.node1_labDesc.isna().sum(), hierDF.node2_labDesc.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "charged-finland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node1_labDesc, modelName)))\n",
-    "hierDF['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node2_labDesc, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "silver-variation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wanted-eclipse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.node1_emb.isna().sum(), hierDF.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "varying-landing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF['similarity_value'] = hierDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "deadly-guard",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.to_csv(P279_CHILD_PAR_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "higher-merchandise",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Filter based on direct P279 counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ordered-basin",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "thirty-display",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "373463"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "recorded-major",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dirP279Counts = pd.read_csv('../data/Master_P279_dataset/hierDF_direct_P279_counts.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "upper-nothing",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1000371</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100047</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052938</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2  node1\n",
-       "0     Q100026      7\n",
-       "1    Q1000371      1\n",
-       "2     Q100047     10\n",
-       "3  Q100052008      4\n",
-       "4  Q100052938      1"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dirP279Counts.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "sunrise-motorcycle",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dirP279Counts = dirP279Counts.set_index('node2')['node1']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "federal-diabetes",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dirP279Counts1 = dirP279Counts[dirP279Counts >= 7]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "superior-today",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "node2\n",
-       "Q100026        7\n",
-       "Q1000371       1\n",
-       "Q100047       10\n",
-       "Q100052008     4\n",
-       "Q100052938     1\n",
-       "Name: node1, dtype: int64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dirP279Counts.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "virgin-freedom",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    109631.000000\n",
-       "mean          5.686056\n",
-       "std         124.365173\n",
-       "min           1.000000\n",
-       "25%           1.000000\n",
-       "50%           2.000000\n",
-       "75%           4.000000\n",
-       "max       39217.000000\n",
-       "Name: node1, dtype: float64"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dirP279Counts.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "received-magic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dirP279CountsNeededSet = set(dirP279Counts1.index.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "damaged-market",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "hierDF_dirP279_filtered = hierDF[hierDF.node2.apply(lambda p: p in dirP279CountsNeededSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "third-queensland",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4864"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(hierDF_dirP279_filtered.node1_desc == hierDF_dirP279_filtered.node2_desc).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "requested-prime",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "hierDF_dirP279_filtered1 = hierDF_dirP279_filtered[hierDF_dirP279_filtered.node1_desc != hierDF_dirP279_filtered.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "intermediate-marsh",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(263907, 259043)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_dirP279_filtered), len(hierDF_dirP279_filtered1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "scientific-martin",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "hierDF_dirP279_filtered1.to_csv('../data/Master_P279_dataset/P279ChildPar_dirP279_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "contained-direction",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "hierDF_dirP279_filtered1[['id', 'node1', 'label', 'node2']].to_csv('../data/Master_P279_dataset/P279ChildPar_dirP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "capital-occasion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.read_csv('../data/Master_P279_dataset/P279ChildPar_dirP279_filtered_baremetal.csv')[['id', 'node1', 'label', 'node2']].to_csv('../data/Master_P279_dataset/P279ChildPar_dirP279_filtered_baremetal.tsv', index=False, sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-jurisdiction",
-   "metadata": {},
-   "source": [
-    "## Filter based on transitive P279 counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "broken-intermediate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "trained-parent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279Counts = pd.read_csv('../data/class-counts.tsv', sep='\\t', header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "tutorial-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279Counts = transitive_P279Counts.set_index(0)[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "great-engine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279Counts = transitive_P279Counts[transitive_P279Counts >= 7]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "matched-dance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    6.853900e+04\n",
-       "mean     2.466702e+04\n",
-       "std      5.315130e+05\n",
-       "min      7.000000e+00\n",
-       "25%      1.200000e+01\n",
-       "50%      2.600000e+01\n",
-       "75%      9.600000e+01\n",
-       "max      4.157572e+07\n",
-       "Name: 1, dtype: float64"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transitive_P279Counts.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "medical-hierarchy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279CountsNeededSet = set(transitive_P279Counts.index.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "peaceful-protocol",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered = hierDF[hierDF.node2.apply(lambda p: p in transitive_P279CountsNeededSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "threaded-insertion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6769"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(hierDF_transP279_filtered.node1_desc == hierDF_transP279_filtered.node2_desc).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "greenhouse-worthy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1 = hierDF_transP279_filtered[hierDF_transP279_filtered.node1_desc != hierDF_transP279_filtered.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "apart-tooth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(310053, 303284)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered), len(hierDF_transP279_filtered1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "spiritual-ballot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1.to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "adjusted-supervision",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1[['id', 'node1', 'label', 'node2']].to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "approved-genome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')[['id', 'node1', 'label', 'node2']].to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.tsv', index=False, sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "extreme-visibility",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>Unnamed: 0.1</th>\n",
-       "      <th>index</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-5.89544833e-01  1.19935878e-01  3.76383096e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.666525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-6.08038664e-01  5.00908047e-02  5.60190260e-...</td>\n",
-       "      <td>[ 1.79253891e-01  6.10661447e-01  8.33987534e-...</td>\n",
-       "      <td>0.700515</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-5.40582001e-01  1.56212196e-01  5.69522917e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.683245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-4.99146163e-01  6.76080063e-02  3.48868877e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.689516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-1.47221148e-01  9.54690054e-02  1.05311513e+...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.603161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  Unnamed: 0.1  index    node2  \\\n",
-       "0           0             0      1  Q100026   \n",
-       "1           1             1      2  Q100026   \n",
-       "2           2             2      3  Q100026   \n",
-       "3           3             3      4  Q100026   \n",
-       "4           4             4      5  Q100026   \n",
-       "\n",
-       "                                  id      node1 label  \\\n",
-       "0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279   \n",
-       "2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279   \n",
-       "3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279   \n",
-       "4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279   \n",
-       "\n",
-       "                                         node2_desc              node1_label  \\\n",
-       "0  1974 fighter aircraft family by General Dynamics                    YF-16   \n",
-       "1  1974 fighter aircraft family by General Dynamics  F-16A/B Fighting Falcon   \n",
-       "2  1974 fighter aircraft family by General Dynamics  F-16C/D Fighting Falcon   \n",
-       "3  1974 fighter aircraft family by General Dynamics    F-16E/F Desert Falcon   \n",
-       "4  1974 fighter aircraft family by General Dynamics               F-16 VISTA   \n",
-       "\n",
-       "            node2_label                                         node1_desc  \\\n",
-       "0  F-16 Fighting Falcon     initial prototype of the F-16 fighter aircraft   \n",
-       "1  F-16 Fighting Falcon        initial series of the F-16 fighter aircraft   \n",
-       "2  F-16 Fighting Falcon      multirole series of the F-16 fighter aircraft   \n",
-       "3  F-16 Fighting Falcon  export strike fighter series of the F-16 fight...   \n",
-       "4  F-16 Fighting Falcon                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \\\n",
-       "0  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "1  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "2  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "3  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "4  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-5.89544833e-01  1.19935878e-01  3.76383096e-...   \n",
-       "1  [-6.08038664e-01  5.00908047e-02  5.60190260e-...   \n",
-       "2  [-5.40582001e-01  1.56212196e-01  5.69522917e-...   \n",
-       "3  [-4.99146163e-01  6.76080063e-02  3.48868877e-...   \n",
-       "4  [-1.47221148e-01  9.54690054e-02  1.05311513e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.666525  \n",
-       "1  [ 1.79253891e-01  6.10661447e-01  8.33987534e-...      0.700515  \n",
-       "2  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.683245  \n",
-       "3  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.689516  \n",
-       "4  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.603161  "
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered.csv')\n",
-    "hierDF_transP279_filtered1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "automated-science",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1[['id', 'node1', 'label', 'node2', 'bert2SentSim']].to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "three-highland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    303284.000000\n",
-       "mean          0.623925\n",
-       "std           0.176450\n",
-       "min          -0.111115\n",
-       "25%           0.496446\n",
-       "50%           0.630122\n",
-       "75%           0.751900\n",
-       "max           1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intermediate-heating",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "advised-stick",
-   "metadata": {},
-   "source": [
-    "## Correct bertsim values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "english-alfred",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.bert.base.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "interpreted-institute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>bert2SentSim</th>\n",
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-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
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-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
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-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-5.40582001e-01  1.56212196e-01  5.69522917e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.683245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-4.99146163e-01  6.76080063e-02  3.48868877e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.689516</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-1.47221148e-01  9.54690054e-02  1.05311513e+...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.603161</td>\n",
-       "    </tr>\n",
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-      "text/plain": [
-       "   Unnamed: 0  Unnamed: 0.1  index    node2  \\\n",
-       "0           0             0      1  Q100026   \n",
-       "1           1             1      2  Q100026   \n",
-       "2           2             2      3  Q100026   \n",
-       "3           3             3      4  Q100026   \n",
-       "4           4             4      5  Q100026   \n",
-       "\n",
-       "                                  id      node1 label  \\\n",
-       "0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279   \n",
-       "2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279   \n",
-       "3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279   \n",
-       "4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279   \n",
-       "\n",
-       "                                         node2_desc              node1_label  \\\n",
-       "0  1974 fighter aircraft family by General Dynamics                    YF-16   \n",
-       "1  1974 fighter aircraft family by General Dynamics  F-16A/B Fighting Falcon   \n",
-       "2  1974 fighter aircraft family by General Dynamics  F-16C/D Fighting Falcon   \n",
-       "3  1974 fighter aircraft family by General Dynamics    F-16E/F Desert Falcon   \n",
-       "4  1974 fighter aircraft family by General Dynamics               F-16 VISTA   \n",
-       "\n",
-       "            node2_label                                         node1_desc  \\\n",
-       "0  F-16 Fighting Falcon     initial prototype of the F-16 fighter aircraft   \n",
-       "1  F-16 Fighting Falcon        initial series of the F-16 fighter aircraft   \n",
-       "2  F-16 Fighting Falcon      multirole series of the F-16 fighter aircraft   \n",
-       "3  F-16 Fighting Falcon  export strike fighter series of the F-16 fight...   \n",
-       "4  F-16 Fighting Falcon                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \\\n",
-       "0  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "1  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "2  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "3  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "4  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-5.89544833e-01  1.19935878e-01  3.76383096e-...   \n",
-       "1  [-6.08038664e-01  5.00908047e-02  5.60190260e-...   \n",
-       "2  [-5.40582001e-01  1.56212196e-01  5.69522917e-...   \n",
-       "3  [-4.99146163e-01  6.76080063e-02  3.48868877e-...   \n",
-       "4  [-1.47221148e-01  9.54690054e-02  1.05311513e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.666525  \n",
-       "1  [ 1.79253891e-01  6.10661447e-01  8.33987534e-...      0.700515  \n",
-       "2  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.683245  \n",
-       "3  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.689516  \n",
-       "4  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.603161  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "committed-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "alpine-virus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "funny-asian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = hierDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "stunning-consolidation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_labDesc.isna().sum(), hierDF.node2_labDesc.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "warming-dinner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "198.2163701057434 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "157.96648454666138 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node1_labDesc, modelName)))\n",
-    "hierDF['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node2_labDesc, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "noticed-electric",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
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-       "      <td>[ 1.79253891e-01  6.10661447e-01  8.33987534e-...</td>\n",
-       "      <td>0.700515</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-5.40582001e-01  1.56212196e-01  5.69522917e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.683245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
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-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.689516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 Fighting Falcon 1974 fighter aircraft fam...</td>\n",
-       "      <td>[-1.47221148e-01  9.54690054e-02  1.05311513e+...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.603161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level_0  Unnamed: 0  Unnamed: 0.1  index    node2  \\\n",
-       "0        0           0             0      1  Q100026   \n",
-       "1        1           1             1      2  Q100026   \n",
-       "2        2           2             2      3  Q100026   \n",
-       "3        3           3             3      4  Q100026   \n",
-       "4        4           4             4      5  Q100026   \n",
-       "\n",
-       "                                  id      node1 label  \\\n",
-       "0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279   \n",
-       "2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279   \n",
-       "3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279   \n",
-       "4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279   \n",
-       "\n",
-       "                                         node2_desc              node1_label  \\\n",
-       "0  1974 fighter aircraft family by General Dynamics                    YF-16   \n",
-       "1  1974 fighter aircraft family by General Dynamics  F-16A/B Fighting Falcon   \n",
-       "2  1974 fighter aircraft family by General Dynamics  F-16C/D Fighting Falcon   \n",
-       "3  1974 fighter aircraft family by General Dynamics    F-16E/F Desert Falcon   \n",
-       "4  1974 fighter aircraft family by General Dynamics               F-16 VISTA   \n",
-       "\n",
-       "            node2_label                                         node1_desc  \\\n",
-       "0  F-16 Fighting Falcon     initial prototype of the F-16 fighter aircraft   \n",
-       "1  F-16 Fighting Falcon        initial series of the F-16 fighter aircraft   \n",
-       "2  F-16 Fighting Falcon      multirole series of the F-16 fighter aircraft   \n",
-       "3  F-16 Fighting Falcon  export strike fighter series of the F-16 fight...   \n",
-       "4  F-16 Fighting Falcon                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \\\n",
-       "0  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "1  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "2  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "3  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "4  F-16 Fighting Falcon 1974 fighter aircraft fam...   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-5.89544833e-01  1.19935878e-01  3.76383096e-...   \n",
-       "1  [-6.08038664e-01  5.00908047e-02  5.60190260e-...   \n",
-       "2  [-5.40582001e-01  1.56212196e-01  5.69522917e-...   \n",
-       "3  [-4.99146163e-01  6.76080063e-02  3.48868877e-...   \n",
-       "4  [-1.47221148e-01  9.54690054e-02  1.05311513e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.666525  \n",
-       "1  [ 1.79253891e-01  6.10661447e-01  8.33987534e-...      0.700515  \n",
-       "2  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.683245  \n",
-       "3  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.689516  \n",
-       "4  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.603161  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "surrounded-thinking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_emb.isna().sum(), hierDF.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "frozen-console",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF['similarity_value'] = hierDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "interior-treat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba6879c669da4812919bee809747ece9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/747 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Alt scoring\n",
-    "from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "crossEncSimVals = model.predict(list(zip(hierDF.node1_labDesc.to_list(), hierDF.node2_labDesc.to_list())), show_progress_bar=True, batch_size=500)\n",
-    "hierDF['similarity_value'] = crossEncSimVals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "optimum-sewing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.to_csv('../data/Master_P279_dataset/P279ChildPar_filtered_cross_enc.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consolidated-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "secret-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confirmed-canberra",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "controlled-perfume",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "through-penalty",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "broke-tolerance",
-   "metadata": {},
-   "source": [
-    "# P279 Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "romantic-corporation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF_dirP279_filtered = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_dirP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "going-custom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "normal-playback",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                                 id      node1 label    node2\n",
-       "0           0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279  Q100026\n",
-       "1           1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279  Q100026\n",
-       "2           2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279  Q100026\n",
-       "3           3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279  Q100026\n",
-       "4           4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279  Q100026"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "interpreted-elimination",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF_dirP279_filtered_left = hierDF_dirP279_filtered.set_index('node2')\n",
-    "# hierDF_dirP279_filtered_right = hierDF_dirP279_filtered_left.copy()\n",
-    "\n",
-    "hierDF_transP279_filtered_left = hierDF_transP279_filtered.set_index('node2')\n",
-    "hierDF_transP279_filtered_right = hierDF_transP279_filtered.copy().set_index('node2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "constant-thread",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF_dirP279_filtered_sibs = hierDF_dirP279_filtered_left.join(hierDF_dirP279_filtered_right, rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "charitable-thesaurus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "hierDF_transP279_filtered_sibs = hierDF_transP279_filtered_left.join(hierDF_transP279_filtered_right, rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consolidated-hampshire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blessed-microwave",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "compliant-measure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.drop(columns=['Unnamed: 0', 'Unnamed: 0_right', 'label_right']).reset_index().rename(columns={'node2': 'par_node', 'node1_right': 'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "anticipated-occasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "steady-jacket",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "italian-slovak",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1 != hierDF_transP279_filtered_sibs1.node2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "digital-oxygen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "109275284"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "fewer-rebel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "failing-accent",
-   "metadata": {},
-   "source": [
-    "## Add labels + descs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "controlled-width",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "prompt-grade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_baremetal.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "american-porter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.iloc[sample_without_replacement(len(hierDF_transP279_filtered_sibs), 10000000, random_state=13)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "third-canon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1.drop(columns=['Unnamed: 0']).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "widespread-construction",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>35336627</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26690795-P279-Q5663900-078ae6ac-0</td>\n",
-       "      <td>Q26690795</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26702202-P279-Q5663900-2e741279-0</td>\n",
-       "      <td>Q26702202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>103575910</td>\n",
-       "      <td>Q99762605</td>\n",
-       "      <td>Q98956067-P279-Q99762605-c31fed7d-0</td>\n",
-       "      <td>Q98956067</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q98954409-P279-Q99762605-e294a05f-0</td>\n",
-       "      <td>Q98954409</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>52173412</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26694052-P279-Q5663900-1876b09d-0</td>\n",
-       "      <td>Q26694052</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26698259-P279-Q5663900-cf00016d-0</td>\n",
-       "      <td>Q26698259</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>34573004</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26690675-P279-Q5663900-6453a6a5-0</td>\n",
-       "      <td>Q26690675</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26692431-P279-Q5663900-d4b30075-0</td>\n",
-       "      <td>Q26692431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2387372</td>\n",
-       "      <td>Q11436</td>\n",
-       "      <td>Q135880-P279-Q11436-96699f83-0</td>\n",
-       "      <td>Q135880</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q7395320-P279-Q11436-c0a6c3fd-0</td>\n",
-       "      <td>Q7395320</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       index   par_node                                   id      node1 label  \\\n",
-       "0   35336627   Q5663900   Q26690795-P279-Q5663900-078ae6ac-0  Q26690795  P279   \n",
-       "1  103575910  Q99762605  Q98956067-P279-Q99762605-c31fed7d-0  Q98956067  P279   \n",
-       "2   52173412   Q5663900   Q26694052-P279-Q5663900-1876b09d-0  Q26694052  P279   \n",
-       "3   34573004   Q5663900   Q26690675-P279-Q5663900-6453a6a5-0  Q26690675  P279   \n",
-       "4    2387372     Q11436       Q135880-P279-Q11436-96699f83-0    Q135880  P279   \n",
-       "\n",
-       "                              id_right      node2  \n",
-       "0   Q26702202-P279-Q5663900-2e741279-0  Q26702202  \n",
-       "1  Q98954409-P279-Q99762605-e294a05f-0  Q98954409  \n",
-       "2   Q26698259-P279-Q5663900-cf00016d-0  Q26698259  \n",
-       "3   Q26692431-P279-Q5663900-d4b30075-0  Q26692431  \n",
-       "4      Q7395320-P279-Q11436-c0a6c3fd-0   Q7395320  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "necessary-gibson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10000000"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "prospective-beast",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dutch-night",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_set = set(hierDF_transP279_filtered_sibs1.node1.to_list() + hierDF_transP279_filtered_sibs1.node2.to_list() + hierDF_transP279_filtered_sibs1.par_node.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "sunset-mercy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ongoing-maryland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8758</th>\n",
-       "      <td>Q1000032-label-en</td>\n",
-       "      <td>Q1000032</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'unschooling'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8970</th>\n",
-       "      <td>Q100004761-label-en</td>\n",
-       "      <td>Q100004761</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'shelf label'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9322</th>\n",
-       "      <td>Q1000064-label-en</td>\n",
-       "      <td>Q1000064</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'nuclear reactor technology'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9558</th>\n",
-       "      <td>Q1000084-label-en</td>\n",
-       "      <td>Q1000084</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'economy of Djibouti'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9716</th>\n",
-       "      <td>Q1000120-label-en</td>\n",
-       "      <td>Q1000120</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'maximal torus'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       id       node1  label                            node2\n",
-       "8758    Q1000032-label-en    Q1000032  label                 'unschooling'@en\n",
-       "8970  Q100004761-label-en  Q100004761  label                 'shelf label'@en\n",
-       "9322    Q1000064-label-en    Q1000064  label  'nuclear reactor technology'@en\n",
-       "9558    Q1000084-label-en    Q1000084  label         'economy of Djibouti'@en\n",
-       "9716    Q1000120-label-en    Q1000120  label               'maximal torus'@en"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "statewide-updating",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "paperback-diagnosis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "radio-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "rising-pride",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "mexican-clothing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_label'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_label'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['par_label'] = hierDF_transP279_filtered_sibs1.par_node.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "rotary-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_desc'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_desc'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: descDict[p][1:-4] if p in descDict else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "minute-chain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node1_label'] + ' ' + p['node1_desc'] + ' is ' + p['par_label'],axis=1)\n",
-    "hierDF_transP279_filtered_sibs1['node2_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node2_label'] + ' ' + p['node2_desc'] + ' is ' + p['par_label'],axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "economic-document",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>35336627</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26690795-P279-Q5663900-078ae6ac-0</td>\n",
-       "      <td>Q26690795</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26702202-P279-Q5663900-2e741279-0</td>\n",
-       "      <td>Q26702202</td>\n",
-       "      <td>mayor of Guarromán</td>\n",
-       "      <td>mayor of La Puebla</td>\n",
-       "      <td>mayor of a place in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>mayor of Guarromán political position in Spain...</td>\n",
-       "      <td>mayor of La Puebla political position in Spain...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>103575910</td>\n",
-       "      <td>Q99762605</td>\n",
-       "      <td>Q98956067-P279-Q99762605-c31fed7d-0</td>\n",
-       "      <td>Q98956067</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q98954409-P279-Q99762605-e294a05f-0</td>\n",
-       "      <td>Q98954409</td>\n",
-       "      <td>mayor of Dobrești</td>\n",
-       "      <td>mayor of Pâncota</td>\n",
-       "      <td>mayor of a place in Romania</td>\n",
-       "      <td>mayor of Dobrești, Bihor county</td>\n",
-       "      <td>public office in Romania</td>\n",
-       "      <td>mayor of Dobrești mayor of Dobrești, Bihor cou...</td>\n",
-       "      <td>mayor of Pâncota public office in Romania is m...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>52173412</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26694052-P279-Q5663900-1876b09d-0</td>\n",
-       "      <td>Q26694052</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26698259-P279-Q5663900-cf00016d-0</td>\n",
-       "      <td>Q26698259</td>\n",
-       "      <td>mayor of Zubieta</td>\n",
-       "      <td>mayor of Congosto</td>\n",
-       "      <td>mayor of a place in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>mayor of Zubieta political position in Spain i...</td>\n",
-       "      <td>mayor of Congosto political position in Spain ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>34573004</td>\n",
-       "      <td>Q5663900</td>\n",
-       "      <td>Q26690675-P279-Q5663900-6453a6a5-0</td>\n",
-       "      <td>Q26690675</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q26692431-P279-Q5663900-d4b30075-0</td>\n",
-       "      <td>Q26692431</td>\n",
-       "      <td>mayor of La Nou de Gaya</td>\n",
-       "      <td>mayor of Pozohondo</td>\n",
-       "      <td>mayor of a place in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>political position in Spain</td>\n",
-       "      <td>mayor of La Nou de Gaya political position in ...</td>\n",
-       "      <td>mayor of Pozohondo political position in Spain...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2387372</td>\n",
-       "      <td>Q11436</td>\n",
-       "      <td>Q135880-P279-Q11436-96699f83-0</td>\n",
-       "      <td>Q135880</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q7395320-P279-Q11436-c0a6c3fd-0</td>\n",
-       "      <td>Q7395320</td>\n",
-       "      <td>A-12</td>\n",
-       "      <td>SZD-49 Jantar K</td>\n",
-       "      <td>aircraft</td>\n",
-       "      <td>1936 autogyro prototype by the Central Aero-Hy...</td>\n",
-       "      <td>glider aircraft</td>\n",
-       "      <td>A-12 1936 autogyro prototype by the Central Ae...</td>\n",
-       "      <td>SZD-49 Jantar K glider aircraft is aircraft</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       index   par_node                                   id      node1 label  \\\n",
-       "0   35336627   Q5663900   Q26690795-P279-Q5663900-078ae6ac-0  Q26690795  P279   \n",
-       "1  103575910  Q99762605  Q98956067-P279-Q99762605-c31fed7d-0  Q98956067  P279   \n",
-       "2   52173412   Q5663900   Q26694052-P279-Q5663900-1876b09d-0  Q26694052  P279   \n",
-       "3   34573004   Q5663900   Q26690675-P279-Q5663900-6453a6a5-0  Q26690675  P279   \n",
-       "4    2387372     Q11436       Q135880-P279-Q11436-96699f83-0    Q135880  P279   \n",
-       "\n",
-       "                              id_right      node2              node1_label  \\\n",
-       "0   Q26702202-P279-Q5663900-2e741279-0  Q26702202       mayor of Guarromán   \n",
-       "1  Q98954409-P279-Q99762605-e294a05f-0  Q98954409        mayor of Dobrești   \n",
-       "2   Q26698259-P279-Q5663900-cf00016d-0  Q26698259         mayor of Zubieta   \n",
-       "3   Q26692431-P279-Q5663900-d4b30075-0  Q26692431  mayor of La Nou de Gaya   \n",
-       "4      Q7395320-P279-Q11436-c0a6c3fd-0   Q7395320                     A-12   \n",
-       "\n",
-       "          node2_label                    par_label  \\\n",
-       "0  mayor of La Puebla    mayor of a place in Spain   \n",
-       "1    mayor of Pâncota  mayor of a place in Romania   \n",
-       "2   mayor of Congosto    mayor of a place in Spain   \n",
-       "3  mayor of Pozohondo    mayor of a place in Spain   \n",
-       "4     SZD-49 Jantar K                     aircraft   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0                        political position in Spain   \n",
-       "1                    mayor of Dobrești, Bihor county   \n",
-       "2                        political position in Spain   \n",
-       "3                        political position in Spain   \n",
-       "4  1936 autogyro prototype by the Central Aero-Hy...   \n",
-       "\n",
-       "                    node2_desc  \\\n",
-       "0  political position in Spain   \n",
-       "1     public office in Romania   \n",
-       "2  political position in Spain   \n",
-       "3  political position in Spain   \n",
-       "4              glider aircraft   \n",
-       "\n",
-       "                                          node1_sent  \\\n",
-       "0  mayor of Guarromán political position in Spain...   \n",
-       "1  mayor of Dobrești mayor of Dobrești, Bihor cou...   \n",
-       "2  mayor of Zubieta political position in Spain i...   \n",
-       "3  mayor of La Nou de Gaya political position in ...   \n",
-       "4  A-12 1936 autogyro prototype by the Central Ae...   \n",
-       "\n",
-       "                                          node2_sent  \n",
-       "0  mayor of La Puebla political position in Spain...  \n",
-       "1  mayor of Pâncota public office in Romania is m...  \n",
-       "2  mayor of Congosto political position in Spain ...  \n",
-       "3  mayor of Pozohondo political position in Spain...  \n",
-       "4        SZD-49 Jantar K glider aircraft is aircraft  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "official-uniform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF_transP279_filtered_sibs1.to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "metric-essex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1[['id', 'node1', 'label', 'node2', 'par_node']].to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "closing-neighbor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1_desc != hierDF_transP279_filtered_sibs1.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "young-british",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:3')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "heavy-composite",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "chemical-simon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs2.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "protecting-reply",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3682.2832567691803 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3649.976934671402 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs2['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node1_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs2['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node2_sent, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "infrared-success",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a35cdeb3c28e445dbfb84b318876a709",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "432a9ea31712417b8b6910e4b16c2008",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fea59ce42344dfaa116390c720ade24",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4f09ee455fd4edcb3d7cde46fa6b141",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ed068be329844b696662aaa852d938c",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84eafe207915471b804c37f346d69474",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56aa42aa7cbb4980992bbe5feb3078db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/3708 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Alt scoring\n",
-    "from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "crossEncSimVals = model.predict(list(zip(hierDF_transP279_filtered_sibs2.node1_sent.to_list(), hierDF_transP279_filtered_sibs2.node2_sent.to_list())), show_progress_bar=True, batch_size=1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "complete-contrary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2['similarity_value'] = crossEncSimVals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "complete-factory",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.707847e+06\n",
-       "mean     4.197320e-01\n",
-       "std      2.005948e-01\n",
-       "min      8.680474e-03\n",
-       "25%      2.612122e-01\n",
-       "50%      4.245352e-01\n",
-       "75%      5.762920e-01\n",
-       "max      9.739158e-01\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs2['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "rapid-communications",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2[['id', 'node1', 'label', 'node2', 'par_node', 'similarity_value']].to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed_cross_enc.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "jewish-magazine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.70532316, -0.61362374,  0.10314991],\n",
-       "       [ 2.0433385 , -1.5074049 , -0.3842408 ]], dtype=float32)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "crossEncSimVals[:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "favorite-raising",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.57001173, -1.8462102 ,  1.5409116 ], dtype=float32)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict([\"mayor of Dobrești mayor of Dobrești, Bihor \", \"mayor of Pâncota public office in Romania is\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "involved-controversy",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>level_0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>103575910</td>\n",
-       "      <td>Q99762605</td>\n",
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-       "      <td>P279</td>\n",
-       "      <td>Q98954409-P279-Q99762605-e294a05f-0</td>\n",
-       "      <td>Q98954409</td>\n",
-       "      <td>mayor of Dobrești</td>\n",
-       "      <td>mayor of Pâncota</td>\n",
-       "      <td>mayor of a place in Romania</td>\n",
-       "      <td>mayor of Dobrești, Bihor county</td>\n",
-       "      <td>public office in Romania</td>\n",
-       "      <td>mayor of Dobrești mayor of Dobrești, Bihor cou...</td>\n",
-       "      <td>mayor of Pâncota public office in Romania is m...</td>\n",
-       "      <td>[0.29432657, -0.12315029, -0.045695502, 0.3632...</td>\n",
-       "      <td>[0.40726575, -0.3538948, -0.19948234, -0.10712...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2387372</td>\n",
-       "      <td>Q11436</td>\n",
-       "      <td>Q135880-P279-Q11436-96699f83-0</td>\n",
-       "      <td>Q135880</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q7395320-P279-Q11436-c0a6c3fd-0</td>\n",
-       "      <td>Q7395320</td>\n",
-       "      <td>A-12</td>\n",
-       "      <td>SZD-49 Jantar K</td>\n",
-       "      <td>aircraft</td>\n",
-       "      <td>1936 autogyro prototype by the Central Aero-Hy...</td>\n",
-       "      <td>glider aircraft</td>\n",
-       "      <td>A-12 1936 autogyro prototype by the Central Ae...</td>\n",
-       "      <td>SZD-49 Jantar K glider aircraft is aircraft</td>\n",
-       "      <td>[-0.19948986, -0.35904005, 0.09085509, 0.16439...</td>\n",
-       "      <td>[-0.22941455, -0.05107765, 0.3621345, 0.495969...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>24016847</td>\n",
-       "      <td>Q21167512</td>\n",
-       "      <td>Q69525859-P279-Q21167512-f5328a1d-0</td>\n",
-       "      <td>Q69525859</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q21175029-P279-Q21167512-56cdfd0e-0</td>\n",
-       "      <td>Q21175029</td>\n",
-       "      <td>sodium carbonate exposures</td>\n",
-       "      <td>pentaborane exposure</td>\n",
-       "      <td>chemical hazard</td>\n",
-       "      <td>hazardous chemical exposures</td>\n",
-       "      <td>hazardous chemical exposure</td>\n",
-       "      <td>sodium carbonate exposures hazardous chemical ...</td>\n",
-       "      <td>pentaborane exposure hazardous chemical exposu...</td>\n",
-       "      <td>[-0.23254174, -0.019664198, -0.49748045, 0.018...</td>\n",
-       "      <td>[-0.57571846, -0.29551026, -0.50132465, -0.239...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10</td>\n",
-       "      <td>108650504</td>\n",
-       "      <td>Q99762605</td>\n",
-       "      <td>Q98958530-P279-Q99762605-fdded642-0</td>\n",
-       "      <td>Q98958530</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q98956734-P279-Q99762605-2dbce366-0</td>\n",
-       "      <td>Q98956734</td>\n",
-       "      <td>mayor of Urziceni</td>\n",
-       "      <td>mayor of Ionești</td>\n",
-       "      <td>mayor of a place in Romania</td>\n",
-       "      <td>mayor of Urziceni, Satu Mare county</td>\n",
-       "      <td>mayor of Ionești, Vâlcea county</td>\n",
-       "      <td>mayor of Urziceni mayor of Urziceni, Satu Mare...</td>\n",
-       "      <td>mayor of Ionești mayor of Ionești, Vâlcea coun...</td>\n",
-       "      <td>[0.5272284, -0.36796138, -0.053719513, 0.40885...</td>\n",
-       "      <td>[0.11683124, -0.23857604, -0.057602327, -0.193...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>11</td>\n",
-       "      <td>106811689</td>\n",
-       "      <td>Q99762605</td>\n",
-       "      <td>Q98957693-P279-Q99762605-5c11a747-0</td>\n",
-       "      <td>Q98957693</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q98955251-P279-Q99762605-400c2ed9-0</td>\n",
-       "      <td>Q98955251</td>\n",
-       "      <td>mayor of Racovițeni</td>\n",
-       "      <td>mayor of Blandiana</td>\n",
-       "      <td>mayor of a place in Romania</td>\n",
-       "      <td>mayor of Racovițeni, Buzău county</td>\n",
-       "      <td>mayor of Blandiana, Alba county</td>\n",
-       "      <td>mayor of Racovițeni mayor of Racovițeni, Buzău...</td>\n",
-       "      <td>mayor of Blandiana mayor of Blandiana, Alba co...</td>\n",
-       "      <td>[0.34337035, -0.25570026, -0.20981959, 0.10492...</td>\n",
-       "      <td>[0.36922738, 0.20065087, -0.18316983, 0.193541...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level_0      index   par_node                                   id  \\\n",
-       "0        1  103575910  Q99762605  Q98956067-P279-Q99762605-c31fed7d-0   \n",
-       "1        4    2387372     Q11436       Q135880-P279-Q11436-96699f83-0   \n",
-       "2        5   24016847  Q21167512  Q69525859-P279-Q21167512-f5328a1d-0   \n",
-       "3       10  108650504  Q99762605  Q98958530-P279-Q99762605-fdded642-0   \n",
-       "4       11  106811689  Q99762605  Q98957693-P279-Q99762605-5c11a747-0   \n",
-       "\n",
-       "       node1 label                             id_right      node2  \\\n",
-       "0  Q98956067  P279  Q98954409-P279-Q99762605-e294a05f-0  Q98954409   \n",
-       "1    Q135880  P279      Q7395320-P279-Q11436-c0a6c3fd-0   Q7395320   \n",
-       "2  Q69525859  P279  Q21175029-P279-Q21167512-56cdfd0e-0  Q21175029   \n",
-       "3  Q98958530  P279  Q98956734-P279-Q99762605-2dbce366-0  Q98956734   \n",
-       "4  Q98957693  P279  Q98955251-P279-Q99762605-400c2ed9-0  Q98955251   \n",
-       "\n",
-       "                  node1_label           node2_label  \\\n",
-       "0           mayor of Dobrești      mayor of Pâncota   \n",
-       "1                        A-12       SZD-49 Jantar K   \n",
-       "2  sodium carbonate exposures  pentaborane exposure   \n",
-       "3           mayor of Urziceni      mayor of Ionești   \n",
-       "4         mayor of Racovițeni    mayor of Blandiana   \n",
-       "\n",
-       "                     par_label  \\\n",
-       "0  mayor of a place in Romania   \n",
-       "1                     aircraft   \n",
-       "2              chemical hazard   \n",
-       "3  mayor of a place in Romania   \n",
-       "4  mayor of a place in Romania   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0                    mayor of Dobrești, Bihor county   \n",
-       "1  1936 autogyro prototype by the Central Aero-Hy...   \n",
-       "2                       hazardous chemical exposures   \n",
-       "3                mayor of Urziceni, Satu Mare county   \n",
-       "4                  mayor of Racovițeni, Buzău county   \n",
-       "\n",
-       "                        node2_desc  \\\n",
-       "0         public office in Romania   \n",
-       "1                  glider aircraft   \n",
-       "2      hazardous chemical exposure   \n",
-       "3  mayor of Ionești, Vâlcea county   \n",
-       "4  mayor of Blandiana, Alba county   \n",
-       "\n",
-       "                                          node1_sent  \\\n",
-       "0  mayor of Dobrești mayor of Dobrești, Bihor cou...   \n",
-       "1  A-12 1936 autogyro prototype by the Central Ae...   \n",
-       "2  sodium carbonate exposures hazardous chemical ...   \n",
-       "3  mayor of Urziceni mayor of Urziceni, Satu Mare...   \n",
-       "4  mayor of Racovițeni mayor of Racovițeni, Buzău...   \n",
-       "\n",
-       "                                          node2_sent  \\\n",
-       "0  mayor of Pâncota public office in Romania is m...   \n",
-       "1        SZD-49 Jantar K glider aircraft is aircraft   \n",
-       "2  pentaborane exposure hazardous chemical exposu...   \n",
-       "3  mayor of Ionești mayor of Ionești, Vâlcea coun...   \n",
-       "4  mayor of Blandiana mayor of Blandiana, Alba co...   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [0.29432657, -0.12315029, -0.045695502, 0.3632...   \n",
-       "1  [-0.19948986, -0.35904005, 0.09085509, 0.16439...   \n",
-       "2  [-0.23254174, -0.019664198, -0.49748045, 0.018...   \n",
-       "3  [0.5272284, -0.36796138, -0.053719513, 0.40885...   \n",
-       "4  [0.34337035, -0.25570026, -0.20981959, 0.10492...   \n",
-       "\n",
-       "                                           node2_emb  \n",
-       "0  [0.40726575, -0.3538948, -0.19948234, -0.10712...  \n",
-       "1  [-0.22941455, -0.05107765, 0.3621345, 0.495969...  \n",
-       "2  [-0.57571846, -0.29551026, -0.50132465, -0.239...  \n",
-       "3  [0.11683124, -0.23857604, -0.057602327, -0.193...  \n",
-       "4  [0.36922738, 0.20065087, -0.18316983, 0.193541...  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "rural-vocabulary",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs2.node1_emb.isna().sum(), hierDF_transP279_filtered_sibs2.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "basic-extreme",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2['similarity_value'] = hierDF_transP279_filtered_sibs2.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "sweet-remainder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.707847e+06\n",
-       "mean     5.596878e-01\n",
-       "std      1.731009e-01\n",
-       "min     -1.290048e-01\n",
-       "25%      4.218415e-01\n",
-       "50%      5.907606e-01\n",
-       "75%      7.042572e-01\n",
-       "max      9.998616e-01\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs2['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "lasting-torture",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset reduced from 10000000 to 3707847 by eliminating rows where node1_desc == node2_desc\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset reduced from {len(hierDF_transP279_filtered_sibs1)} to {len(hierDF_transP279_filtered_sibs2)} by eliminating rows where node1_desc == node2_desc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "continental-interim",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2[['id', 'node1', 'label', 'node2', 'par_node', 'similarity_value']].to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cathedral-charge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import pandas as pd\n",
-    "# pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collect-picking",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "declared-monroe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "opening-rally",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_nodes = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "diagnostic-guess",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "utility-prophet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "treated-anniversary",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 40.2 s, sys: 779 ms, total: 40.9 s\n",
-      "Wall time: 40.9 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "398221"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "P279_3M_data.apply(lambda p: p['node1'] in wordsim_nodes or p['node2'] in wordsim_nodes or p['par_node'] in wordsim_nodes, axis=1).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "laughing-blake",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1min 48s, sys: 2.78 s, total: 1min 51s\n",
-      "Wall time: 1min 51s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "406185"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "P279_10M_data.apply(lambda p: p['node1'] in wordsim_nodes or p['node2'] in wordsim_nodes or p['par_node'] in wordsim_nodes, axis=1).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "traditional-guess",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_10M_data[P279_10M_data.apply(lambda p: p['node1'] in wordsim_nodes or p['node2'] in wordsim_nodes or p['par_node'] in wordsim_nodes, axis=1)].to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_wordsim_only.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "loved-arkansas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_19k_data = pd.read_csv('../data/P279_dataset/P279_19k_Siblings_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cloudy-encoding",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2897760</td>\n",
-       "      <td>Q7047901</td>\n",
-       "      <td>Q17517</td>\n",
-       "      <td>Q7410591</td>\n",
-       "      <td>Nokia 5330 Mobile TV Edition</td>\n",
-       "      <td>mobile phone</td>\n",
-       "      <td>Samsung SGH-i780</td>\n",
-       "      <td>Nokia 5330 Mobile TV Edition is mobile phone</td>\n",
-       "      <td>Samsung SGH-i780 is mobile phone</td>\n",
-       "      <td>mobile phone is typically Nokia 5330 Mobile TV...</td>\n",
-       "      <td>[-5.48004732e-02  3.50154340e-01  4.38094527e-...</td>\n",
-       "      <td>[-2.98046470e-01  3.74356419e-01  5.27383387e-...</td>\n",
-       "      <td>0.883490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>10431390</td>\n",
-       "      <td>Q15991265</td>\n",
-       "      <td>Q2736</td>\n",
-       "      <td>Q15991267</td>\n",
-       "      <td>amateur football</td>\n",
-       "      <td>association football</td>\n",
-       "      <td>professional football</td>\n",
-       "      <td>amateur football is association football</td>\n",
-       "      <td>professional football is association football</td>\n",
-       "      <td>association football is typically amateur foot...</td>\n",
-       "      <td>[-1.95897296e-01  1.04528800e-01  4.40772206e-...</td>\n",
-       "      <td>[-2.94859111e-01  1.88360184e-01 -1.96894854e-...</td>\n",
-       "      <td>0.803443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>417162</td>\n",
-       "      <td>Q15222772</td>\n",
-       "      <td>Q1420</td>\n",
-       "      <td>Q7246913</td>\n",
-       "      <td>Glider (automobiles)</td>\n",
-       "      <td>motor car</td>\n",
-       "      <td>Probe 16</td>\n",
-       "      <td>Glider (automobiles) is motor car</td>\n",
-       "      <td>Probe 16 is motor car</td>\n",
-       "      <td>motor car is typically Glider (automobiles) or...</td>\n",
-       "      <td>[-1.21378571e-01  3.04012895e-01  4.18751419e-...</td>\n",
-       "      <td>[ 0.16769831  0.62631613  1.0727373   0.235646...</td>\n",
-       "      <td>0.760663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>420822</td>\n",
-       "      <td>Q15609267</td>\n",
-       "      <td>Q1420</td>\n",
-       "      <td>Q16997137</td>\n",
-       "      <td>Isotta Fraschini Tipo D</td>\n",
-       "      <td>motor car</td>\n",
-       "      <td>Allard M2</td>\n",
-       "      <td>Isotta Fraschini Tipo D is motor car</td>\n",
-       "      <td>Allard M2 is motor car</td>\n",
-       "      <td>motor car is typically Isotta Fraschini Tipo D...</td>\n",
-       "      <td>[ 1.71256125e-01  2.56909907e-01  7.91365981e-...</td>\n",
-       "      <td>[-3.38893235e-01  3.94817479e-02  6.53142512e-...</td>\n",
-       "      <td>0.851455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3361954</td>\n",
-       "      <td>Q10748135</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q17116114</td>\n",
-       "      <td>kelewele</td>\n",
-       "      <td>food</td>\n",
-       "      <td>Horsebread</td>\n",
-       "      <td>kelewele is food</td>\n",
-       "      <td>Horsebread is food</td>\n",
-       "      <td>food is typically kelewele or Horsebread</td>\n",
-       "      <td>[ 2.51727879e-01 -1.59988493e-01  1.39032674e+...</td>\n",
-       "      <td>[-2.70331770e-01 -6.03621602e-01  7.06666887e-...</td>\n",
-       "      <td>0.728477</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0     index      node1  parent      node2  \\\n",
-       "0           0   2897760   Q7047901  Q17517   Q7410591   \n",
-       "1           1  10431390  Q15991265   Q2736  Q15991267   \n",
-       "2           2    417162  Q15222772   Q1420   Q7246913   \n",
-       "3           3    420822  Q15609267   Q1420  Q16997137   \n",
-       "4           4   3361954  Q10748135   Q2095  Q17116114   \n",
-       "\n",
-       "                    node1_label             par_label            node2_label  \\\n",
-       "0  Nokia 5330 Mobile TV Edition          mobile phone       Samsung SGH-i780   \n",
-       "1              amateur football  association football  professional football   \n",
-       "2          Glider (automobiles)             motor car               Probe 16   \n",
-       "3       Isotta Fraschini Tipo D             motor car              Allard M2   \n",
-       "4                      kelewele                  food             Horsebread   \n",
-       "\n",
-       "                                     node1_sent  \\\n",
-       "0  Nokia 5330 Mobile TV Edition is mobile phone   \n",
-       "1      amateur football is association football   \n",
-       "2             Glider (automobiles) is motor car   \n",
-       "3          Isotta Fraschini Tipo D is motor car   \n",
-       "4                              kelewele is food   \n",
-       "\n",
-       "                                      node2_sent  \\\n",
-       "0               Samsung SGH-i780 is mobile phone   \n",
-       "1  professional football is association football   \n",
-       "2                          Probe 16 is motor car   \n",
-       "3                         Allard M2 is motor car   \n",
-       "4                             Horsebread is food   \n",
-       "\n",
-       "                                           comb_sent  \\\n",
-       "0  mobile phone is typically Nokia 5330 Mobile TV...   \n",
-       "1  association football is typically amateur foot...   \n",
-       "2  motor car is typically Glider (automobiles) or...   \n",
-       "3  motor car is typically Isotta Fraschini Tipo D...   \n",
-       "4           food is typically kelewele or Horsebread   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-5.48004732e-02  3.50154340e-01  4.38094527e-...   \n",
-       "1  [-1.95897296e-01  1.04528800e-01  4.40772206e-...   \n",
-       "2  [-1.21378571e-01  3.04012895e-01  4.18751419e-...   \n",
-       "3  [ 1.71256125e-01  2.56909907e-01  7.91365981e-...   \n",
-       "4  [ 2.51727879e-01 -1.59988493e-01  1.39032674e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [-2.98046470e-01  3.74356419e-01  5.27383387e-...      0.883490  \n",
-       "1  [-2.94859111e-01  1.88360184e-01 -1.96894854e-...      0.803443  \n",
-       "2  [ 0.16769831  0.62631613  1.0727373   0.235646...      0.760663  \n",
-       "3  [-3.38893235e-01  3.94817479e-02  6.53142512e-...      0.851455  \n",
-       "4  [-2.70331770e-01 -6.03621602e-01  7.06666887e-...      0.728477  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P279_19k_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "creative-lloyd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 305 ms, sys: 2.01 ms, total: 307 ms\n",
-      "Wall time: 304 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "19454"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "P279_19k_data.apply(lambda p: p['node1'] in wordsim_nodes or p['node2'] in wordsim_nodes or p['parent'] in wordsim_nodes, axis=1).sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "radio-trainer",
-   "metadata": {},
-   "source": [
-    "## Correct bertsim values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "stupid-doubt",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "EmptyDataError",
-     "evalue": "No columns to parse from file",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mEmptyDataError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-a356dd453328>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhierDF_transP279_filtered_sibs1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../data/Master_P279_dataset/P279Siblings_transP279_filtered.tsv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             )\n\u001b[1;32m   1049\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1897\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mEmptyDataError\u001b[0m: No columns to parse from file"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "placed-michigan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1_desc != hierDF_transP279_filtered_sibs1.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "ethical-bacteria",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset reduced from 10000000 to 3707847 by eliminating rows where node1_desc == node2_desc\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset reduced from {len(hierDF_transP279_filtered_sibs1)} to {len(hierDF_transP279_filtered_sibs2)} by eliminating rows where node1_desc == node2_desc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "welsh-blast",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "individual-neighborhood",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "worldwide-mayor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15682.8797955513 s\n",
-      "15108.38348031044 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs1.node1_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs1['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs1.node2_sent, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "weighted-raise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.node1_emb.isna().sum(), hierDF_transP279_filtered_sibs1.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "monetary-experiment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['similarity_value'] = hierDF_transP279_filtered_sibs1.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "efficient-cancer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.000000e+07\n",
-       "mean     9.141334e-01\n",
-       "std      1.338514e-01\n",
-       "min     -5.180746e-02\n",
-       "25%      8.585989e-01\n",
-       "50%      9.876406e-01\n",
-       "75%      9.928079e-01\n",
-       "max      1.000000e+00\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "scientific-fossil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2[['id', 'node1', 'label', 'node2', 'par_node', 'similarity_value']].to_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wanted-vault",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "automated-heath",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-switzerland",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "neither-consumer",
-   "metadata": {},
-   "source": [
-    "# Class Similarity Datasets\n",
-    "\n",
-    "`zcat derived.dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > class-counts.tsv`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spread-snapshot",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "supposed-newport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "reliable-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2[['node1', 'node2']].rename(columns={'node1':'q1', 'node2':'q2'}).to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols_FOR_CLASS.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "impressed-airfare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_dict = {}\n",
-    "with open('../data/derived.dwd_isa_class_count.compact.tsv') as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            class_dict[line[0]] = line[2].strip()\n",
-    "all_class_count_dict = {}\n",
-    "with open('../data/class-counts.tsv') as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            all_class_count_dict[line[0]] = line[1].strip()\n",
-    "\n",
-    "\n",
-    "class SemanticSimilarity(object):\n",
-    "    def __init__(self):\n",
-    "#         self.config = config\n",
-    "#         self.embeddings_type = ['class']\n",
-    "        self.N = float(52546967)\n",
-    "        self.all_class_count_dict = all_class_count_dict\n",
-    "        \n",
-    "    def get_qnode_details(self, qnodes: list):\n",
-    "        global class_dict\n",
-    "        qnodes_dict = {}\n",
-    "        qnodes_dict[qnodes[0]] = class_dict.get(qnodes[0],'')\n",
-    "        qnodes_dict[qnodes[1]] = class_dict.get(qnodes[1],'')\n",
-    "        return qnodes_dict\n",
-    "        \n",
-    "    def compute_class_similarity(self, q1, q2):\n",
-    "        qnodes_dict = self.get_qnode_details([q1, q2])\n",
-    "#         print(qnodes_dict)\n",
-    "        feature_dict, feature_count_dict = self.build_qnode_feature_dict(qnodes_dict)\n",
-    "#         print(feature_dict, feature_count_dict)\n",
-    "        normalized_classes_idf = self.normalize_idf_classes(feature_dict, feature_count_dict)\n",
-    "        if q1 in feature_dict and q2 in feature_dict:\n",
-    "            q1_cl = set(feature_dict[q1])\n",
-    "            q2_cl = set(feature_dict[q2])\n",
-    "            q1_q2_intersection = q1_cl.intersection(q2_cl)\n",
-    "\n",
-    "            _similarity = sum([normalized_classes_idf.get(c) for c in q1_q2_intersection])\n",
-    "            return {\n",
-    "                'similarity': _similarity\n",
-    "            }\n",
-    "        return {\n",
-    "            'similarity': None\n",
-    "        }\n",
-    "\n",
-    "    def build_qnode_feature_dict(self, qnodes_dict: dict) -> (dict, dict):\n",
-    "        feature_dict = {}\n",
-    "        feature_count_dict = {}\n",
-    "\n",
-    "        for qnode in qnodes_dict:\n",
-    "            feature_val = []\n",
-    "            \n",
-    "            if '|' in qnodes_dict[qnode]:\n",
-    "                cl = qnodes_dict[qnode].split(\"|\")\n",
-    "\n",
-    "                for c in cl:\n",
-    "                    vals = c.split(\":\")\n",
-    "                    feature_val.append(vals[0])\n",
-    "                    feature_count_dict[vals[0]] = float(vals[1])\n",
-    "            if qnode not in feature_val:\n",
-    "                feature_val.append(qnode)\n",
-    "            if qnode not in feature_count_dict:\n",
-    "                feature_count_dict[qnode] = float(self.all_class_count_dict.get(qnode, 1.0))\n",
-    "            feature_dict[qnode] = feature_val\n",
-    "\n",
-    "        return feature_dict, feature_count_dict\n",
-    "\n",
-    "    def normalize_idf_classes(self, feature_dict, feature_count_dict):\n",
-    "\n",
-    "        classes_count = {}\n",
-    "        for qnode in feature_dict:\n",
-    "\n",
-    "            classes = feature_dict[qnode]\n",
-    "            for c in classes:\n",
-    "                if c not in classes_count:\n",
-    "                    classes_count[c] = 0\n",
-    "                classes_count[c] += 1\n",
-    "\n",
-    "        classes_idf = self.calculate_idf_features(feature_count_dict)\n",
-    "\n",
-    "        # multiply class count with idf\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_count[c] * classes_idf[c]\n",
-    "\n",
-    "        # normalize the idf scores so that they sum to 1\n",
-    "        classes_idf_sum = sum([classes_idf[x] for x in classes_idf])\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_idf[c] / classes_idf_sum\n",
-    "\n",
-    "        return classes_idf\n",
-    "\n",
-    "    def calculate_idf_features(self, feature_count_dict):\n",
-    "        _ = {}\n",
-    "        for c in feature_count_dict:\n",
-    "            _[c] = math.log(self.N / feature_count_dict[c])\n",
-    "        return _"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flying-perception",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "# import sys\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "#     try:\n",
-    "#         row['classSim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "#     except Exception as exc:\n",
-    "#         print(exc)\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     row['Resp_code'] = resp\n",
-    "#     return row\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "empirical-suspect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "helpful-medline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "activated-excuse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "baking-input",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cognitive-decade",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "pacific-warehouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outdoor-sense",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_3M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "handy-coral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_10M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "descending-validity",
-   "metadata": {},
-   "source": [
-    "# JC Similarity Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "distinct-chamber",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "indonesian-jersey",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "remarkable-wallet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "978c7892341342029d881b0bf31a6f66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/303284 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "could not convert string to float: ''\n",
-      "Resp not found for Q102108504, Q100171002\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "def fetchJCSim(row):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "    try:\n",
-    "        row['similarity_value'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['similarity_value'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "indoor-inspection",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Unnamed: 0                                      0\n",
-       " id              Q17372279-P279-Q100026-beba8cd1-0\n",
-       " node1                                   Q17372279\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.666525\n",
-       " classSim                                 0.835782\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 0, dtype: object,\n",
-       " Unnamed: 0                                      1\n",
-       " id              Q17372377-P279-Q100026-fd42bd71-0\n",
-       " node1                                   Q17372377\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.700515\n",
-       " classSim                                  0.94674\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 1, dtype: object,\n",
-       " Unnamed: 0                                      2\n",
-       " id              Q17372444-P279-Q100026-ca0fc4bd-0\n",
-       " node1                                   Q17372444\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.683245\n",
-       " classSim                                 0.946647\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 2, dtype: object,\n",
-       " Unnamed: 0                                      3\n",
-       " id              Q17372455-P279-Q100026-c2b1bf36-0\n",
-       " node1                                   Q17372455\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.689516\n",
-       " classSim                                 0.943256\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 3, dtype: object,\n",
-       " Unnamed: 0                                     4\n",
-       " id              Q2029940-P279-Q100026-ceba4380-0\n",
-       " node1                                   Q2029940\n",
-       " label                                       P279\n",
-       " node2                                    Q100026\n",
-       " bert2SentSim                            0.603161\n",
-       " classSim                                0.809487\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 4, dtype: object,\n",
-       " Unnamed: 0                                    5\n",
-       " id              Q209480-P279-Q100026-a218e96b-0\n",
-       " node1                                   Q209480\n",
-       " label                                      P279\n",
-       " node2                                   Q100026\n",
-       " bert2SentSim                            0.61352\n",
-       " classSim                               0.975716\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 5, dtype: object,\n",
-       " Unnamed: 0                                      6\n",
-       " id              Q77929250-P279-Q100026-964ec021-0\n",
-       " node1                                   Q77929250\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                              0.76767\n",
-       " classSim                                 0.961308\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 6, dtype: object,\n",
-       " Unnamed: 0                                      7\n",
-       " id              Q1087043-P279-Q1000371-945e7797-0\n",
-       " node1                                    Q1087043\n",
-       " label                                        P279\n",
-       " node2                                    Q1000371\n",
-       " bert2SentSim                             0.699843\n",
-       " classSim                                 0.671266\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 7, dtype: object,\n",
-       " Unnamed: 0                                    8\n",
-       " id              Q384656-P279-Q100047-6147b9a8-0\n",
-       " node1                                   Q384656\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 8, dtype: object,\n",
-       " Unnamed: 0                                    9\n",
-       " id              Q471089-P279-Q100047-473b504b-0\n",
-       " node1                                   Q471089\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.679593\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 9, dtype: object,\n",
-       " Unnamed: 0                                   10\n",
-       " id              Q471094-P279-Q100047-14b1b1df-0\n",
-       " node1                                   Q471094\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 10, dtype: object,\n",
-       " Unnamed: 0                                   11\n",
-       " id              Q471102-P279-Q100047-886a847c-0\n",
-       " node1                                   Q471102\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 11, dtype: object,\n",
-       " Unnamed: 0                                   12\n",
-       " id              Q471127-P279-Q100047-6738fc46-0\n",
-       " node1                                   Q471127\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 12, dtype: object,\n",
-       " Unnamed: 0                                   13\n",
-       " id              Q471144-P279-Q100047-766e1fa3-0\n",
-       " node1                                   Q471144\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.811462\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 13, dtype: object,\n",
-       " Unnamed: 0                                   14\n",
-       " id              Q471158-P279-Q100047-7955aa40-0\n",
-       " node1                                   Q471158\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 14, dtype: object,\n",
-       " Unnamed: 0                                         15\n",
-       " id              Q100052562-P279-Q100052008-1a4fa432-0\n",
-       " node1                                      Q100052562\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.515993\n",
-       " classSim                                     0.913344\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 15, dtype: object,\n",
-       " Unnamed: 0                                         16\n",
-       " id              Q100052791-P279-Q100052008-1d15b1ae-0\n",
-       " node1                                      Q100052791\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.478483\n",
-       " classSim                                     0.907455\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 16, dtype: object,\n",
-       " Unnamed: 0                                         17\n",
-       " id              Q100119502-P279-Q100052008-d2c330a4-0\n",
-       " node1                                      Q100119502\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                  0.51221\n",
-       " classSim                                     0.913229\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 17, dtype: object,\n",
-       " Unnamed: 0                                        18\n",
-       " id              Q88594442-P279-Q100052008-badba21b-0\n",
-       " node1                                      Q88594442\n",
-       " label                                           P279\n",
-       " node2                                     Q100052008\n",
-       " bert2SentSim                                0.424672\n",
-       " classSim                                     0.91916\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 18, dtype: object,\n",
-       " Unnamed: 0                                     19\n",
-       " id              Q1133024-P279-Q1000660-6d8a1a17-0\n",
-       " node1                                    Q1133024\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.556017\n",
-       " classSim                                  0.96031\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 19, dtype: object,\n",
-       " Unnamed: 0                                     20\n",
-       " id              Q1231309-P279-Q1000660-656bad41-0\n",
-       " node1                                    Q1231309\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.473025\n",
-       " classSim                                 0.987153\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 20, dtype: object,\n",
-       " Unnamed: 0                                     21\n",
-       " id              Q1892554-P279-Q1000660-b60addd4-0\n",
-       " node1                                    Q1892554\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.710084\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 21, dtype: object,\n",
-       " Unnamed: 0                                     22\n",
-       " id              Q2835946-P279-Q1000660-ff0a5cf9-0\n",
-       " node1                                    Q2835946\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                              0.69158\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 22, dtype: object,\n",
-       " Unnamed: 0                                     23\n",
-       " id              Q3413373-P279-Q1000660-9d565f5e-0\n",
-       " node1                                    Q3413373\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.676028\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 23, dtype: object,\n",
-       " Unnamed: 0                                     24\n",
-       " id              Q5611316-P279-Q1000660-6b051008-0\n",
-       " node1                                    Q5611316\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.652025\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 24, dtype: object,\n",
-       " Unnamed: 0                                    25\n",
-       " id              Q649977-P279-Q1000660-cc4475c9-0\n",
-       " node1                                    Q649977\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.548393\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 25, dtype: object,\n",
-       " Unnamed: 0                                     26\n",
-       " id              Q7269432-P279-Q1000660-8976e0ff-0\n",
-       " node1                                    Q7269432\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.439381\n",
-       " classSim                                 0.989483\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 26, dtype: object,\n",
-       " Unnamed: 0                                    27\n",
-       " id              Q844980-P279-Q1000660-5f020e98-0\n",
-       " node1                                    Q844980\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.678084\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 27, dtype: object,\n",
-       " Unnamed: 0                                     28\n",
-       " id              Q1051606-P279-Q1000809-995e5221-0\n",
-       " node1                                    Q1051606\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.740599\n",
-       " classSim                                 0.912943\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 28, dtype: object,\n",
-       " Unnamed: 0                                     29\n",
-       " id              Q4984171-P279-Q1000809-95749f41-0\n",
-       " node1                                    Q4984171\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.808338\n",
-       " classSim                                 0.957835\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 29, dtype: object,\n",
-       " Unnamed: 0                                     30\n",
-       " id              Q2047562-P279-Q1001041-45feee81-0\n",
-       " node1                                    Q2047562\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.359032\n",
-       " classSim                                 0.707243\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 30, dtype: object,\n",
-       " Unnamed: 0                                     31\n",
-       " id              Q8069073-P279-Q1001041-3f13f5bb-0\n",
-       " node1                                    Q8069073\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.769952\n",
-       " classSim                                 0.849692\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 31, dtype: object,\n",
-       " Unnamed: 0                                     32\n",
-       " id              Q1070955-P279-Q1001051-5df03dac-0\n",
-       " node1                                    Q1070955\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.800508\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 32, dtype: object,\n",
-       " Unnamed: 0                                     33\n",
-       " id              Q1153367-P279-Q1001051-34783d55-0\n",
-       " node1                                    Q1153367\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.837581\n",
-       " classSim                                 0.876525\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 33, dtype: object,\n",
-       " Unnamed: 0                                     34\n",
-       " id              Q1958065-P279-Q1001051-d6eb5968-0\n",
-       " node1                                    Q1958065\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.848892\n",
-       " classSim                                 0.707989\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 34, dtype: object,\n",
-       " Unnamed: 0                                    35\n",
-       " id              Q209680-P279-Q1001051-072eeb1b-0\n",
-       " node1                                    Q209680\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.656872\n",
-       " classSim                                0.724838\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 35, dtype: object,\n",
-       " Unnamed: 0                                     36\n",
-       " id              Q2986204-P279-Q1001051-7b21695a-0\n",
-       " node1                                    Q2986204\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.816212\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 36, dtype: object,\n",
-       " Unnamed: 0                                    37\n",
-       " id              Q385530-P279-Q1001051-17db3704-0\n",
-       " node1                                    Q385530\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.75061\n",
-       " classSim                                0.780191\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 37, dtype: object,\n",
-       " Unnamed: 0                                      38\n",
-       " id              Q41795401-P279-Q1001051-5747dcc4-0\n",
-       " node1                                    Q41795401\n",
-       " label                                         P279\n",
-       " node2                                     Q1001051\n",
-       " bert2SentSim                              0.787191\n",
-       " classSim                                   0.88381\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 38, dtype: object,\n",
-       " Unnamed: 0                                    39\n",
-       " id              Q612405-P279-Q1001051-bb6fa17c-0\n",
-       " node1                                    Q612405\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.72366\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 39, dtype: object,\n",
-       " Unnamed: 0                                    40\n",
-       " id              Q668439-P279-Q1001051-bbba9ea3-0\n",
-       " node1                                    Q668439\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.698926\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 40, dtype: object,\n",
-       " Unnamed: 0                                     41\n",
-       " id              Q7133779-P279-Q1001051-4fc71627-0\n",
-       " node1                                    Q7133779\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.576617\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 41, dtype: object,\n",
-       " Unnamed: 0                                     42\n",
-       " id              Q7630946-P279-Q1001051-2e78f5b5-0\n",
-       " node1                                    Q7630946\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.632298\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 42, dtype: object,\n",
-       " Unnamed: 0                                     43\n",
-       " id              Q1056352-P279-Q1001059-c1976180-0\n",
-       " node1                                    Q1056352\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.717131\n",
-       " classSim                                 0.886053\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 43, dtype: object,\n",
-       " Unnamed: 0                                     44\n",
-       " id              Q2123051-P279-Q1001059-18b4564b-0\n",
-       " node1                                    Q2123051\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.606046\n",
-       " classSim                                 0.465094\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 44, dtype: object,\n",
-       " Unnamed: 0                                    45\n",
-       " id              Q444278-P279-Q1001059-0c74af28-0\n",
-       " node1                                    Q444278\n",
-       " label                                       P279\n",
-       " node2                                   Q1001059\n",
-       " bert2SentSim                            0.481163\n",
-       " classSim                                0.881922\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 45, dtype: object,\n",
-       " Unnamed: 0                                     46\n",
-       " id              Q7240696-P279-Q1001059-31dfa177-0\n",
-       " node1                                    Q7240696\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.752948\n",
-       " classSim                                 0.881922\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 46, dtype: object,\n",
-       " Unnamed: 0                                      47\n",
-       " id              Q85776190-P279-Q1001059-b7a4aad6-0\n",
-       " node1                                    Q85776190\n",
-       " label                                         P279\n",
-       " node2                                     Q1001059\n",
-       " bert2SentSim                              0.747289\n",
-       " classSim                                   0.53963\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 47, dtype: object,\n",
-       " Unnamed: 0                                         48\n",
-       " id              Q100052008-P279-Q100116222-d1597eca-0\n",
-       " node1                                      Q100052008\n",
-       " label                                            P279\n",
-       " node2                                      Q100116222\n",
-       " bert2SentSim                                 0.725062\n",
-       " classSim                                     0.909364\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 48, dtype: object,\n",
-       " Unnamed: 0                                        49\n",
-       " id              Q27303706-P279-Q100116222-526530c6-0\n",
-       " node1                                      Q27303706\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.537653\n",
-       " classSim                                    0.917887\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 49, dtype: object,\n",
-       " Unnamed: 0                                        50\n",
-       " id              Q99565932-P279-Q100116222-139f958c-0\n",
-       " node1                                      Q99565932\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.691907\n",
-       " classSim                                    0.892978\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 50, dtype: object,\n",
-       " Unnamed: 0                                     51\n",
-       " id              Q1416523-P279-Q1001329-2b6d0c1e-0\n",
-       " node1                                    Q1416523\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.160931\n",
-       " classSim                                 0.871646\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 51, dtype: object,\n",
-       " Unnamed: 0                                     52\n",
-       " id              Q2963918-P279-Q1001329-c85a352f-0\n",
-       " node1                                    Q2963918\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.469497\n",
-       " classSim                                 0.878703\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 52, dtype: object,\n",
-       " Unnamed: 0                                      53\n",
-       " id              Q56756850-P279-Q1001329-6cb334b1-0\n",
-       " node1                                    Q56756850\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.412103\n",
-       " classSim                                  0.363595\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 53, dtype: object,\n",
-       " Unnamed: 0                                      54\n",
-       " id              Q60782200-P279-Q1001329-0b4da903-0\n",
-       " node1                                    Q60782200\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.239958\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 54, dtype: object,\n",
-       " Unnamed: 0                                      55\n",
-       " id              Q78841106-P279-Q1001329-d474c822-0\n",
-       " node1                                    Q78841106\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.496099\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 55, dtype: object,\n",
-       " Unnamed: 0                                    56\n",
-       " id              Q798863-P279-Q1001329-c284c2ec-0\n",
-       " node1                                    Q798863\n",
-       " label                                       P279\n",
-       " node2                                   Q1001329\n",
-       " bert2SentSim                            0.597623\n",
-       " classSim                                0.871646\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 56, dtype: object,\n",
-       " Unnamed: 0                                      57\n",
-       " id              Q96337569-P279-Q1001329-b8b30fb9-0\n",
-       " node1                                    Q96337569\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.210435\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 57, dtype: object,\n",
-       " Unnamed: 0                                       58\n",
-       " id              Q3403683-P279-Q100135295-fd0a0a92-0\n",
-       " node1                                      Q3403683\n",
-       " label                                          P279\n",
-       " node2                                    Q100135295\n",
-       " bert2SentSim                               0.371629\n",
-       " classSim                                   0.811291\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 58, dtype: object,\n",
-       " Unnamed: 0                                         59\n",
-       " id              Q100095237-P279-Q100136951-a7f6f753-0\n",
-       " node1                                      Q100095237\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.909817\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 59, dtype: object,\n",
-       " Unnamed: 0                                         60\n",
-       " id              Q100136066-P279-Q100136951-1e2368cd-0\n",
-       " node1                                      Q100136066\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.843155\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 60, dtype: object,\n",
-       " Unnamed: 0                                      61\n",
-       " id              Q235041-P279-Q100136951-07d30679-0\n",
-       " node1                                      Q235041\n",
-       " label                                         P279\n",
-       " node2                                   Q100136951\n",
-       " bert2SentSim                              0.607752\n",
-       " classSim                                  0.719618\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 61, dtype: object,\n",
-       " Unnamed: 0                                      62\n",
-       " id              Q22006653-P279-Q1001378-98eddb62-0\n",
-       " node1                                    Q22006653\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.366579\n",
-       " classSim                                    0.5263\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 62, dtype: object,\n",
-       " Unnamed: 0                                     63\n",
-       " id              Q2895476-P279-Q1001378-9d15228e-0\n",
-       " node1                                    Q2895476\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.682378\n",
-       " classSim                                  0.75066\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 63, dtype: object,\n",
-       " Unnamed: 0                                     64\n",
-       " id              Q2918907-P279-Q1001378-39c09697-0\n",
-       " node1                                    Q2918907\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.813292\n",
-       " classSim                                 0.775328\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 64, dtype: object,\n",
-       " Unnamed: 0                                     65\n",
-       " id              Q3516833-P279-Q1001378-30384a7c-0\n",
-       " node1                                    Q3516833\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.684669\n",
-       " classSim                                 0.916831\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 65, dtype: object,\n",
-       " Unnamed: 0                                     66\n",
-       " id              Q4129855-P279-Q1001378-3ad464f7-0\n",
-       " node1                                    Q4129855\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.740775\n",
-       " classSim                                 0.905545\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 66, dtype: object,\n",
-       " Unnamed: 0                                     67\n",
-       " id              Q5197004-P279-Q1001378-513f9939-0\n",
-       " node1                                    Q5197004\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.642596\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 67, dtype: object,\n",
-       " Unnamed: 0                                     68\n",
-       " id              Q7237336-P279-Q1001378-55e73e1a-0\n",
-       " node1                                    Q7237336\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                              0.58928\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 68, dtype: object,\n",
-       " Unnamed: 0                                    69\n",
-       " id              Q738160-P279-Q1001378-b69af0cb-0\n",
-       " node1                                    Q738160\n",
-       " label                                       P279\n",
-       " node2                                   Q1001378\n",
-       " bert2SentSim                            0.813292\n",
-       " classSim                                0.892815\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 69, dtype: object,\n",
-       " Unnamed: 0                                  70\n",
-       " id              Q8317-P279-Q1001378-a56c669f-0\n",
-       " node1                                    Q8317\n",
-       " label                                     P279\n",
-       " node2                                 Q1001378\n",
-       " bert2SentSim                          0.636886\n",
-       " classSim                              0.541961\n",
-       " Resp_code                     <Response [200]>\n",
-       " Name: 70, dtype: object,\n",
-       " Unnamed: 0                                      71\n",
-       " id              Q86908314-P279-Q1001378-7a81e91b-0\n",
-       " node1                                    Q86908314\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.421366\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 71, dtype: object,\n",
-       " Unnamed: 0                                      72\n",
-       " id              Q86910224-P279-Q1001378-b427331c-0\n",
-       " node1                                    Q86910224\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.584772\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 72, dtype: object,\n",
-       " Unnamed: 0                                         73\n",
-       " id              Q100148346-P279-Q100148352-003654ac-0\n",
-       " node1                                      Q100148346\n",
-       " label                                            P279\n",
-       " node2                                      Q100148352\n",
-       " bert2SentSim                                 0.872339\n",
-       " classSim                                     0.951283\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 73, dtype: object,\n",
-       " Unnamed: 0                                        74\n",
-       " id              Q27147769-P279-Q100148352-8254b7c4-0\n",
-       " node1                                      Q27147769\n",
-       " label                                           P279\n",
-       " node2                                     Q100148352\n",
-       " bert2SentSim                                0.886007\n",
-       " classSim                                    0.924902\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 74, dtype: object,\n",
-       " Unnamed: 0                                     75\n",
-       " id              Q14747611-P279-Q100159-567e45a7-0\n",
-       " node1                                   Q14747611\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                              0.50114\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 75, dtype: object,\n",
-       " Unnamed: 0                                     76\n",
-       " id              Q17089549-P279-Q100159-3247e0d4-0\n",
-       " node1                                   Q17089549\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.705335\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 76, dtype: object,\n",
-       " Unnamed: 0                                     77\n",
-       " id              Q18923049-P279-Q100159-04db261f-0\n",
-       " node1                                   Q18923049\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.861815\n",
-       " classSim                                 0.719858\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 77, dtype: object,\n",
-       " Unnamed: 0                                    78\n",
-       " id              Q2056084-P279-Q100159-94d1b7b8-0\n",
-       " node1                                   Q2056084\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.782052\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 78, dtype: object,\n",
-       " Unnamed: 0                                    79\n",
-       " id              Q5097845-P279-Q100159-c2bd0b24-0\n",
-       " node1                                   Q5097845\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.551256\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 79, dtype: object,\n",
-       " Unnamed: 0                                    80\n",
-       " id              Q6061403-P279-Q100159-d5101564-0\n",
-       " node1                                   Q6061403\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.565718\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 80, dtype: object,\n",
-       " Unnamed: 0                                   81\n",
-       " id              Q652653-P279-Q100159-bd6c5d3a-0\n",
-       " node1                                   Q652653\n",
-       " label                                      P279\n",
-       " node2                                   Q100159\n",
-       " bert2SentSim                           0.501037\n",
-       " classSim                               0.837687\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 81, dtype: object,\n",
-       " Unnamed: 0                                     82\n",
-       " id              Q66293226-P279-Q100159-ad4b8016-0\n",
-       " node1                                   Q66293226\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                               0.8089\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 82, dtype: object,\n",
-       " Unnamed: 0                                         83\n",
-       " id              Q100166380-P279-Q100166391-4684ea65-0\n",
-       " node1                                      Q100166380\n",
-       " label                                            P279\n",
-       " node2                                      Q100166391\n",
-       " bert2SentSim                                 0.752609\n",
-       " classSim                                     0.799499\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 83, dtype: object,\n",
-       " Unnamed: 0                                      84\n",
-       " id              Q244326-P279-Q100166391-35359099-0\n",
-       " node1                                      Q244326\n",
-       " label                                         P279\n",
-       " node2                                   Q100166391\n",
-       " bert2SentSim                              0.522545\n",
-       " classSim                                  0.470975\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 84, dtype: object,\n",
-       " Unnamed: 0                                     85\n",
-       " id              Q40551-P279-Q100166391-7242baf9-0\n",
-       " node1                                      Q40551\n",
-       " label                                        P279\n",
-       " node2                                  Q100166391\n",
-       " bert2SentSim                              0.74986\n",
-       " classSim                                 0.781795\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 85, dtype: object,\n",
-       " Unnamed: 0                                         86\n",
-       " id              Q102108504-P279-Q100171002-7ccf3934-0\n",
-       " node1                                      Q102108504\n",
-       " label                                            P279\n",
-       " node2                                      Q100171002\n",
-       " bert2SentSim                                 0.680867\n",
-       " classSim                                         None\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 86, dtype: object,\n",
-       " Unnamed: 0                                       87\n",
-       " id              Q1150647-P279-Q100171002-fb1cad26-0\n",
-       " node1                                      Q1150647\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.664551\n",
-       " classSim                                   0.795416\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 87, dtype: object,\n",
-       " Unnamed: 0                                       88\n",
-       " id              Q1503443-P279-Q100171002-8282ae74-0\n",
-       " node1                                      Q1503443\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.748802\n",
-       " classSim                                   0.822611\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 88, dtype: object,\n",
-       " Unnamed: 0                                       89\n",
-       " id              Q2616305-P279-Q100171002-49dc8346-0\n",
-       " node1                                      Q2616305\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.646817\n",
-       " classSim                                   0.887266\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 89, dtype: object,\n",
-       " Unnamed: 0                                        90\n",
-       " id              Q24050179-P279-Q100195948-90966900-0\n",
-       " node1                                      Q24050179\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.652214\n",
-       " classSim                                    0.698738\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 90, dtype: object,\n",
-       " Unnamed: 0                                        91\n",
-       " id              Q29479539-P279-Q100195948-e222789a-0\n",
-       " node1                                      Q29479539\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.689148\n",
-       " classSim                                    0.681145\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 91, dtype: object,\n",
-       " Unnamed: 0                                      92\n",
-       " id              Q308859-P279-Q100195948-a5841742-0\n",
-       " node1                                      Q308859\n",
-       " label                                         P279\n",
-       " node2                                   Q100195948\n",
-       " bert2SentSim                              0.750328\n",
-       " classSim                                  0.279686\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 92, dtype: object,\n",
-       " Unnamed: 0                                         93\n",
-       " id              Q100251305-P279-Q100251275-02ddd08e-0\n",
-       " node1                                      Q100251305\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.486286\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 93, dtype: object,\n",
-       " Unnamed: 0                                         94\n",
-       " id              Q100251705-P279-Q100251275-fa0c89c7-0\n",
-       " node1                                      Q100251705\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.370606\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 94, dtype: object,\n",
-       " Unnamed: 0                                        95\n",
-       " id              Q16007968-P279-Q100251275-318d5598-0\n",
-       " node1                                      Q16007968\n",
-       " label                                           P279\n",
-       " node2                                     Q100251275\n",
-       " bert2SentSim                                0.478886\n",
-       " classSim                                      0.9303\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 95, dtype: object,\n",
-       " Unnamed: 0                                       96\n",
-       " id              Q3310017-P279-Q100251275-772909ea-0\n",
-       " node1                                      Q3310017\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                                0.57202\n",
-       " classSim                                   0.929488\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 96, dtype: object,\n",
-       " Unnamed: 0                                       97\n",
-       " id              Q4855428-P279-Q100251275-fbf2100a-0\n",
-       " node1                                      Q4855428\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                               0.545835\n",
-       " classSim                                   0.931676\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 97, dtype: object,\n",
-       " Unnamed: 0                                      98\n",
-       " id              Q862086-P279-Q100251275-faf57342-0\n",
-       " node1                                      Q862086\n",
-       " label                                         P279\n",
-       " node2                                   Q100251275\n",
-       " bert2SentSim                              0.630551\n",
-       " classSim                                  0.948207\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 98, dtype: object,\n",
-       " Unnamed: 0                                        99\n",
-       " id              Q15209696-P279-Q100257358-c4b60f40-0\n",
-       " node1                                      Q15209696\n",
-       " label                                           P279\n",
-       " node2                                     Q100257358\n",
-       " bert2SentSim                                0.484455\n",
-       " classSim                                    0.610338\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 99, dtype: object]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "rental-stationery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "flexible-packaging",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "emerging-music",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nervous-balloon",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "under-lloyd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "diagnostic-shade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_3M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "first-canal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_10M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "removable-watershed",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confident-detail",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "important-palace",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "charitable-jefferson",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "satisfactory-union",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integrated-intention",
-   "metadata": {},
-   "source": [
-    "# Wiki-CS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "functioning-scholarship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unique-weekend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs-20200504.tsv',sep='\\t')\n",
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])\n",
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')\n",
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "located-swaziland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sharing-scientist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]\n",
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fewer-place",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})\n",
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label']].to_csv('../data/wikidata-cs_categorized.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "noted-hopkins",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "enhanced-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs_categorized.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "complex-character",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "exciting-anderson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I_set_size = len(wikiDF[wikiDF.category == 'I'])\n",
-    "M_sampled_set = wikiDF[wikiDF.category == 'M'].sample(I_set_size, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "thrown-graham",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import combinations\n",
-    "\n",
-    "def find_relation_mapping(mainset, subset, needed_size):\n",
-    "    nodes = set([row['word1_kg_id'] for _,row in subset.iterrows()] \n",
-    "            + [row['word2_kg_id'] for _,row in subset.iterrows()])\n",
-    "    node_label_mappings = {row['word1_kg_id']: row['node1;label'] for _, row in subset.iterrows()}.update(\n",
-    "                                {row['word2_kg_id']: row['node2;label'] for _, row in subset.iterrows()}\n",
-    "                        )\n",
-    "    mainset_pairs = set([(row['word1_kg_id'], row['word2_kg_id']) for _,row in mainset.iterrows()])\n",
-    "\n",
-    "    all_pairs = set([pair for pair in list(combinations(list(nodes), 2))[10*needed_size]])\n",
-    "    \n",
-    "    removed_pairs = set()\n",
-    "    \n",
-    "    first_line = True\n",
-    "    with gzip.open('../data/wikidata-20210215-dwd/claims.tsv.gz', 'r') as claims_file:\n",
-    "        for line in tqdm(claims_file,total=98482585):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            curr_pair = (line[1], line[3])\n",
-    "            if curr_pair in all_pairs:\n",
-    "                removed_pairs.add(curr_pair)\n",
-    "    newDF = []\n",
-    "    for pair in all_pairs - removed_pairs - mainset_pairs:\n",
-    "        try:\n",
-    "            if pair[0] is None or pair[1] is None:\n",
-    "                continue\n",
-    "            newDF.append(pair[0], pair[1], 'U', node_label_mappings[pair[0]], node_label_mappings[pair[1]])\n",
-    "        except:\n",
-    "            print(pair)\n",
-    "    return pd.DataFrame(newDF, columns=subset.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "immune-jesus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3df524d9d4fd47e6864fe57299e1f8e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/98482585 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q1070669\n",
-      "Q20755059\n",
-      "CPU times: user 13min 20s, sys: 1min 24s, total: 14min 44s\n",
-      "Wall time: 14min 40s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "U_sampled_set = find_relation_mapping(wikiDF, wikiDF[wikiDF.category == 'M'], I_set_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "cordless-commerce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(U_sampled_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mental-terrace",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "friendly-murder",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "complicated-acceptance",
-   "metadata": {},
-   "source": [
-    "# Check Diff in versions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "southeast-desperate",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "275927\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "def get_all_nodes():\n",
-    "    p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "    wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "    concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eligible-wireless",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "files_set_1 = ['../data/wikidata-20210215-dwd/descriptions.en.tsv.gz',\n",
-    "              '../data/wikidata-20210215-dwd/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "files_set_2 = [DESCRIPTIONS_FILE,\n",
-    "              '../../wd-correctness/gdrive-kgtk-dump-2020-12-07/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "\n",
-    "all_nodes_set = set(allNodes)\n",
-    "for file1, file2 in zip(files_set_1, files_set_2):\n",
-    "    file1_dict, file2_dict = {}, {}\n",
-    "    with gzip.open(file1) as file1:\n",
-    "        for line in tqdm(file1):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file1_dict[line[1]] = line[3][1:-4]\n",
-    "    with gzip.open(file2) as file2:\n",
-    "        for line in tqdm(file2):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file2_dict[line[1]] = line[3][1:-4]\n",
-    "    for elem in file1_dict.keys():\n",
-    "        try:\n",
-    "            if file1_dict[elem] != file2_dict[elem]:\n",
-    "                print(elem, file1_dict[elem], file2_dict[elem])\n",
-    "        except:\n",
-    "            continue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "enormous-client",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "twelve-possible",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "joint-cuisine",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "played-repeat",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "278px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Archived/Retrofitting using 19k + 19k dataset REEXEC.ipynb b/Archived/Retrofitting using 19k + 19k dataset REEXEC.ipynb
deleted file mode 100644
index 30bb697..0000000
--- a/Archived/Retrofitting using 19k + 19k dataset REEXEC.ipynb	
+++ /dev/null
@@ -1,14914 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "strange-saturn",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "random-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "incorporated-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'id', 'node1_label', 'label', 'node2_label', 'sent',\n",
-       "       'node1_emb', 'node2_emb', 'bert2SentSim', 'abstract',\n",
-       "       'abstract_firstSent', 'BERT_abstract_emb',\n",
-       "       'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'textEmb_7props_left',\n",
-       "       'textEmb_7props_right', 'textEmb_2props_left', 'textEmb_2props_right',\n",
-       "       'textEmb_7props_cosSim', 'textEmb_2props_cosSim',\n",
-       "       'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.772184\n",
-       "std          0.122326\n",
-       "min          0.120480\n",
-       "25%          0.684141\n",
-       "50%          0.793718\n",
-       "75%          0.874284\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "under-rabbit",
-   "metadata": {},
-   "source": [
-    "# Embeddings Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "annoying-intake",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Complex, Transe Embeddings Datasets generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "tamil-activity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "immune-nashville",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "residential-cleaners",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "current-great",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id  \n",
-       "0        Q454  \n",
-       "1   Q13648784  \n",
-       "2     Q178022  \n",
-       "3   Q21004260  \n",
-       "4      Q49112  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "valued-progressive",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# wordSimSet = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "different-vancouver",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list() + p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list() + wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-lewis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19166"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "collected-regular",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1499717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4765290\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16000518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6423382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2177259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q100448831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2857578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13577338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q96158854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7892\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7675683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2380954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21083881\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q211521\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16023742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q97627995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16721350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q32979618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4553362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q98232491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66194218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5372\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25481995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61466331\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6983403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84105477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30107768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56753514\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91927989\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12047900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11698973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q517386\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17097928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19838691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q101541623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17376918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23013268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66363580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55693905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68131879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85803433\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21406831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29957548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78710574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6462051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1042920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7925\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28324850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2849391\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "elect-gothic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(compEmbeddings.items(), columns=['node', 'complex_embedding']).to_csv('../data/wordsim353_complex_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "combined-theme",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(transeEmbeddings.items(), columns=['node', 'transe_embedding']).to_csv('../data/wordsim353_transe_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-extraction",
-   "metadata": {},
-   "source": [
-    "# Target Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hairy-tuition",
-   "metadata": {},
-   "source": [
-    "## Probase Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "opened-quarter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = pd.read_csv('../data/probase/probase_WQnodes_subset_and_sim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "major-peter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1_qnode</th>\n",
-       "      <th>node2_qnode</th>\n",
-       "      <th>n1_final_qnode</th>\n",
-       "      <th>n2_final_qnode</th>\n",
-       "      <th>sim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>gender</td>\n",
-       "      <td>14230</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>0.913568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>temperature</td>\n",
-       "      <td>13660</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>P2076</td>\n",
-       "      <td>0.909663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>7</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>copper</td>\n",
-       "      <td>11142</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>Q15830500</td>\n",
-       "      <td>0.890199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>variable</td>\n",
-       "      <td>age</td>\n",
-       "      <td>9375</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q10954303</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>0.873703</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0 node1_label  node2_label  no_of_relations node1_qnode  \\\n",
-       "0           0      factor          age            35167         NaN   \n",
-       "1           5      factor       gender            14230         NaN   \n",
-       "2           6      factor  temperature            13660         NaN   \n",
-       "3           7       metal       copper            11142      Q11426   \n",
-       "4           9    variable          age             9375         NaN   \n",
-       "\n",
-       "  node2_qnode n1_final_qnode n2_final_qnode       sim  \n",
-       "0         NaN      Q15061738     Q100343219  1.000000  \n",
-       "1      Q48277      Q15061738         Q48277  0.913568  \n",
-       "2         NaN      Q15061738          P2076  0.909663  \n",
-       "3         NaN         Q11426      Q15830500  0.890199  \n",
-       "4         NaN      Q10954303     Q100343219  0.873703  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "photographic-character",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes1_subset.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'bert2SentSim'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "russian-proceeding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'] = 0.5 + 0.5 * probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "painted-selection",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.600841e+06\n",
-       "mean     5.429549e-01\n",
-       "std      5.363352e-02\n",
-       "min      5.000000e-01\n",
-       "25%      5.000000e-01\n",
-       "50%      5.331083e-01\n",
-       "75%      5.662167e-01\n",
-       "max      1.000000e+00\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "consolidated-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "irish-disposal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "            sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-milan",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
-   "source": [
-    "## Sample attempt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "substantial-aggregate",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text_sample = p279WordSimSeededDF_wabs_text[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "analyzed-passion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "japanese-defense",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dying-nigeria",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "reduced-avenue",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1034"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "advised-hollywood",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "authentic-greene",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1034.000000\n",
-       "mean        0.281990\n",
-       "std         0.054759\n",
-       "min         0.069792\n",
-       "25%         0.244698\n",
-       "50%         0.278019\n",
-       "75%         0.319656\n",
-       "max         0.450444\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "variable-transportation",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
-   "source": [
-    "## ChildPar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "central-spyware",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "white-genesis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "registered-original",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "experienced-conspiracy",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "every-interface",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "specified-berry",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.283712\n",
-       "std          0.056573\n",
-       "min          0.050719\n",
-       "25%          0.246137\n",
-       "50%          0.281897\n",
-       "75%          0.319687\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incoming-calvin",
-   "metadata": {},
-   "source": [
-    "# Correlation Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "answering-exhaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "    print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "    print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "    print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    return wordSimMissingSet, cm_old, cm_new"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "premier-crisis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "confusionMatrixMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scientific-heading",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "stretch-elizabeth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "sapphire-bulgarian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "rising-lawyer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "closed-charles",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.271898\n",
-      "std          0.057984\n",
-      "min          0.000000\n",
-      "25%          0.231530\n",
-      "50%          0.268294\n",
-      "75%          0.308696\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3876948460943732, pvalue=2.944054929116869e-23)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41910186320873755, pvalue=2.2966918648680457e-29)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5153374233128835\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.193850\n",
-      "std          0.113418\n",
-      "min          0.000000\n",
-      "25%          0.086588\n",
-      "50%          0.192671\n",
-      "75%          0.291258\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3896871945725784, pvalue=1.7567320499364868e-23)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.429300613416863, pvalue=9.889526081071043e-31)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5920245398773006\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.325848\n",
-      "std          0.071942\n",
-      "min          0.000000\n",
-      "25%          0.277981\n",
-      "50%          0.321763\n",
-      "75%          0.370070\n",
-      "max          0.648638\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3975352135008474, pvalue=2.274798622604064e-24)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.380602867202403, pvalue=1.7935601808141945e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4253385100650523, pvalue=3.397679519805734e-30)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3995893685604078, pvalue=7.608607437786723e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.5950920245398773\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.39570552147239263\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_7props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['text_7props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_7props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accessory-isolation",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings - 2 props | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "bored-sweden",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "speaking-sharing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "addressed-march",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "latter-spelling",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.270895\n",
-      "std          0.059990\n",
-      "min          0.000000\n",
-      "25%          0.228547\n",
-      "50%          0.266816\n",
-      "75%          0.309132\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3793643502923977, pvalue=2.45298661169592e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40586371991954756, pvalue=1.2142237724855381e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.49079754601226994\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.194648\n",
-      "std          0.115900\n",
-      "min          0.000000\n",
-      "25%          0.083470\n",
-      "50%          0.191821\n",
-      "75%          0.294976\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.36958425592956784, pvalue=2.7849781516020684e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39348934596160956, pvalue=4.414924758703321e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5766871165644172\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.323673\n",
-      "std          0.069955\n",
-      "min          0.000000\n",
-      "25%          0.277272\n",
-      "50%          0.319275\n",
-      "75%          0.365774\n",
-      "max          0.677490\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3746327832763941, pvalue=8.004468522493588e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39398189437020814, pvalue=3.8343633718362455e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.3834355828220859\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_2props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_2props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['text_2props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_2props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "promising-estonia",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "flexible-creature",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "sustainable-nurse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "amino-there",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "yellow-estate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "necessary-house",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316720\n",
-      "std          0.076381\n",
-      "min          0.000000\n",
-      "25%          0.261478\n",
-      "50%          0.313781\n",
-      "75%          0.370851\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3168500395328192, pvalue=7.486323665386832e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27202499209162273, pvalue=6.770258859650813e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.232133\n",
-      "std          0.142269\n",
-      "min          0.000000\n",
-      "25%          0.089938\n",
-      "50%          0.228553\n",
-      "75%          0.361251\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3361212489826738, pvalue=9.03613034020374e-19)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.29248433284517855, pvalue=7.890529591252537e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6618075801749271\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.375082\n",
-      "std          0.073982\n",
-      "min          0.000000\n",
-      "25%          0.330119\n",
-      "50%          0.377307\n",
-      "75%          0.421860\n",
-      "max          0.841769\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.26508277549218445, pvalue=3.0194195465523546e-12)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21338136285025483, pvalue=4.17252744630606e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5830903790087464\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['complex_19k_old_'+str(weightCase)], confusionMatrixMaster['complex_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "racial-tutorial",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "dominant-youth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "complicated-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "interstate-worst",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "color-newspaper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "sealed-undergraduate",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327802\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23996930346932127, pvalue=2.6942170209534906e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21449104599327523, pvalue=3.4760310637715348e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237343\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24439786532398405, pvalue=1.2600161404554345e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22590625729631375, pvalue=4.917533132159808e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.394277\n",
-      "std          0.086197\n",
-      "min          0.000000\n",
-      "25%          0.339162\n",
-      "50%          0.397921\n",
-      "75%          0.452267\n",
-      "max          0.850905\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24274131207452157, pvalue=1.6736422306859635e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.201734473314897, pvalue=2.7559659468673134e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amber-priority",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "coastal-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "familiar-springer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "duplicate-storm",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "unsigned-hungarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "mental-coaching",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.887688e-02\n",
-      "std      1.377091e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.314421e-01\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31031745482774675, pvalue=3.150145092040765e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.26161279947706156, pvalue=5.774092784287413e-13)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6268221574344023\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.252887e-02\n",
-      "std      8.773144e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.094678e-03\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.35020973149202944, pvalue=3.059720074110226e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3013432304449195, pvalue=1.0451590470362559e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6588921282798834\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.251606e-01\n",
-      "std      2.222320e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.940167e-01\n",
-      "max      8.900593e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.14149430916593933, pvalue=0.00019608082976567604)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14687524939411561, pvalue=5.22383486460822e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.1457725947521866\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sticky-light",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "normal-house",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "reserved-sport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "norman-graph",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "identical-amsterdam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "knowing-register",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "inside-opportunity",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     8.215694e-02\n",
-      "std      1.439020e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.305726e-01\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22012582471779607, pvalue=6.9001945281841384e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.19941082640413085, pvalue=3.969975785916229e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6472303206997084\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.399453e-02\n",
-      "std      9.216743e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.230265e-03\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24783606125619878, pvalue=6.903890636652704e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22349921038500295, pvalue=7.473348989752392e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.302927e-01\n",
-      "std      2.320257e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.960976e-01\n",
-      "max      9.949517e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12855892543728606, pvalue=0.0007155142288469726)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.13749877727529278, pvalue=0.00015240104075019828)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.17784256559766765\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "listed-parent",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "sustained-benchmark",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "qualified-chase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "occupational-survival",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "intermediate-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "piano-delta",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.302721\n",
-      "std          0.067184\n",
-      "min          0.000000\n",
-      "25%          0.259615\n",
-      "50%          0.294771\n",
-      "75%          0.342928\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31582754528140355, pvalue=9.373921755437325e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27369250100948833, pvalue=4.756656623381545e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6326530612244898\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.178804\n",
-      "std          0.139703\n",
-      "min          0.000000\n",
-      "25%          0.054379\n",
-      "50%          0.137749\n",
-      "75%          0.307442\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3464370368444909, pvalue=7.656876025494246e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3011763909407297, pvalue=1.0831987277611373e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.402505\n",
-      "std          0.098028\n",
-      "min          0.000000\n",
-      "25%          0.339741\n",
-      "50%          0.393894\n",
-      "75%          0.452606\n",
-      "max          0.889403\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12191472957942284, pvalue=0.0013338871853105486)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14137053781971248, pvalue=9.875366583327447e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16034985422740525\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "square-outreach",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "powered-kidney",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "eligible-mercury",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "passing-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "catholic-flexibility",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "every-transmission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "floral-union",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316457\n",
-      "std          0.074755\n",
-      "min          0.000000\n",
-      "25%          0.265029\n",
-      "50%          0.307908\n",
-      "75%          0.361417\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22203711859153544, pvalue=5.1043215609543335e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.20512238050033801, pvalue=1.6083667850582075e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6443148688046647\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.186190\n",
-      "std          0.146220\n",
-      "min          0.000000\n",
-      "25%          0.056864\n",
-      "50%          0.144985\n",
-      "75%          0.307240\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2479273527617227, pvalue=6.816190052635217e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2263056114290315, pvalue=4.5887579264134097e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.597667638483965\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.421187\n",
-      "std          0.108116\n",
-      "min          0.000000\n",
-      "25%          0.350740\n",
-      "50%          0.416101\n",
-      "75%          0.480072\n",
-      "max          0.972091\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12731453008255086, pvalue=0.0008059106457544173)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1476648434866464, pvalue=4.7610094969077205e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.2594752186588921\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "organic-earth",
-   "metadata": {},
-   "source": [
-    "### Abstract Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "royal-update",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "straight-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "pursuant-compact",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "australian-legend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "advanced-pattern",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "funded-third",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "identical-aspect",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "specific-namibia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "affected-consolidation",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.3354244232177734 s\n",
-      "0.11755561828613281 s\n",
-      "0.11744570732116699 s\n",
-      "0.1175832748413086 s\n",
-      "0.11750054359436035 s\n",
-      "0.11730837821960449 s\n",
-      "0.11754989624023438 s\n",
-      "0.11766695976257324 s\n",
-      "0.11713695526123047 s\n",
-      "0.11739706993103027 s\n",
-      "0.12069988250732422 s\n",
-      "0.11744093894958496 s\n",
-      "0.11738061904907227 s\n",
-      "0.11722540855407715 s\n",
-      "0.11743450164794922 s\n",
-      "0.11770343780517578 s\n",
-      "0.11734700202941895 s\n",
-      "0.11752867698669434 s\n",
-      "0.11753010749816895 s\n",
-      "0.11727476119995117 s\n",
-      "0.11757302284240723 s\n",
-      "0.11744475364685059 s\n",
-      "0.1178896427154541 s\n",
-      "0.11759018898010254 s\n",
-      "0.11737680435180664 s\n",
-      "0.11777734756469727 s\n",
-      "0.11771225929260254 s\n",
-      "0.11792659759521484 s\n",
-      "0.1174314022064209 s\n",
-      "0.1175081729888916 s\n",
-      "0.11757636070251465 s\n",
-      "0.11760926246643066 s\n",
-      "0.11748123168945312 s\n",
-      "0.11753368377685547 s\n",
-      "0.11712884902954102 s\n",
-      "0.11767840385437012 s\n",
-      "0.11764121055603027 s\n",
-      "0.11763525009155273 s\n",
-      "0.11754012107849121 s\n",
-      "0.11885714530944824 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "fuzzy-attack",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "hawaiian-portal",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.336606\n",
-      "std          0.081060\n",
-      "min          0.000000\n",
-      "25%          0.278247\n",
-      "50%          0.326289\n",
-      "75%          0.388139\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3471431474655109, pvalue=5.660815639939061e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.35383380764979844, pvalue=1.6754686847593527e-22)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5697674418604651\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244583\n",
-      "std          0.150115\n",
-      "min          0.000000\n",
-      "25%          0.103316\n",
-      "50%          0.233314\n",
-      "75%          0.372639\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.34735894519691607, pvalue=5.347633678079643e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3462632061224712, pvalue=1.2793035570514484e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.400089\n",
-      "std          0.083229\n",
-      "min          0.000000\n",
-      "25%          0.344853\n",
-      "50%          0.397861\n",
-      "75%          0.451782\n",
-      "max          0.835283\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.32390174521065745, pvalue=1.3650315049340146e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3325159043509967, pvalue=4.65056862032923e-20)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.47093023255813954\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['abstract_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "central-valley",
-   "metadata": {},
-   "source": [
-    "### Abstract 1st Sentence Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "occupied-emission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "sexual-press",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "cognitive-import",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "irish-north",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "neural-communications",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "apart-church",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "monthly-enough",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "varying-plaza",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "changing-cycle",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11401677131652832 s\n",
-      "0.11957383155822754 s\n",
-      "0.09752821922302246 s\n",
-      "0.128997802734375 s\n",
-      "0.11990189552307129 s\n",
-      "0.11844491958618164 s\n",
-      "0.11752724647521973 s\n",
-      "0.11877274513244629 s\n",
-      "0.11719059944152832 s\n",
-      "0.11760425567626953 s\n",
-      "0.11708760261535645 s\n",
-      "0.09807181358337402 s\n",
-      "0.0916135311126709 s\n",
-      "0.09286952018737793 s\n",
-      "0.0907738208770752 s\n",
-      "0.0915842056274414 s\n",
-      "0.09347748756408691 s\n",
-      "0.08993768692016602 s\n",
-      "0.10148978233337402 s\n",
-      "0.11854410171508789 s\n",
-      "0.12027955055236816 s\n",
-      "0.13148927688598633 s\n",
-      "0.11908864974975586 s\n",
-      "0.13748478889465332 s\n",
-      "0.13097643852233887 s\n",
-      "0.11984562873840332 s\n",
-      "0.12159109115600586 s\n",
-      "0.12565040588378906 s\n",
-      "0.1192171573638916 s\n",
-      "0.14190435409545898 s\n",
-      "0.128098726272583 s\n",
-      "0.1173861026763916 s\n",
-      "0.11848211288452148 s\n",
-      "0.11741471290588379 s\n",
-      "0.11730766296386719 s\n",
-      "0.12353205680847168 s\n",
-      "0.11716032028198242 s\n",
-      "0.11889147758483887 s\n",
-      "0.11724376678466797 s\n",
-      "0.11714792251586914 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "documentary-midnight",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "sixth-paradise",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.339064\n",
-      "std          0.072734\n",
-      "min          0.000000\n",
-      "25%          0.287403\n",
-      "50%          0.334725\n",
-      "75%          0.388172\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3571490077264898, pvalue=4.7415066582386246e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3713906787854944, pvalue=1.253257701630852e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6017441860465116\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244476\n",
-      "std          0.145834\n",
-      "min          0.000000\n",
-      "25%          0.104152\n",
-      "50%          0.234678\n",
-      "75%          0.376765\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.37186923032141733, pvalue=1.0985961673989065e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.37051869405377086, pvalue=1.6123151472069758e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6540697674418605\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.404541\n",
-      "std          0.081848\n",
-      "min          0.000000\n",
-      "25%          0.351736\n",
-      "50%          0.401690\n",
-      "75%          0.453416\n",
-      "max          0.872588\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31762341611315487, pvalue=5.624902334302071e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.33907069116618244, pvalue=8.52432972070843e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.4941860465116279\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_firstSent_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_firstSent_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['abstract_firstSent_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_firstSent_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-cameroon",
-   "metadata": {},
-   "source": [
-    "### Combination of all old embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "smooth-playlist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', 'complex_probase', 'transe_probase', 'complex_probase_19k', 'transe_probase_19k', 'abstract_19k', 'abstract_firstSent_19k'])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "material-design",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k_1', 'text_7props_19k_2', 'text_7props_19k_0.5', 'text_2props_19k_1', 'text_2props_19k_2', 'text_2props_19k_0.5', 'complex_19k_1', 'complex_19k_2', 'complex_19k_0.5', 'transe_19k_1', 'transe_19k_2', 'transe_19k_0.5', 'complex_probase_1', 'complex_probase_2', 'complex_probase_0.5', 'transe_probase_1', 'transe_probase_2', 'transe_probase_0.5', 'complex_probase_19k_1', 'complex_probase_19k_2', 'complex_probase_19k_0.5', 'transe_probase_19k_1', 'transe_probase_19k_2', 'transe_probase_19k_0.5', 'abstract_19k_1', 'abstract_19k_2', 'abstract_19k_0.5', 'abstract_firstSent_19k_1', 'abstract_firstSent_19k_2', 'abstract_firstSent_19k_0.5'])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "verbal-frederick",
-   "metadata": {},
-   "source": [
-    "#### Compl + Transe + Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "removed-conditions",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['complex_19k', 'transe_19k', \n",
-    "            'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "indie-washington",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 968\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "caring-wellington",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 968:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "twelve-tribute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19101.0\n",
-       "mean       968.0\n",
-       "std          0.0\n",
-       "min        968.0\n",
-       "25%        968.0\n",
-       "50%        968.0\n",
-       "75%        968.0\n",
-       "max        968.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "animated-weight",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.336903\n",
-      "std          0.071873\n",
-      "min          0.000000\n",
-      "25%          0.284286\n",
-      "50%          0.332284\n",
-      "75%          0.389200\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31344285039654224, pvalue=1.587990989990702e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2983652058391759, pvalue=2.0692364624319403e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6676384839650146\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.245075\n",
-      "std          0.146398\n",
-      "min          0.000000\n",
-      "25%          0.099960\n",
-      "50%          0.239725\n",
-      "75%          0.380577\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3270255968193857, pvalue=7.482780885295614e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3144288703997577, pvalue=4.696112489737555e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6880466472303207\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.400303\n",
-      "std          0.071506\n",
-      "min          0.000000\n",
-      "25%          0.357059\n",
-      "50%          0.402058\n",
-      "75%          0.445388\n",
-      "max          0.813749\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2967991230842074, pvalue=5.639053240442108e-15)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2739615906247492, pvalue=4.489403776140874e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "suspected-footage",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.321205\n",
-      "std          0.063429\n",
-      "min          0.000000\n",
-      "25%          0.277493\n",
-      "50%          0.311720\n",
-      "75%          0.359268\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2891828519936888, pvalue=2.717366700252014e-14)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27713398557337643, pvalue=2.2907553462981174e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6501457725947521\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.188928\n",
-      "std          0.144828\n",
-      "min          0.000000\n",
-      "25%          0.059667\n",
-      "50%          0.147733\n",
-      "75%          0.327197\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.33562487778756755, pvalue=1.0136749729444057e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.31665315908408964, pvalue=2.7384643013776528e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.427234\n",
-      "std          0.094690\n",
-      "min          0.000000\n",
-      "25%          0.367149\n",
-      "50%          0.418971\n",
-      "75%          0.473102\n",
-      "max          0.895330\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.13050673469358315, pvalue=0.0005927381978235836)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1636905372538761, pvalue=6.524566396115546e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.22157434402332363\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k__v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "second-norway",
-   "metadata": {},
-   "source": [
-    "#### Text_7props + Text_2props + Complex + Transe + Abstract + Abstract_FirstSent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "guided-accounting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', \n",
-    "            'abstract_19k', 'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "creative-advice",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 3784\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "apart-storm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 3784:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "foreign-merchant",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19090.0\n",
-       "mean      3784.0\n",
-       "std          0.0\n",
-       "min       3784.0\n",
-       "25%       3784.0\n",
-       "50%       3784.0\n",
-       "75%       3784.0\n",
-       "max       3784.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "aquatic-harbor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.318414\n",
-      "std          0.060577\n",
-      "min          0.000000\n",
-      "25%          0.273273\n",
-      "50%          0.313881\n",
-      "75%          0.362231\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40347307747318084, pvalue=5.629213176377413e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40536224043013125, pvalue=1.6868252763103453e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6307692307692307\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.230697\n",
-      "std          0.135101\n",
-      "min          0.000000\n",
-      "25%          0.097307\n",
-      "50%          0.223935\n",
-      "75%          0.356438\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40442451164800897, pvalue=4.371701842100601e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.4101503518414275, pvalue=4.1055209935697305e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.378997\n",
-      "std          0.061137\n",
-      "min          0.000000\n",
-      "25%          0.342927\n",
-      "50%          0.379359\n",
-      "75%          0.416067\n",
-      "max          0.760023\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3828276431671376, pvalue=1.2015280268194575e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36987136293537864, pvalue=3.618695156207996e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5415384615384615\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "behavioral-brooks",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.304466\n",
-      "std          0.054097\n",
-      "min          0.000000\n",
-      "25%          0.266260\n",
-      "50%          0.295631\n",
-      "75%          0.337646\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3563958420194592, pvalue=7.705212357023742e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36941390801600477, pvalue=4.091096997249633e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6092307692307692\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.178573\n",
-      "std          0.134771\n",
-      "min          0.000000\n",
-      "25%          0.057617\n",
-      "50%          0.140175\n",
-      "75%          0.314839\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.4167472980442226, pvalue=1.5499840293424274e-26)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41238438242524034, pvalue=2.1128593107239733e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6553846153846153\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.405175\n",
-      "std          0.085240\n",
-      "min          0.000000\n",
-      "25%          0.352177\n",
-      "50%          0.394099\n",
-      "75%          0.443428\n",
-      "max          0.797620\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.1299009113611685, pvalue=0.0008902251998159792)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1761010833372823, pvalue=2.3561612238907513e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16923076923076924\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "quality-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ideal-pipeline",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "organized-student",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alternative-cloud",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings | 19k + 19k dataset unweighted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "id": "dangerous-carrier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "fossil-exemption",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "antique-purchase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "id": "automatic-monitor",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-192-4bfdddd84fe6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0membedDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetchEmbeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextEmb_7props_DF\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0membedDictMaster\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text_7props_19k_uw'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedDict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mneighDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetchNeighbours\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp279WordSimSeededDF_wabs_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp279Seeded_SiblingsDF3_wabs_text\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"No. of keys in embedDict: {len(embedDict.keys())}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-16-fcd3022d5c0a>\u001b[0m in \u001b[0;36mfetchNeighbours\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfetchNeighbours\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mneighboursDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighboursDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mneighboursDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36miterrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1069\u001b[0m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   5482\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5483\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5484\u001b[0;31m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5485\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mname\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    536\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m         \u001b[0mvalidate_all_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"{type(self).__name__}.name\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mvalidate_all_hashable\u001b[0;34m(error_name, *args)\u001b[0m\n\u001b[1;32m   1760\u001b[0m     \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \"\"\"\n\u001b[0;32m-> 1762\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{error_name} must be a hashable type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1760\u001b[0m     \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \"\"\"\n\u001b[0;32m-> 1762\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{error_name} must be a hashable type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/dtypes/inference.py\u001b[0m in \u001b[0;36mis_hashable\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m         \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props_19k_uw'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_7props_19k_uw_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['text_7props_19k_uw_old_'+str(weightCase)], confusionMatrixMaster['text_7props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "horizontal-repair",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings - 2 props | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "vital-royal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "adopted-edwards",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "equipped-vaccine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "fourth-drawing",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19155\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19155.000000\n",
-      "mean         0.270895\n",
-      "std          0.059990\n",
-      "min          0.000000\n",
-      "25%          0.228547\n",
-      "50%          0.266816\n",
-      "75%          0.309132\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3793643502923977, pvalue=2.45298661169592e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40586371991954756, pvalue=1.2142237724855381e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.49079754601226994\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19155.000000\n",
-      "mean         0.194648\n",
-      "std          0.115900\n",
-      "min          0.000000\n",
-      "25%          0.083470\n",
-      "50%          0.191821\n",
-      "75%          0.294976\n",
-      "max          0.564922\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.36958425592956784, pvalue=2.7849781516020684e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39348934596160956, pvalue=4.414924758703321e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5766871165644172\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19155.000000\n",
-      "mean         0.323673\n",
-      "std          0.069955\n",
-      "min          0.000000\n",
-      "25%          0.277272\n",
-      "50%          0.319275\n",
-      "75%          0.365774\n",
-      "max          0.677490\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 326\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37836622063614156, pvalue=3.15520072942914e-22)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3746327832763941, pvalue=8.004468522493588e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.39160232226212405, pvalue=7.580483725421636e-26)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.39398189437020814, pvalue=3.8343633718362455e-26)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.588957055214724\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.3834355828220859\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_2props_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['text_2props_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['text_2props_19k_old_'+str(weightCase)], confusionMatrixMaster['text_2props_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mobile-paradise",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "allied-newcastle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "abstract-survey",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "wanted-parade",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "considered-azerbaijan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "composite-shield",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316720\n",
-      "std          0.076381\n",
-      "min          0.000000\n",
-      "25%          0.261478\n",
-      "50%          0.313781\n",
-      "75%          0.370851\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3168500395328192, pvalue=7.486323665386832e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27202499209162273, pvalue=6.770258859650813e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.232133\n",
-      "std          0.142269\n",
-      "min          0.000000\n",
-      "25%          0.089938\n",
-      "50%          0.228553\n",
-      "75%          0.361251\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3361212489826738, pvalue=9.03613034020374e-19)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.29248433284517855, pvalue=7.890529591252537e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6618075801749271\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.375082\n",
-      "std          0.073982\n",
-      "min          0.000000\n",
-      "25%          0.330119\n",
-      "50%          0.377307\n",
-      "75%          0.421860\n",
-      "max          0.841769\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.26508277549218445, pvalue=3.0194195465523546e-12)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21338136285025483, pvalue=4.17252744630606e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5830903790087464\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['complex_19k_old_'+str(weightCase)], confusionMatrixMaster['complex_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "crucial-translation",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "selected-gentleman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "ranking-characterization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "fewer-average",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "rational-present",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "interstate-episode",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327802\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23996930346932127, pvalue=2.6942170209534906e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21449104599327523, pvalue=3.4760310637715348e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237343\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24439786532398405, pvalue=1.2600161404554345e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22590625729631375, pvalue=4.917533132159808e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.394277\n",
-      "std          0.086197\n",
-      "min          0.000000\n",
-      "25%          0.339162\n",
-      "50%          0.397921\n",
-      "75%          0.452267\n",
-      "max          0.850905\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24274131207452157, pvalue=1.6736422306859635e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.201734473314897, pvalue=2.7559659468673134e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "professional-cutting",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "bridal-scope",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "premium-manufacturer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "organic-above",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "removable-steps",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "rubber-principal",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.887688e-02\n",
-      "std      1.377091e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.314421e-01\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31031745482774675, pvalue=3.150145092040765e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.26161279947706156, pvalue=5.774092784287413e-13)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6268221574344023\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.252887e-02\n",
-      "std      8.773144e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.094678e-03\n",
-      "max      5.041080e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.35020973149202944, pvalue=3.059720074110226e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3013432304449195, pvalue=1.0451590470362559e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6588921282798834\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.251606e-01\n",
-      "std      2.222320e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.940167e-01\n",
-      "max      8.900593e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.14149430916593933, pvalue=0.00019608082976567604)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14687524939411561, pvalue=5.22383486460822e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.1457725947521866\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "congressional-teaching",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "medieval-retail",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "artificial-dispatch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "satellite-liquid",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "muslim-gospel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "fuzzy-seven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "sapphire-prescription",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17845\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     8.215694e-02\n",
-      "std      1.439020e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.305726e-01\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22012582471779607, pvalue=6.9001945281841384e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.19941082640413085, pvalue=3.969975785916229e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6472303206997084\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    1.910100e+04\n",
-      "mean     3.399453e-02\n",
-      "std      9.216743e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      1.230265e-03\n",
-      "max      5.315488e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24783606125619878, pvalue=6.903890636652704e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22349921038500295, pvalue=7.473348989752392e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    1.910100e+04\n",
-      "mean     1.302927e-01\n",
-      "std      2.320257e-01\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      1.490116e-08\n",
-      "75%      2.960976e-01\n",
-      "max      9.949517e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12855892543728606, pvalue=0.0007155142288469726)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.13749877727529278, pvalue=0.00015240104075019828)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.17784256559766765\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase'] = embedDict\n",
-    "neighDict = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spiritual-johnson",
-   "metadata": {},
-   "source": [
-    "### Complex Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "monthly-mexican",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "equipped-structure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "natural-integer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>complex_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[-0.263069391, 0.028501017, -0.034664098, 0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.148671135, 0.003585682, 0.319970608, 0.2488...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.396341473, 0.148302898, 0.221964046, 0.5786...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.069007814, -0.053335845, -0.03083352, 0.56...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[0.366982907, -0.250219911, -0.287434697, 0.45...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[-0.377670556, 0.123239793, 0.159144968, 0.648...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.082544975, 0.058447052, -0.614189446, 0.81...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.132387042, -0.025238875, -0.262788028, 0.36...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.459155083, -0.277395934, -0.454340786, 0.44...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.133135661, -0.003577206, -0.233342946, 0.46...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                  complex_embedding\n",
-       "0      Q18037012  [-0.263069391, 0.028501017, -0.034664098, 0.13...\n",
-       "1      Q13112971  [0.148671135, 0.003585682, 0.319970608, 0.2488...\n",
-       "2      Q59336660  [0.396341473, 0.148302898, 0.221964046, 0.5786...\n",
-       "3        Q289472  [-0.069007814, -0.053335845, -0.03083352, 0.56...\n",
-       "4       Q1931010  [0.366982907, -0.250219911, -0.287434697, 0.45...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [-0.377670556, 0.123239793, 0.159144968, 0.648...\n",
-       "19097    Q412770  [-0.082544975, 0.058447052, -0.614189446, 0.81...\n",
-       "19098   Q8063082  [0.132387042, -0.025238875, -0.262788028, 0.36...\n",
-       "19099  Q58308662  [0.459155083, -0.277395934, -0.454340786, 0.44...\n",
-       "19100   Q7205879  [0.133135661, -0.003577206, -0.233342946, 0.46...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "joint-revision",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "rocky-announcement",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.302721\n",
-      "std          0.067184\n",
-      "min          0.000000\n",
-      "25%          0.259615\n",
-      "50%          0.294771\n",
-      "75%          0.342928\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31582754528140355, pvalue=9.373921755437325e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27369250100948833, pvalue=4.756656623381545e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6326530612244898\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.178804\n",
-      "std          0.139703\n",
-      "min          0.000000\n",
-      "25%          0.054379\n",
-      "50%          0.137749\n",
-      "75%          0.307442\n",
-      "max          0.600672\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3464370368444909, pvalue=7.656876025494246e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3011763909407297, pvalue=1.0831987277611373e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.402505\n",
-      "std          0.098028\n",
-      "min          0.000000\n",
-      "25%          0.339741\n",
-      "50%          0.393894\n",
-      "75%          0.452606\n",
-      "max          0.889403\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.34963457034499734, pvalue=3.525500513811503e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12191472957942284, pvalue=0.0013338871853105486)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.3027318092124757, pvalue=7.573910025067949e-17)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.14137053781971248, pvalue=9.875366583327447e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6647230320699709\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16034985422740525\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['complex_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['complex_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "damaged-future",
-   "metadata": {},
-   "source": [
-    "### Transe Embeddings | Probase + 19k + 19k Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "stable-front",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "sublime-cleaner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compEmbeddingsDF['transe_embedding'] = compEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "opposite-destiny",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "defined-disney",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19096</th>\n",
-       "      <td>Q2963354</td>\n",
-       "      <td>[0.126148477, -0.760043263, 0.834101379, 0.413...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19097</th>\n",
-       "      <td>Q412770</td>\n",
-       "      <td>[-0.131047472, -0.532605469, -0.180144981, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19098</th>\n",
-       "      <td>Q8063082</td>\n",
-       "      <td>[0.173550904, -0.246142939, 0.750203907, 0.030...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19099</th>\n",
-       "      <td>Q58308662</td>\n",
-       "      <td>[0.572182953, 0.186988175, -0.116030365, -0.35...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19100</th>\n",
-       "      <td>Q7205879</td>\n",
-       "      <td>[0.752269626, -0.460156113, 0.517503202, 0.019...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            node                                   transe_embedding\n",
-       "0      Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1      Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2      Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3        Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4       Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3...\n",
-       "...          ...                                                ...\n",
-       "19096   Q2963354  [0.126148477, -0.760043263, 0.834101379, 0.413...\n",
-       "19097    Q412770  [-0.131047472, -0.532605469, -0.180144981, -0....\n",
-       "19098   Q8063082  [0.173550904, -0.246142939, 0.750203907, 0.030...\n",
-       "19099  Q58308662  [0.572182953, 0.186988175, -0.116030365, -0.35...\n",
-       "19100   Q7205879  [0.752269626, -0.460156113, 0.517503202, 0.019...\n",
-       "\n",
-       "[19101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compEmbeddingsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "simplified-honolulu",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "suitable-theory",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.316457\n",
-      "std          0.074755\n",
-      "min          0.000000\n",
-      "25%          0.265029\n",
-      "50%          0.307908\n",
-      "75%          0.361417\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.22203711859153544, pvalue=5.1043215609543335e-09)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.20512238050033801, pvalue=1.6083667850582075e-08)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6443148688046647\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.186190\n",
-      "std          0.146220\n",
-      "min          0.000000\n",
-      "25%          0.056864\n",
-      "50%          0.144985\n",
-      "75%          0.307240\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2479273527617227, pvalue=6.816190052635217e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2263056114290315, pvalue=4.5887579264134097e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.597667638483965\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.421187\n",
-      "std          0.108116\n",
-      "min          0.000000\n",
-      "25%          0.350740\n",
-      "50%          0.416101\n",
-      "75%          0.480072\n",
-      "max          0.972091\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2509658430035214, pvalue=3.9754031639979325e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.12731453008255086, pvalue=0.0008059106457544173)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.22562477749827262, pvalue=5.15975984253164e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1476648434866464, pvalue=4.7610094969077205e-05)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.2594752186588921\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(compEmbeddings)\n",
-    "embedDictMaster['transe_probase_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['transe_probase_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chemical-ministry",
-   "metadata": {},
-   "source": [
-    "### Abstract Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "unusual-wesley",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "numerous-timer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "detected-royalty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "familiar-cabinet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "overhead-intellectual",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "fifth-campbell",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "systematic-specialist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "warming-medicare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "convenient-honduras",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.3354244232177734 s\n",
-      "0.11755561828613281 s\n",
-      "0.11744570732116699 s\n",
-      "0.1175832748413086 s\n",
-      "0.11750054359436035 s\n",
-      "0.11730837821960449 s\n",
-      "0.11754989624023438 s\n",
-      "0.11766695976257324 s\n",
-      "0.11713695526123047 s\n",
-      "0.11739706993103027 s\n",
-      "0.12069988250732422 s\n",
-      "0.11744093894958496 s\n",
-      "0.11738061904907227 s\n",
-      "0.11722540855407715 s\n",
-      "0.11743450164794922 s\n",
-      "0.11770343780517578 s\n",
-      "0.11734700202941895 s\n",
-      "0.11752867698669434 s\n",
-      "0.11753010749816895 s\n",
-      "0.11727476119995117 s\n",
-      "0.11757302284240723 s\n",
-      "0.11744475364685059 s\n",
-      "0.1178896427154541 s\n",
-      "0.11759018898010254 s\n",
-      "0.11737680435180664 s\n",
-      "0.11777734756469727 s\n",
-      "0.11771225929260254 s\n",
-      "0.11792659759521484 s\n",
-      "0.1174314022064209 s\n",
-      "0.1175081729888916 s\n",
-      "0.11757636070251465 s\n",
-      "0.11760926246643066 s\n",
-      "0.11748123168945312 s\n",
-      "0.11753368377685547 s\n",
-      "0.11712884902954102 s\n",
-      "0.11767840385437012 s\n",
-      "0.11764121055603027 s\n",
-      "0.11763525009155273 s\n",
-      "0.11754012107849121 s\n",
-      "0.11885714530944824 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "documentary-puppy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "rural-spyware",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.336606\n",
-      "std          0.081060\n",
-      "min          0.000000\n",
-      "25%          0.278247\n",
-      "50%          0.326289\n",
-      "75%          0.388139\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3471431474655109, pvalue=5.660815639939061e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.35383380764979844, pvalue=1.6754686847593527e-22)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5697674418604651\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244583\n",
-      "std          0.150115\n",
-      "min          0.000000\n",
-      "25%          0.103316\n",
-      "50%          0.233314\n",
-      "75%          0.372639\n",
-      "max          0.669782\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.34735894519691607, pvalue=5.347633678079643e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3462632061224712, pvalue=1.2793035570514484e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.400089\n",
-      "std          0.083229\n",
-      "min          0.000000\n",
-      "25%          0.344853\n",
-      "50%          0.397861\n",
-      "75%          0.451782\n",
-      "max          0.835283\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.3498489915976348, pvalue=2.913399131760102e-20)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.32390174521065745, pvalue=1.3650315049340146e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.34236242720323, pvalue=3.60507767671425e-21)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3325159043509967, pvalue=4.65056862032923e-20)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.627906976744186\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.47093023255813954\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _,confusionMatrixMaster['abstract_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "angry-ladder",
-   "metadata": {},
-   "source": [
-    "### Abstract 1st Sentence Text Embeddings | 19k+19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "missing-velvet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "absEmbeddings = {}\n",
-    "for _, row in pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows():\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "increasing-ability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "quarterly-collector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "collaborative-yield",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "associate-seminar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "falling-dublin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "north-interpretation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "willing-hanging",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "functional-mattress",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11401677131652832 s\n",
-      "0.11957383155822754 s\n",
-      "0.09752821922302246 s\n",
-      "0.128997802734375 s\n",
-      "0.11990189552307129 s\n",
-      "0.11844491958618164 s\n",
-      "0.11752724647521973 s\n",
-      "0.11877274513244629 s\n",
-      "0.11719059944152832 s\n",
-      "0.11760425567626953 s\n",
-      "0.11708760261535645 s\n",
-      "0.09807181358337402 s\n",
-      "0.0916135311126709 s\n",
-      "0.09286952018737793 s\n",
-      "0.0907738208770752 s\n",
-      "0.0915842056274414 s\n",
-      "0.09347748756408691 s\n",
-      "0.08993768692016602 s\n",
-      "0.10148978233337402 s\n",
-      "0.11854410171508789 s\n",
-      "0.12027955055236816 s\n",
-      "0.13148927688598633 s\n",
-      "0.11908864974975586 s\n",
-      "0.13748478889465332 s\n",
-      "0.13097643852233887 s\n",
-      "0.11984562873840332 s\n",
-      "0.12159109115600586 s\n",
-      "0.12565040588378906 s\n",
-      "0.1192171573638916 s\n",
-      "0.14190435409545898 s\n",
-      "0.128098726272583 s\n",
-      "0.1173861026763916 s\n",
-      "0.11848211288452148 s\n",
-      "0.11741471290588379 s\n",
-      "0.11730766296386719 s\n",
-      "0.12353205680847168 s\n",
-      "0.11716032028198242 s\n",
-      "0.11889147758483887 s\n",
-      "0.11724376678466797 s\n",
-      "0.11714792251586914 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _, row in wordSim353AnnotDF_New.iterrows():\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "macro-headline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingWordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "provincial-matter",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19166\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19166.000000\n",
-      "mean         0.339064\n",
-      "std          0.072734\n",
-      "min          0.000000\n",
-      "25%          0.287403\n",
-      "50%          0.334725\n",
-      "75%          0.388172\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3571490077264898, pvalue=4.7415066582386246e-21)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3713906787854944, pvalue=1.253257701630852e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6017441860465116\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19166.000000\n",
-      "mean         0.244476\n",
-      "std          0.145834\n",
-      "min          0.000000\n",
-      "25%          0.104152\n",
-      "50%          0.234678\n",
-      "75%          0.376765\n",
-      "max          0.660678\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.37186923032141733, pvalue=1.0985961673989065e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.37051869405377086, pvalue=1.6123151472069758e-24)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6540697674418605\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19166.000000\n",
-      "mean         0.404541\n",
-      "std          0.081848\n",
-      "min          0.000000\n",
-      "25%          0.351736\n",
-      "50%          0.401690\n",
-      "75%          0.453416\n",
-      "max          0.872588\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 344\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.37808291427810886, pvalue=2.1412324981626763e-23)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31762341611315487, pvalue=5.624902334302071e-17)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.36636798649447055, pvalue=5.2187863707845704e-24)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.33907069116618244, pvalue=8.52432972070843e-21)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6511627906976745\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.4941860465116279\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(absEmbeddings)\n",
-    "embedDictMaster['abstract_firstSent_19k'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['abstract_firstSent_19k_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _, confusionMatrixMaster['abstract_firstSent_19k_old_'+str(weightCase)], confusionMatrixMaster['abstract_firstSent_19k_new_'+str(weightCase)] = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "commercial-uzbekistan",
-   "metadata": {},
-   "source": [
-    "### Combination of all old embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "selected-grain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', 'complex_probase', 'transe_probase', 'complex_probase_19k', 'transe_probase_19k', 'abstract_19k', 'abstract_firstSent_19k'])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "remarkable-river",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['text_7props_19k_1', 'text_7props_19k_2', 'text_7props_19k_0.5', 'text_2props_19k_1', 'text_2props_19k_2', 'text_2props_19k_0.5', 'complex_19k_1', 'complex_19k_2', 'complex_19k_0.5', 'transe_19k_1', 'transe_19k_2', 'transe_19k_0.5', 'complex_probase_1', 'complex_probase_2', 'complex_probase_0.5', 'transe_probase_1', 'transe_probase_2', 'transe_probase_0.5', 'complex_probase_19k_1', 'complex_probase_19k_2', 'complex_probase_19k_0.5', 'transe_probase_19k_1', 'transe_probase_19k_2', 'transe_probase_19k_0.5', 'abstract_19k_1', 'abstract_19k_2', 'abstract_19k_0.5', 'abstract_firstSent_19k_1', 'abstract_firstSent_19k_2', 'abstract_firstSent_19k_0.5'])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "after-wallace",
-   "metadata": {},
-   "source": [
-    "#### Compl + Transe + Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "manual-twist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['complex_19k', 'transe_19k', \n",
-    "            'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "according-identity",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 968\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "noble-fisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 968:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "liquid-spray",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19101.0\n",
-       "mean       968.0\n",
-       "std          0.0\n",
-       "min        968.0\n",
-       "25%        968.0\n",
-       "50%        968.0\n",
-       "75%        968.0\n",
-       "max        968.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "designed-killing",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.336903\n",
-      "std          0.071873\n",
-      "min          0.000000\n",
-      "25%          0.284286\n",
-      "50%          0.332284\n",
-      "75%          0.389200\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.31344285039654224, pvalue=1.587990989990702e-16)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2983652058391759, pvalue=2.0692364624319403e-16)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6676384839650146\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.245075\n",
-      "std          0.146398\n",
-      "min          0.000000\n",
-      "25%          0.099960\n",
-      "50%          0.239725\n",
-      "75%          0.380577\n",
-      "max          0.596558\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3270255968193857, pvalue=7.482780885295614e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.3144288703997577, pvalue=4.696112489737555e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6880466472303207\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.400303\n",
-      "std          0.071506\n",
-      "min          0.000000\n",
-      "25%          0.357059\n",
-      "50%          0.402058\n",
-      "75%          0.445388\n",
-      "max          0.813749\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33855152383136056, pvalue=5.074789109206347e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2967991230842074, pvalue=5.639053240442108e-15)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31870634863885755, pvalue=1.6590685394196158e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2739615906247492, pvalue=4.489403776140874e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "suburban-legislature",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.321205\n",
-      "std          0.063429\n",
-      "min          0.000000\n",
-      "25%          0.277493\n",
-      "50%          0.311720\n",
-      "75%          0.359268\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2891828519936888, pvalue=2.717366700252014e-14)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.27713398557337643, pvalue=2.2907553462981174e-14)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6501457725947521\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19101.000000\n",
-      "mean         0.188928\n",
-      "std          0.144828\n",
-      "min          0.000000\n",
-      "25%          0.059667\n",
-      "50%          0.147733\n",
-      "75%          0.327197\n",
-      "max          0.596557\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.33562487778756755, pvalue=1.0136749729444057e-18)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.31665315908408964, pvalue=2.7384643013776528e-18)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19101.000000\n",
-      "mean         0.427234\n",
-      "std          0.094690\n",
-      "min          0.000000\n",
-      "25%          0.367149\n",
-      "50%          0.418971\n",
-      "75%          0.473102\n",
-      "max          0.895330\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.33868087126132435, pvalue=4.924627127334063e-19)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.13050673469358315, pvalue=0.0005927381978235836)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.31852085513832423, pvalue=1.7372739400060115e-18)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1636905372538761, pvalue=6.524566396115546e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6822157434402333\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.22157434402332363\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v1'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k__v1_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interstate-tribute",
-   "metadata": {},
-   "source": [
-    "#### Text_7props + Text_2props + Complex + Transe + Abstract + Abstract_FirstSent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "crucial-italian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictConcat = {}\n",
-    "for key1 in ['text_7props_19k', 'text_2props_19k', 'complex_19k', 'transe_19k', \n",
-    "            'abstract_19k', 'abstract_firstSent_19k']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        if word not in embedDictConcat:\n",
-    "            embedDictConcat[word] = []\n",
-    "        embedDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "for word in embedDictConcat.keys():\n",
-    "    embedDictConcat[word] = [item for sublist in embedDictConcat[word] for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "fatal-robert",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of embedding: 3784\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Size of embedding: {len(embedDictConcat['Q18037012'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "inside-offset",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for word1 in list(embedDictConcat.keys()):\n",
-    "    if len(embedDictConcat[word1]) != 3784:\n",
-    "        del embedDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "colored-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19090.0\n",
-       "mean      3784.0\n",
-       "std          0.0\n",
-       "min       3784.0\n",
-       "25%       3784.0\n",
-       "50%       3784.0\n",
-       "75%       3784.0\n",
-       "max       3784.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(val1) for val1 in embedDictConcat.values()]).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "promising-marijuana",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.318414\n",
-      "std          0.060577\n",
-      "min          0.000000\n",
-      "25%          0.273273\n",
-      "50%          0.313881\n",
-      "75%          0.362231\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40347307747318084, pvalue=5.629213176377413e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.40536224043013125, pvalue=1.6868252763103453e-27)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6307692307692307\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.230697\n",
-      "std          0.135101\n",
-      "min          0.000000\n",
-      "25%          0.097307\n",
-      "50%          0.223935\n",
-      "75%          0.356438\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.40442451164800897, pvalue=4.371701842100601e-25)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.4101503518414275, pvalue=4.1055209935697305e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.378997\n",
-      "std          0.061137\n",
-      "min          0.000000\n",
-      "25%          0.342927\n",
-      "50%          0.379359\n",
-      "75%          0.416067\n",
-      "max          0.760023\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.41886430173530353, pvalue=8.645413536273471e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3828276431671376, pvalue=1.2015280268194575e-22)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.4124606263600543, pvalue=2.0652878227656027e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36987136293537864, pvalue=3.618695156207996e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.5415384615384615\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "piano-mortality",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "17849\n",
-      "No. of keys in embedDict: 19090\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19090.000000\n",
-      "mean         0.304466\n",
-      "std          0.054097\n",
-      "min          0.000000\n",
-      "25%          0.266260\n",
-      "50%          0.295631\n",
-      "75%          0.337646\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.3563958420194592, pvalue=7.705212357023742e-20)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.36941390801600477, pvalue=4.091096997249633e-23)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6092307692307692\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n",
-      "count    19090.000000\n",
-      "mean         0.178573\n",
-      "std          0.134771\n",
-      "min          0.000000\n",
-      "25%          0.057617\n",
-      "50%          0.140175\n",
-      "75%          0.314839\n",
-      "max          0.546240\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.4167472980442226, pvalue=1.5499840293424274e-26)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.41238438242524034, pvalue=2.1128593107239733e-28)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6553846153846153\n",
-      "\n",
-      "\n",
-      "Weight Case: 0.5\n",
-      "count    19090.000000\n",
-      "mean         0.405175\n",
-      "std          0.085240\n",
-      "min          0.000000\n",
-      "25%          0.352177\n",
-      "50%          0.394099\n",
-      "75%          0.443428\n",
-      "max          0.797620\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 325\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.418699029532589, pvalue=9.039782435991911e-27)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.1299009113611685, pvalue=0.0008902251998159792)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.41247576976710154, pvalue=2.053616736808825e-28)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.1761010833372823, pvalue=2.3561612238907513e-06)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6523076923076923\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.16923076923076924\n"
-     ]
-    }
-   ],
-   "source": [
-    "embedDict = normalize(embedDictConcat)\n",
-    "embedDictMaster['concat_probase_19k_v2'] = embedDict\n",
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "for weightCase in [1,2,0.5]:\n",
-    "    print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "    newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "    newEmbedDictMaster['concat_probase_19k_v2_'+str(weightCase)] = newEmbedDict\n",
-    "    dists = determineDistances(embedDict, newEmbedDict)\n",
-    "    print(pd.Series(dists).describe())\n",
-    "    print()\n",
-    "    _ = fetchCorrelationResults(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "extensive-forty",
-   "metadata": {},
-   "source": [
-    "### Confusion Matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "maritime-director",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusionMatrixMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qualified-empty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = embedDictMaster['text_7props_19k']\n",
-    "newEmbedDict = newEmbedDictMaster['text_7props_19k_2']\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "\n",
-    "print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "distinct-timothy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    210\n",
-       "U    101\n",
-       "I     20\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['category'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "antique-infrastructure",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(figsize=(20,20))\n",
-    "for num, key1 in enumerate(['text_7props_19k_old_2', 'text_7props_19k_new_2', 'text_2props_19k_old_2', 'text_2props_19k_new_2', \n",
-    "             'transe_19k_old_1', 'transe_19k_new_1', 'complex_19k_old_1', 'complex_19k_new_1']):\n",
-    "    plt.subplot(4, 2, num+1)\n",
-    "    sns.heatmap(pd.DataFrame(confusionMatrixMaster[key1], LABELS, LABELS), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "micro-phrase",
-   "metadata": {},
-   "source": [
-    "# Save all embeddings and similarity scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "boring-campus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster['concat_probase_19k_v1_2'] = newEmbedDictMaster['concat_probase_19k__v1_2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "automatic-update",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d97162bbb58542999eb317ede34a3300",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "\n",
-    "for key1, key2 in tqdm(emb_pairs):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "related-opposition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "allied-sunrise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004515859562266559, -0.011723668837741234,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.384407</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.0050116109222357935, -0.013383541509188317...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004971298302113362, -0.013254267455943004,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.463734</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004862257036458548, 0.014077170447753274, ...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.005805280378137928, 0.015115419540555773, ...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004862255010517295, 0.01407716458226321, 0...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.01523113313292473, 0.005271417432183582, -0...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.850218</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013922101038066187, 0.0023911841962956006, ...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.01381456413075283, 0.0024352598782685863, -...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.895993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.013016090555331788, 0.01114268765690899, -0...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.962306</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013274960537868754, 0.010965634834463656, -...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013242882478240212, 0.010953106140234146, -...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.993596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626153814759, 0.005047111609608307, -0...</td>\n",
-       "      <td>[0.010410829355084429, 0.014776997941327313, -...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "1  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "2  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "3  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "4  ...  [0.01322626153814759, 0.005047111609608307, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859562266559, -0.011723668837741234,...                 3.468370   \n",
-       "1  [-0.004862257036458548, 0.014077170447753274, ...                 3.035534   \n",
-       "2  [0.01523113313292473, 0.005271417432183582, -0...                 2.902356   \n",
-       "3  [0.013016090555331788, 0.01114268765690899, -0...                 2.995206   \n",
-       "4  [0.010410829355084429, 0.014776997941327313, -...                 1.689315   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.384407   \n",
-       "1                    2.991152   \n",
-       "2                    2.850218   \n",
-       "3                    2.962306   \n",
-       "4                    1.689315   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.0050116109222357935, -0.013383541509188317...   \n",
-       "1  [-0.005805280378137928, 0.015115419540555773, ...   \n",
-       "2  [0.013922101038066187, 0.0023911841962956006, ...   \n",
-       "3  [0.013274960537868754, 0.010965634834463656, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.004971298302113362, -0.013254267455943004,...   \n",
-       "1  [-0.004862255010517295, 0.01407716458226321, 0...   \n",
-       "2  [0.01381456413075283, 0.0024352598782685863, -...   \n",
-       "3  [0.013242882478240212, 0.010953106140234146, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.468370                           3.463734  \n",
-       "1                         3.035534                           2.991152  \n",
-       "2                         2.902356                           2.895993  \n",
-       "3                         2.995206                           2.993596  \n",
-       "4                         1.689315                           1.689315  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "connected-limit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cons_ann_3 = pd.read_csv('../data/consolidated_annotation_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "id": "trying-authorization",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tool</td>\n",
-       "      <td>implement</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>5.313</td>\n",
-       "      <td>4.688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>arrangement</td>\n",
-       "      <td>accommodation</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.304</td>\n",
-       "      <td>5.125</td>\n",
-       "      <td>4.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>l</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>7.938</td>\n",
-       "      <td>2.063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>benchmark</td>\n",
-       "      <td>index</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>5.500</td>\n",
-       "      <td>4.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>practice</td>\n",
-       "      <td>institution</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L,I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.447</td>\n",
-       "      <td>3.563</td>\n",
-       "      <td>6.438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Word 1         Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0         tool      implement      1   NaN      3     U      1   NaN      2   \n",
-       "1  arrangement  accommodation      1   NaN      4   NaN      2     I      1   \n",
-       "2         wood         forest      3     H      1   NaN      1   NaN      1   \n",
-       "3    benchmark          index      2     I      2     I      2     I      4   \n",
-       "4     practice    institution      2     I      3   L,I      3     I      3   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg  Stdev  H_orig  H_reversed  \n",
-       "0     i      1   NaN  1.6  0.894   5.313       4.688  \n",
-       "1   NaN      1   NaN  1.8  1.304   5.125       4.875  \n",
-       "2   NaN      3     l  1.8  1.095   7.938       2.063  \n",
-       "3   NaN      4   NaN  2.8  1.095   5.500       4.500  \n",
-       "4     i      3     i  2.8  0.447   3.563       6.438  "
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cons_ann_3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "id": "political-laptop",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(cons_ann_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "id": "improved-relaxation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ca_cols = cons_ann_3.columns\n",
-    "for _,row in cons_ann_3.iterrows():\n",
-    "#     if ((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum() == 2:\n",
-    "#         print(row['Word 1'], row['Word 2'])\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2']))])\n",
-    "#     print (((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum())\n",
-    "    for col in ca_cols:\n",
-    "        if col in ['Word 1', 'Word 2']:\n",
-    "            continue\n",
-    "        \n",
-    "        wordSim353AnnotDF_New_Merged_DF.at[wordSim353AnnotDF_New_Merged_DF.index[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])], col] = row[col]\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])][col])\n",
-    "#     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "id": "relevant-dodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[~wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2']].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outside-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "pretty-scholar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "gorgeous-syndrome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'ID',\n",
-    " 'H_Sim',\n",
-    " 'H_Dim',\n",
-    " 'F_Sim',\n",
-    " 'F_Dim',\n",
-    " 'N_Sim',\n",
-    " 'N_Dim',\n",
-    " 'D_Sim',\n",
-    " 'D_Dim',\n",
-    " 'P_Sim',\n",
-    " 'P_Dim',\n",
-    " 'Avg',\n",
-    " 'Stdev',\n",
-    " 'H_orig',\n",
-    " 'H_reversed',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].to_csv('../data/wordsim353_all_embeddings_with_retrofits_scores_only_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ahead-syndication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "racial-behalf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.580594\n",
-       "std        0.457960\n",
-       "min        0.000000\n",
-       "25%        0.222619\n",
-       "50%        0.495179\n",
-       "75%        0.852326\n",
-       "max        5.000000\n",
-       "Name: transe_19k_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_old_cosSim_Diff'],ascending=False)['transe_19k_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.645644\n",
-       "std        0.526932\n",
-       "min        0.000000\n",
-       "25%        0.250461\n",
-       "50%        0.562808\n",
-       "75%        0.927608\n",
-       "max        5.000000\n",
-       "Name: transe_19k_1_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_1_new_cosSim_Diff'],ascending=False)['transe_19k_1_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.495179:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.562808:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'transe_19k_old_cosSim', 'transe_19k_1_new_cosSim', 'transe_19k_old_cosSim_Diff', 'transe_19k_1_new_cosSim_Diff', 'transe_19k_old_cosSim_Diff_Cat', 'transe_19k_1_new_cosSim_Diff_Cat']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff_Cat</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>0.523697</td>\n",
-       "      <td>0.122335</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>0.496332</td>\n",
-       "      <td>0.647819</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>0.688666</td>\n",
-       "      <td>0.473791</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>0.118237</td>\n",
-       "      <td>0.056250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>0.064958</td>\n",
-       "      <td>0.119978</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.807539</td>\n",
-       "      <td>3.161937</td>\n",
-       "      <td>0.192461</td>\n",
-       "      <td>0.161937</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.310261</td>\n",
-       "      <td>2.793832</td>\n",
-       "      <td>0.489739</td>\n",
-       "      <td>1.006168</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.449602</td>\n",
-       "      <td>2.739280</td>\n",
-       "      <td>0.550398</td>\n",
-       "      <td>0.260720</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.931610</td>\n",
-       "      <td>3.541754</td>\n",
-       "      <td>1.131610</td>\n",
-       "      <td>1.741754</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.648427</td>\n",
-       "      <td>2.758156</td>\n",
-       "      <td>1.101573</td>\n",
-       "      <td>0.991844</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  transe_19k_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60               3.076303   \n",
-       "1     Arafat         terror  3.60               3.103668   \n",
-       "2        FBI    fingerprint  3.60               2.911334   \n",
-       "3        FBI  investigation  3.00               2.881763   \n",
-       "4    Harvard           Yale  2.20               2.135042   \n",
-       "..       ...            ...   ...                    ...   \n",
-       "339      war         troops  3.00               2.807539   \n",
-       "340   weapon         secret  3.80               3.310261   \n",
-       "341  weather       forecast  3.00               2.449602   \n",
-       "342     wood         forest  1.80               2.931610   \n",
-       "343     word     similarity  3.75               2.648427   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim  transe_19k_old_cosSim_Diff  \\\n",
-       "0                   3.477665                    0.523697   \n",
-       "1                   2.952181                    0.496332   \n",
-       "2                   3.126209                    0.688666   \n",
-       "3                   2.943750                    0.118237   \n",
-       "4                   2.080022                    0.064958   \n",
-       "..                       ...                         ...   \n",
-       "339                 3.161937                    0.192461   \n",
-       "340                 2.793832                    0.489739   \n",
-       "341                 2.739280                    0.550398   \n",
-       "342                 3.541754                    1.131610   \n",
-       "343                 2.758156                    1.101573   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff  transe_19k_old_cosSim_Diff_Cat  \\\n",
-       "0                        0.122335                               1   \n",
-       "1                        0.647819                               1   \n",
-       "2                        0.473791                               1   \n",
-       "3                        0.056250                               0   \n",
-       "4                        0.119978                               0   \n",
-       "..                            ...                             ...   \n",
-       "339                      0.161937                               0   \n",
-       "340                      1.006168                               0   \n",
-       "341                      0.260720                               1   \n",
-       "342                      1.741754                               1   \n",
-       "343                      0.991844                               1   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff_Cat  \n",
-       "0                                   0  \n",
-       "1                                   1  \n",
-       "2                                   0  \n",
-       "3                                   0  \n",
-       "4                                   0  \n",
-       "..                                ...  \n",
-       "339                                 0  \n",
-       "340                                 1  \n",
-       "341                                 0  \n",
-       "342                                 1  \n",
-       "343                                 1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "compressed-emergency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['old_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "alert-holmes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['new_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "psychological-walter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['cat'] = wordSim353AnnotDF_New_Merged_DF['Avg'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badBefore.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badAfter.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consistent-difference",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "unknown-techno",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "false-status",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "varying-bones",
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-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
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-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "employed-clearing",
-   "metadata": {},
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
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-       "      <th>transe_19k_1_new_cosSim</th>\n",
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-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
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-       "      <th>count</th>\n",
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-       "      <td>344.000000</td>\n",
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-       "      <td>344.000000</td>\n",
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-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "creative-interview",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "noticed-andrew",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[col].apply(labelSamples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(labelSamples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
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-       "12350 -1.470588  8      \n",
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-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
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-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
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-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
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-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
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-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
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-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
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-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
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-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
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-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
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-       "1035  2.613445  4      \n",
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-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
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-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
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-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
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-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
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-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
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-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
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-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
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-     },
-     "execution_count": 259,
-     "metadata": {},
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-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>7</td>\n",
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-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
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-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
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-     "metadata": {},
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-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
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-   "execution_count": 261,
-   "id": "binary-committee",
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-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
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-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
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-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
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-    "builtin_function_or_method",
-    "instance",
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-   "window_display": false
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- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Archived/Retrofitting using 19k + 19k dataset-Copy1.ipynb b/Archived/Retrofitting using 19k + 19k dataset-Copy1.ipynb
deleted file mode 100644
index c1a6272..0000000
--- a/Archived/Retrofitting using 19k + 19k dataset-Copy1.ipynb	
+++ /dev/null
@@ -1,714 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "consolidated-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "irish-disposal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1_label not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2_label not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "            sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "            sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "    #         newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])) / ((len(neighbs)) + sum([neighb[1] for neighb in neighbs]))\n",
-    "            newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "    #         newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])) / ((len(neighbs))**0.5 + sum([neighb[1] for neighb in neighbs]))\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {},
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "divine-chair",
-   "metadata": {},
-   "source": [
-    "## Concatenated ChildPar + Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scientific-heading",
-   "metadata": {},
-   "source": [
-    "### Text Embeddings | 19k + 19k dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "stretch-elizabeth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "young-prototype",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "id": "sapphire-bulgarian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.13148594, 0.20147827, 0.015393771, 1.12779...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.19386396, 0.41306746, -0.32783666, 0.450188...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.89479333, 0.27038768, 0.15989815, -0.092144...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.10686234, 0.95048314, 0.32389534, 0.1960084...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.23211154, 0.060303785, -1.0003253, 0.637829...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "1  Q99898510  text_embedding   \n",
-       "2  Q99526025  text_embedding   \n",
-       "3  Q99365546  text_embedding   \n",
-       "4    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  [-0.13148594, 0.20147827, 0.015393771, 1.12779...  \n",
-       "1  [0.19386396, 0.41306746, -0.32783666, 0.450188...  \n",
-       "2  [0.89479333, 0.27038768, 0.15989815, -0.092144...  \n",
-       "3  [0.10686234, 0.95048314, 0.32389534, 0.1960084...  \n",
-       "4  [0.23211154, 0.060303785, -1.0003253, 0.637829...  "
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "closed-charles",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "hungry-cream",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "leading-invasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "protecting-devon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19155"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "id": "prescribed-right",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "boring-williams",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19155.000000\n",
-       "mean         0.310037\n",
-       "std          0.062412\n",
-       "min          0.000000\n",
-       "25%          0.269865\n",
-       "50%          0.310933\n",
-       "75%          0.351558\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
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-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
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-   "cols": {
-    "lenName": 16,
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-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
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-     "delete_cmd_prefix": "rm(",
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diff --git a/Archived/Retrofitting using Master datasets.ipynb b/Archived/Retrofitting using Master datasets.ipynb
deleted file mode 100644
index 7f05d50..0000000
--- a/Archived/Retrofitting using Master datasets.ipynb	
+++ /dev/null
@@ -1,13982 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import scipy.stats as stats\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from time import time\n",
-    "from datetime import date\n",
-    "import warnings\n",
-    "from joblib import Parallel, delayed\n",
-    "import json\n",
-    "\n",
-    "from sklearn.model_selection import StratifiedKFold, KFold\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC, SVR\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error, classification_report, plot_confusion_matrix, confusion_matrix\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "warnings.filterwarnings('ignore')\n",
-    "# pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "balanced-conspiracy",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Pre-Req Class Definitions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "theoretical-perception",
-   "metadata": {},
-   "source": [
-    "## Utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "arbitrary-palace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Utils:\n",
-    "    \"\"\"\n",
-    "    This contains all the utility functions needed by any part of retrofitting\n",
-    "    \"\"\"\n",
-    "    _today = date.today()\n",
-    "    today_date = _today.strftime(\"%b_%d_%Y\")\n",
-    "    LABELS = ['I','M','U']\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def normalize(cls, embed_dict):\n",
-    "        for key, val in embed_dict.items():\n",
-    "            temp = np.array([float(val1) for val1 in val])\n",
-    "            temp2 = temp**2\n",
-    "            embed_dict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def fetch_embeddings(cls, df):\n",
-    "        embed_dict = {}\n",
-    "        for _, row in df.iterrows():\n",
-    "            embed_dict[row.node] = row.value\n",
-    "        return normalize(embed_dict)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def fill_coverage(cls, embed_dict, embed_name):\n",
-    "        wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "        concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "        \n",
-    "        compulsory_coverage_set = set(\n",
-    "                        wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + concept_net_df['word2_kg_id'].to_list())\n",
-    "        \n",
-    "        embed_size = len(embed_dict[next(iter(embed_dict))])\n",
-    "        count = 0\n",
-    "        for word in compulsory_coverage_set:\n",
-    "            if word not in embed_dict:\n",
-    "                embed_dict[word] = np.zeros((embed_size))\n",
-    "                count += 1\n",
-    "        print(f\"Added {count} corrections to {embed_name}\")\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def check_coverage(cls, embed_dict):\n",
-    "        wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        \n",
-    "        compulsory_coverage_set = set(wordsim_df['word1_kg_id'].to_list() + wordsim_df['word2_kg_id'].to_list())\n",
-    "        embed_size = len(embed_dict[next(iter(embed_dict))])\n",
-    "        count = 0\n",
-    "        for word in compulsory_coverage_set:\n",
-    "            if word not in embed_dict:\n",
-    "                count += 1\n",
-    "        return (len(wordSim353AnnotDF_New) - count)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def determine_distances(cls, embed_dict, new_embed_dict):\n",
-    "        dist = []\n",
-    "        for word in embed_dict.keys():\n",
-    "            dist.append(euclidean_distances([embed_dict[word]], [new_embed_dict[word]])[0][0])\n",
-    "        return dist\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def serialize_embedding_dict(cls, embed_dict):\n",
-    "        for key2 in embed_dict.keys():\n",
-    "            embed_dict[key2] = embed_dict[key2].tolist() if type(embed_dict[key2]) != list else embed_dict[key2]\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def deserialize_embedding_dict(cls, embed_dict):\n",
-    "        for key2 in embed_dict.keys():\n",
-    "            embed_dict[key2] = np.array(embed_dict[key2])\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def label_samples(cls, score):\n",
-    "        return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def determine_cos_sim(cls, emb1, emb2):\n",
-    "        return cosine_similarity(\n",
-    "                np.array(emb1).reshape(1,-1), \n",
-    "                np.array(emb2).reshape(1,-1)\n",
-    "            )[0][0]\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def plot_confusion_matrix(cls, conf_matrix, title):\n",
-    "        plt.close()\n",
-    "        sns.heatmap(conf_matrix, xticklabels=Utils.LABELS, yticklabels=Utils.LABELS, annot=True)\n",
-    "        plt.xlabel('Predicted')\n",
-    "        plt.ylabel('Actual')\n",
-    "        plt.title(title+' Confusion Matrix')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "urban-pennsylvania",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import sys\n",
-    "# def sizeof_fmt(num, suffix='B'):\n",
-    "#     ''' by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified'''\n",
-    "#     for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:\n",
-    "#         if abs(num) < 1024.0:\n",
-    "#             return \"%3.1f %s%s\" % (num, unit, suffix)\n",
-    "#         num /= 1024.0\n",
-    "#     return \"%.1f %s%s\" % (num, 'Yi', suffix)\n",
-    "\n",
-    "#     for name, size in sorted(((name, sys.getsizeof(value)) for name, value in locals().items()),\n",
-    "#                              key= lambda x: -x[1])[:10]:\n",
-    "#         print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "equal-papua",
-   "metadata": {},
-   "source": [
-    "## InputEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "excess-mambo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class InputEmbeddings:\n",
-    "    \"\"\"\n",
-    "    Instance variables:\n",
-    "        - embed_dict_master - holds all qnode to embedding mappings as a dictionary\n",
-    "        - embedding_list - list of all keys of the above dictionary\n",
-    "    \"\"\"\n",
-    "    def __init__(self, has_embeddings_include: bool = True):\n",
-    "        self.embed_dict_master = {}\n",
-    "        self.emb_list = ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "        if has_embeddings_include: # TODO\n",
-    "            self.emb_list += ['has_h', 'has_a', 'has_s']\n",
-    "        self.embedding_lengths = {}\n",
-    "        \n",
-    "        for emb_key in tqdm(self.emb_list, desc='Input Embeddings', leave=False):\n",
-    "            self.embed_dict_master[emb_key] = self.fetch_embedding(emb_key)\n",
-    "            \n",
-    "        self.fetch_embedding_stats()\n",
-    "        print(\"Fetched all input embeddings\")\n",
-    "\n",
-    "    def fetch_embedding(self, emb_key):\n",
-    "        return Utils.fill_coverage(\n",
-    "                Utils.deserialize_embedding_dict(\n",
-    "                    json.load(open('../data/Master_P279_dataset/embeddings/'+emb_key+'_orig_embedding_dict.json'))\n",
-    "                ), emb_key\n",
-    "            )\n",
-    "\n",
-    "    def fetch_embedding_stats(self):\n",
-    "        for emb_name in self.embed_dict_master.keys():\n",
-    "            self.embedding_lengths[emb_name] = len(next(iter(self.embed_dict_master[emb_name].values())))\n",
-    "            print(f\"Embedding: {emb_name}, Size: {len(self.embed_dict_master[emb_name].keys())}, Length: {self.embedding_lengths[emb_name]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ceramic-secretary",
-   "metadata": {},
-   "source": [
-    "## NeighborDatasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "detected-division",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NeighborDatasets:\n",
-    "    \"\"\"\n",
-    "    Instance variables:\n",
-    "        - neighbors_dict_master - holds all qnode to neighbor qnode mappings as a dictionary\n",
-    "        - basis_list - list of all keys of the above dictionary\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    def __init__(self, class_datasets_fetch: bool = True, probase_datasets_fetch: bool = False):\n",
-    "        self.neighbors_dict_master = {}\n",
-    "        \n",
-    "        pbar = tqdm(desc='Neighbor Datasets', leave=False, total = \n",
-    "                    6 \n",
-    "                    + (3 if class_datasets_fetch else 0) \n",
-    "                    + (2 if probase_datasets_fetch else 0) \n",
-    "                   )\n",
-    "        \n",
-    "        bert_P279_child_par_df = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')\n",
-    "        bert_P279_child_par_df_cross_enc = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered_cross_enc.csv')\n",
-    "        bert_P279_siblings_df = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "        bert_P279_siblings_df_cross_enc = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed_cross_enc.csv')\n",
-    "        \n",
-    "        self.neighbors_dict_master['bert_child_par'] = self.fetch_neighbours(bert_P279_child_par_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['bert_siblings'] = self.fetch_neighbours(bert_P279_siblings_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['bert_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                bert_P279_child_par_df, bert_P279_siblings_df\n",
-    "            ]))\n",
-    "        pbar.update(1)\n",
-    "        \n",
-    "        self.neighbors_dict_master['cross_enc_child_par'] = self.fetch_neighbours(bert_P279_child_par_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['cross_enc_siblings'] = self.fetch_neighbours(bert_P279_siblings_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['cross_enc_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                bert_P279_child_par_df, bert_P279_siblings_df\n",
-    "            ]))\n",
-    "        pbar.update(1)\n",
-    "            \n",
-    "        if class_datasets_fetch:\n",
-    "            class_P279_child_par_df = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_w_classSim.csv')\n",
-    "            class_P279_child_par_df['similarity_value'] = class_P279_child_par_df['classSim']\n",
-    "            \n",
-    "            class_P279_siblings_df = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_w_classSim.csv')\n",
-    "            class_P279_siblings_df['similarity_value'] = class_P279_siblings_df['classSim']\n",
-    "            \n",
-    "            self.neighbors_dict_master['class_child_par'] = self.fetch_neighbours(class_P279_child_par_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['class_siblings'] = self.fetch_neighbours(class_P279_siblings_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['class_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                    class_P279_child_par_df, class_P279_siblings_df\n",
-    "                ]))\n",
-    "            pbar.update(1)\n",
-    "\n",
-    "        if probase_datasets_fetch:\n",
-    "            probase_df = self.fetch_probase('../data/probase/probase_WQnodes_subset_and_sim.csv')\n",
-    "            \n",
-    "            self.neighbors_dict_master['probase'] = self.fetch_neighbours(probase_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['probase+bert_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                    bert_P279_child_par_df, bert_P279_siblings_df, probase_df\n",
-    "                ]))\n",
-    "            pbar.update(1)\n",
-    "        \n",
-    "        self.basis_list = list(self.neighbors_dict_master.keys())\n",
-    "        \n",
-    "        pbar.close()\n",
-    "\n",
-    "        print(f\"Fetched neighbour datasets: {self.basis_list}\")\n",
-    "    \n",
-    "    def process_probase(self, probase_file_path):\n",
-    "        probase_df = pd.read_csv(probase_file_path)\n",
-    "        probase_df = probase_df.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'similarity_value'})\n",
-    "        probase_df['similarity_value'] = 0.5 + 0.5 * probase_df['similarity_value']\n",
-    "        \n",
-    "        return probase_df\n",
-    "        \n",
-    "    def fetch_neighbours(self, df):\n",
-    "        neighbours_dict = {}\n",
-    "        for _, row in df.iterrows():\n",
-    "            if row.node1 not in neighbours_dict:\n",
-    "                neighbours_dict[row.node1] = []\n",
-    "            neighbours_dict[row.node1].append((row.node2, row.similarity_value))\n",
-    "\n",
-    "            if row.node2 not in neighbours_dict:\n",
-    "                neighbours_dict[row.node2] = []\n",
-    "            neighbours_dict[row.node2].append((row.node1, row.similarity_value))\n",
-    "#         print(max([len(neigh) for neigh in neighbours_dict.values()]))\n",
-    "        \n",
-    "        return neighbours_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "architectural-service",
-   "metadata": {},
-   "source": [
-    "## EvaluationDatasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "through-address",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EvaluationDatasets:\n",
-    "    def __init__(self):\n",
-    "        self.wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        self.fetch_distribution_stats(\"Wordsim-353\", self.wordsim_df)\n",
-    "        \n",
-    "        self.wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "        self.fetch_distribution_stats(\"Wikidata CS\", self.wiki_cs_df)\n",
-    "        \n",
-    "        self.concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "        self.fetch_distribution_stats(\"Concept Net\", self.concept_net_df)\n",
-    "        \n",
-    "        self.get_coverage_nodes()\n",
-    "        \n",
-    "    def fetch_distribution_stats(self, name, dataset):\n",
-    "        print(f\"Dataset: {name}\")\n",
-    "        print(dataset.category.value_counts())\n",
-    "    \n",
-    "    def get_coverage_nodes(self):\n",
-    "        self.coverage = set(\n",
-    "                        self.wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + self.wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + self.wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + self.wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + self.concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + self.concept_net_df['word2_kg_id'].to_list())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "refined-growth",
-   "metadata": {},
-   "source": [
-    "## ResultMetrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "necessary-naples",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ResultMetrics:\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def compute_classification_results(cls,\n",
-    "            embed_dict, \n",
-    "            eval_dataset,\n",
-    "            get_output_values: bool = False,\n",
-    "            old_accuracy = None\n",
-    "            ):\n",
-    "        \"\"\"\n",
-    "        Inputs:\n",
-    "            - embed_dict - dictionary of qnodes with node embeddings as its values\n",
-    "            - eval_dataset - evaluation dataset as pandas dataframe that must have the \n",
-    "                following columns for this function to work correctly:\n",
-    "                * word1_kg_id - Qnode of node1 in the evaluation pair\n",
-    "                * word2_kg_id - Qnode of node2 in the evaluation pair\n",
-    "                * category - Category of the evaluation pair. One of the labels: I/U/M\n",
-    "        Outputs:\n",
-    "            - response_dict - Returns a dictionary with the following keys:\n",
-    "                * covered_pairs - Indicates the number of pairs of the evaluation dataset that the \n",
-    "                    embedding dictionary can cover\n",
-    "                \n",
-    "        \"\"\"\n",
-    "        response_dict = {}\n",
-    "        \n",
-    "        eval_dataset = eval_dataset.copy()\n",
-    "\n",
-    "        missing_words_set = set(\n",
-    "            eval_dataset[eval_dataset.word1_kg_id.apply(lambda p: p not in embed_dict)].word1_kg_id.to_list() \n",
-    "            + eval_dataset[eval_dataset.word2_kg_id.apply(lambda p: p not in embed_dict)].word2_kg_id.to_list()\n",
-    "        )\n",
-    "        \n",
-    "        response_dict['covered_pairs'] = len(eval_dataset)\n",
-    "\n",
-    "        eval_dataset['embedding_cos_sim'] = eval_dataset.apply(lambda p: Utils.determine_cos_sim(embed_dict[p['word1_kg_id']], embed_dict[p['word2_kg_id']]) \n",
-    "                                                   if p['word1_kg_id'] in embed_dict and p['word2_kg_id'] in embed_dict \n",
-    "                                                   else None, axis=1)\n",
-    "        \n",
-    "        eval_dataset['embedding_cos_sim'].fillna(eval_dataset['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "        \n",
-    "        # Scale abs value of cosine similarities to 1,4 strictly\n",
-    "        eval_dataset['embedding_cos_sim'] = eval_dataset['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "        response_dict['accuracy'] = 100 * accuracy_score(\n",
-    "                eval_dataset['category'],\n",
-    "                eval_dataset['embedding_cos_sim'].apply(Utils.label_samples)\n",
-    "            )\n",
-    "    \n",
-    "        response_dict['classification_report'] = classification_report(\n",
-    "                eval_dataset['category'], \n",
-    "                eval_dataset['embedding_cos_sim'].apply(Utils.label_samples), \n",
-    "                output_dict=True\n",
-    "            )\n",
-    "\n",
-    "        response_dict['conf_matrix'] = confusion_matrix(\n",
-    "                eval_dataset['category'], \n",
-    "                eval_dataset['embedding_cos_sim'].apply(Utils.label_samples), \n",
-    "                labels=Utils.LABELS\n",
-    "            )\n",
-    "        \n",
-    "        if old_accuracy is not None:\n",
-    "            response_dict['increase_acc'] = response_dict['accuracy'] - old_accuracy\n",
-    "        else:\n",
-    "            response_dict['increase_acc'] = None\n",
-    "        \n",
-    "        if get_output_values:\n",
-    "            response_dict['preds'] = eval_dataset['embedding_cos_sim'].apply(Utils.label_samples)\n",
-    "\n",
-    "        return response_dict, \\\n",
-    "                (response_dict['covered_pairs'],  \\\n",
-    "                 response_dict['accuracy'], \\\n",
-    "                 response_dict['increase_acc'], \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['I']['precision'],  \\\n",
-    "                 response_dict['classification_report']['I']['recall'],  \\\n",
-    "                 response_dict['classification_report']['I']['f1-score'],   \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['M']['precision'],  \\\n",
-    "                 response_dict['classification_report']['M']['recall'],  \\\n",
-    "                 response_dict['classification_report']['M']['f1-score'], \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['U']['precision'],  \\\n",
-    "                 response_dict['classification_report']['U']['recall'],  \\\n",
-    "                 response_dict['classification_report']['U']['f1-score'])\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def fetch_best_result_for_emb(cls, results_df, emb_col, target_col, iter_col, highest: bool = True):\n",
-    "        opt_value = {}\n",
-    "        for _, row in results_df.iterrows():\n",
-    "            if row[emb_col] not in opt_value:\n",
-    "                opt_value[row[emb_col]] = {'opt_metric': float('-inf') if highest else float('inf'),\n",
-    "                                     'opt_row': [], 'old_row': []}\n",
-    "            if row[iter_col] == 0:\n",
-    "                opt_value[row[emb_col]]['old_row'] = row\n",
-    "            else:\n",
-    "                if (highest and row[target_col] > opt_value[row[emb_col]]['opt_metric']) \\\n",
-    "                        or (not(highest) and row[target_col] < opt_value[row[emb_col]]['opt_metric']):\n",
-    "                    opt_value[row[emb_col]]['opt_metric'] = row[target_col]\n",
-    "                    opt_value[row[emb_col]]['opt_row'] = row\n",
-    "        best_results = []\n",
-    "        for emb_key in opt_value:\n",
-    "            best_results.append(opt_value[emb_key]['old_row'])\n",
-    "            best_results.append(opt_value[emb_key]['opt_row'])\n",
-    "        return pd.DataFrame(best_results, columns = results_df.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "arranged-cosmetic",
-   "metadata": {},
-   "source": [
-    "## RetrofittingProcedures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "prescription-mainstream",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RetrofittingProcedures:\n",
-    "    \n",
-    "    np_label_samples = np.vectorize(Utils.label_samples)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def retrofit(cls,embed_dict, neighbors_dict, weight_case, weight_assignment=False):\n",
-    "        new_embed_dict = {}\n",
-    "        for word in embed_dict.keys():\n",
-    "            if word in neighbors_dict:\n",
-    "                neighbs = neighbors_dict[word]\n",
-    "                neighbs = list(filter(lambda p: p[0] in embed_dict, neighbs))\n",
-    "                if len(neighbs) == 0:\n",
-    "                    new_embed_dict[word] = embed_dict[word]\n",
-    "                    continue\n",
-    "                if weight_assignment:\n",
-    "                    sum_of_sims = sum([neighb[1] for neighb in neighbs])\n",
-    "                    sum_of_embs = sum([embed_dict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "                else:\n",
-    "                    sum_of_sims = sum([1 for neighb in neighbs])\n",
-    "                    sum_of_embs = sum([embed_dict[neighb[0]] for neighb in neighbs])\n",
-    "\n",
-    "                if weight_case == 1:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs)) + sum_of_embs) / ((len(neighbs)) + sum_of_sims)\n",
-    "                elif weight_case == 2:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs))**2 + sum_of_embs) / ((len(neighbs))**2 + sum_of_sims)\n",
-    "                elif weight_case == 0.5:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs))**0.5 + sum_of_embs) / ((len(neighbs))**0.5 + sum_of_sims)\n",
-    "                else:\n",
-    "                    raise\n",
-    "            else:\n",
-    "                new_embed_dict[word] = embed_dict[word]\n",
-    "        return new_embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def execute_all_unsupervised_scenarios(cls,\n",
-    "                emb_list, basis_list, \n",
-    "                embed_dict_master, neigh_dict_master,\n",
-    "                eval_dataset,\n",
-    "                scenario_name: str,\n",
-    "                num_of_iterations: int = 3, \n",
-    "                weightedness_list: list = [True],\n",
-    "                weight_cases_list: list = [1,2],\n",
-    "                get_output_values: bool = False\n",
-    "            ):\n",
-    "        \n",
-    "        new_embed_dict_master = {}\n",
-    "        responses_dict_master = {}\n",
-    "        results = []\n",
-    "        \n",
-    "        for basis in tqdm(basis_list, desc='Basis', leave=False):\n",
-    "            for emb in tqdm(emb_list, desc='Embedding', leave=False):\n",
-    "                for weightedness in weightedness_list:\n",
-    "                    for weight_case in tqdm(weight_cases_list, desc='Weight Case', leave=False):\n",
-    "                        # Base Reference Initializations and Calculations\n",
-    "                        embed_dict = embed_dict_master[emb]\n",
-    "                        responses_dict, result_values = ResultMetrics.compute_classification_results(\n",
-    "                            embed_dict, eval_dataset, get_output_values=get_output_values, old_accuracy=None)\n",
-    "                        results.append([emb, basis, weight_case, weightedness, 0, 'base', *result_values])\n",
-    "                        old_accuracy = responses_dict['accuracy']\n",
-    "                        \n",
-    "                        for iter_num in tqdm(range(1,num_of_iterations+1), desc='Iteration', leave=False):\n",
-    "                            start_time = time()\n",
-    "                            \n",
-    "                            case_name = emb + '_' + basis + '_' + str(weight_case) + ('_weighted' if weightedness else '_unweighted')\n",
-    "                            \n",
-    "                            new_embed_dict = cls.retrofit(embed_dict, neigh_dict_master[basis], weight_case, weightedness)\n",
-    "                            \n",
-    "                            responses_dict, result_values = ResultMetrics.compute_classification_results(\n",
-    "                                new_embed_dict, eval_dataset, get_output_values=get_output_values, old_accuracy=old_accuracy)\n",
-    "                            \n",
-    "                            results.append([emb, basis, weight_case, weightedness, iter_num, case_name, \\\n",
-    "                                                *result_values, \\\n",
-    "                                                time() - start_time\n",
-    "                                            ])\n",
-    "                            \n",
-    "                            new_embed_dict_master[case_name] = embed_dict = new_embed_dict\n",
-    "                            responses_dict_master[case_name] = responses_dict\n",
-    "\n",
-    "        #                     if iter_num == num_of_iterations and highestOne:\n",
-    "        #                         case_name = gR[0] + '_' + gR[1] + '_' + str(gR[2]) + '_weighted'\n",
-    "        #                         new_embed_dict_master[case_name] = serializeEmbeddingDict(new_embed_dict_master[case_name])\n",
-    "        #                         highestOne = False\n",
-    "        #                         json.dump(new_embed_dict_master[case_name],open('../data/Master_P279_dataset/embeddings/new_embedding_dict_'+case_name+'.json','w'))\n",
-    "        #                         new_embed_dict_master[case_name] = deserializeEmbeddingDict(new_embed_dict_master[case_name])\n",
-    "        resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight Case', 'Weightedness', \n",
-    "                                                   'Iteration Num', 'Case Name', \\\n",
-    "                                                   'No. of Pairs Covered', 'Accuracy', 'Increase in Accuracy', \\\n",
-    "                                                   'I Precision', 'I Recall', 'I F1-Score', \\\n",
-    "                                                   'M Precision', 'M Recall', 'M F1-Score', \\\n",
-    "                                                   'U Precision', 'U Recall', 'U F1-Score', \\\n",
-    "                                                   'Time to Retrofit'])\n",
-    "        resultsDF.to_csv('../data/retrofitting/retro_unsup_results.' + scenario_name + '.'+ Utils.today_date +'.csv', index=False)\n",
-    "        best_results_df = ResultMetrics.fetch_best_result_for_emb(resultsDF, 'Embedding', 'Accuracy', 'Iteration Num', highest=True)\n",
-    "        best_results_df.to_csv('../data/retrofitting/retro_unsup_results.' + scenario_name + '.'+ Utils.today_date +'.best.csv', index=False)\n",
-    "        \n",
-    "        cls.save_needed_embeddings(new_embed_dict_master)\n",
-    "        \n",
-    "        return new_embed_dict_master, responses_dict_master\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def save_needed_embeddings(cls, new_embed_dict_master):\n",
-    "        for case_name in new_embed_dict_master:\n",
-    "            temp = {key: new_embed_dict_master[case_name][key] for key in new_embed_dict_master[case_name] if key in evalD.coverage}\n",
-    "            new_embed_dict_master[case_name] = temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attempted-implementation",
-   "metadata": {},
-   "source": [
-    "## SVMProcedures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "missing-facial",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SVMProcedures:\n",
-    "    @classmethod\n",
-    "    def execute_supervised_scenario(cls,\n",
-    "                eval_dataset, case, embed_dict_master, new_embed_dict_master, \n",
-    "                num_of_splits = 10, \n",
-    "                comb_mode: bool = False, SVC_or_SVR: str = 'SVC'\n",
-    "            ):\n",
-    "        \n",
-    "        X = []        \n",
-    "        \n",
-    "        ################\n",
-    "        # 2 Approaches based on argument: `comb_mode`\n",
-    "        ################\n",
-    "        \n",
-    "        if comb_mode: ########## COMBINATION MODE CODE ####################\n",
-    "            case_name = \" & \".join(case['emb']) + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "            \n",
-    "            for _, row in eval_dataset.iterrows():\n",
-    "                if case['svm_input'] == 'emb':\n",
-    "                    tempX = []\n",
-    "                    for individual_emb in case['emb']:\n",
-    "                        ind_case_name = individual_emb + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "                        if case['iter_num'] != 0 and ind_case_name not in new_embed_dict_master:\n",
-    "                            return case_name, case, None\n",
-    "                        if case['iter_num'] == 0:\n",
-    "                            tempX += embed_dict_master[individual_emb][row['word1_kg_id']].tolist() + embedDictMaster[individual_emb][row['word2_kg_id']].tolist()\n",
-    "                        else:\n",
-    "                            tempX += new_embed_dict_master[ind_case_name][row['word1_kg_id']].tolist() + newEmbedDictMaster[ind_case_name][row['word2_kg_id']].tolist()\n",
-    "                    X.append(tempX)\n",
-    "                else:\n",
-    "                    tempX = []\n",
-    "                    for individual_emb in case['emb']:\n",
-    "                        ind_case_name = individual_emb + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "                        if case['iter_num'] != 0 and ind_case_name not in new_embed_dict_master:\n",
-    "                            return case_name, case, None\n",
-    "                        if case['iter_num'] == 0:\n",
-    "                            tempX.append(abs(Utils.determine_cos_sim(\n",
-    "                                embed_dict_master[individual_emb][row['word1_kg_id']], \n",
-    "                                embed_dict_master[individual_emb][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                        else:\n",
-    "                            tempX.append(abs(Utils.determine_cos_sim(\n",
-    "                                new_embed_dict_master[ind_case_name][row['word1_kg_id']],\n",
-    "                                new_embed_dict_master[ind_case_name][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    X.append(tempX)\n",
-    "\n",
-    "        else: ########## NON-COMBINATION MODE CODE ####################\n",
-    "            case_name = case['emb'] + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "            if case['iter_num'] != 0 and case_name not in new_embed_dict_master:\n",
-    "                return case_name, case, None\n",
-    "            for _, row in eval_dataset.iterrows():\n",
-    "                if case['svm_input'] == 'emb':\n",
-    "                    if case['iter_num'] == 0:\n",
-    "                        X.append(embed_dict_master[case['emb']][row['word1_kg_id']].tolist() + embed_dict_master[case['emb']][row['word2_kg_id']].tolist())\n",
-    "                    else:\n",
-    "                        X.append(new_embed_dict_master[case_name][row['word1_kg_id']].tolist() + new_embed_dict_master[case_name][row['word2_kg_id']].tolist())\n",
-    "                else:\n",
-    "                    if case['iter_num'] == 0:\n",
-    "                        X.append(abs(Utils.determine_cos_sim(\n",
-    "                                embed_dict_master[case['emb']][row['word1_kg_id']], \n",
-    "                                embed_dict_master[case['emb']][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    else:\n",
-    "                        X.append(abs(Utils.determine_cos_sim(\n",
-    "                                new_embed_dict_master[case_name][row['word1_kg_id']],\n",
-    "                                new_embed_dict_master[case_name][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    \n",
-    "        X = pd.DataFrame(X)\n",
-    "        \n",
-    "        ################\n",
-    "        # 2 Approaches based on argument: `SVC_or_SVR`\n",
-    "        ################\n",
-    "        \n",
-    "        # Target split depending on SVC or SVM\n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            Y = eval_dataset['category']\n",
-    "        elif SVC_or_SVR == 'SVR':\n",
-    "            if 'Avg' not in eval_dataset.columns:\n",
-    "                raise ValueError(\"Avg column not present in the provided eval_dataset\")\n",
-    "            Y = (eval_dataset['Avg'] - 1) / 3\n",
-    "        else:\n",
-    "            raise ValueError(\"Invalid SVC_or_SVR provided\")\n",
-    "        \n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            skf = StratifiedKFold(n_splits=num_of_splits, random_state=19, shuffle=True)\n",
-    "            X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "            for train_index, test_index in skf.split(X, Y):\n",
-    "                X_train_splits.append(X.iloc[train_index])\n",
-    "                X_test_splits.append(X.iloc[test_index])\n",
-    "                Y_train_splits.append(Y.iloc[train_index])\n",
-    "                Y_test_splits.append(Y.iloc[test_index])\n",
-    "        elif SVC_or_SVR == 'SVR':\n",
-    "            skf = KFold(n_splits=num_of_splits, random_state=19, shuffle=True)\n",
-    "            X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "            for train_index, test_index in skf.split(X, Y):\n",
-    "                X_train_splits.append(X.iloc[train_index])\n",
-    "                X_test_splits.append(X.iloc[test_index])\n",
-    "                Y_train_splits.append(Y.iloc[train_index])\n",
-    "                Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "        preds = []\n",
-    "        \n",
-    "        # Classifier/Regressor training depending on SVC or SVM\n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "                clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100, max_iter=100))\n",
-    "                clf.fit(X_train1, Y_train1)\n",
-    "                preds.append(clf.predict(X_test1))\n",
-    "                \n",
-    "            acc = 0\n",
-    "            for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "                acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "            return case_name, *list(case.values()), acc/num_of_splits\n",
-    "        \n",
-    "        elif SVC_or_SVR == 'SVR':\n",
-    "            for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "                clf = make_pipeline(StandardScaler(), SVR(gamma='auto', max_iter=100))\n",
-    "                clf.fit(X_train1, Y_train1)\n",
-    "                preds.append(clf.predict(X_test1))\n",
-    "            \n",
-    "            acc = 0\n",
-    "            ktCorr = 0\n",
-    "            for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "                acc += mean_squared_error(pred * 3 + 1, Y_test1 * 3 + 1)\n",
-    "                ktCorr += stats.kendalltau(Y_test1 * 3 + 1, pred * 3 + 1).correlation\n",
-    "                \n",
-    "            return case_name, *list(case.values()), acc/num_of_splits, ktCorr/num_of_splits\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def execute_all_supervised_scenarios(cls,\n",
-    "                emb_list, basis_list, embed_dict_master, new_embed_dict_master, \n",
-    "                eval_dataset, \n",
-    "                scenario_name: str,\n",
-    "                num_of_splits = 10,\n",
-    "                comb_mode: bool = False, SVC_or_SVR: str = 'SVC', \n",
-    "                num_of_iterations = 3,\n",
-    "                num_of_jobs = 1\n",
-    "            ):\n",
-    "        \n",
-    "        if not(comb_mode):\n",
-    "            svm_cases_list = []\n",
-    "            for basis in basis_list:\n",
-    "                for emb in emb_list:\n",
-    "                    for weightedness in [True]:\n",
-    "                        for iter_num in range(0,num_of_iterations+1):\n",
-    "                            for weight_case in [1,2]:\n",
-    "                                for svm_input in ['emb','score']:\n",
-    "                                    temp_dict = {'basis': basis, 'emb': emb, 'weightedness': weightedness, \n",
-    "                                                'iter_num': iter_num, 'weight_case': weight_case, 'svm_input': svm_input}\n",
-    "                                    svm_cases_list.append(temp_dict) \n",
-    "        else:\n",
-    "            svm_cases_list = []\n",
-    "            for basis in basis_list:\n",
-    "                for emb in emb_list:\n",
-    "                    for weightedness in [True]:\n",
-    "                        for iter_num in range(0,num_of_iterations+1):\n",
-    "                            for weight_case in [1,2]:\n",
-    "                                for svm_input in ['score']:\n",
-    "                                    for i in range(1,len(emb_list)+1):\n",
-    "                                        for emb_comb in combinations(emb_list, i):\n",
-    "                                            temp_dict = {'basis': basis, 'emb': emb_comb, 'weightedness': weightedness, \n",
-    "                                                        'iter_num': iter_num, 'weight_case': weight_case, 'svm_input': svm_input}\n",
-    "                                            svm_cases_list.append(temp_dict) \n",
-    "\n",
-    "        results = Parallel(n_jobs=num_of_jobs)(delayed(cls.execute_supervised_scenario)(\n",
-    "                eval_dataset, caseDict, embed_dict_master, \n",
-    "                new_embed_dict_master,num_of_splits,\n",
-    "                comb_mode, SVC_or_SVR\n",
-    "            ) for caseDict in tqdm(svm_cases_list))\n",
-    "        \n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            results_df = pd.DataFrame(results, columns=['Case Name','Basis','Embedding','Weightedness', 'Iteration Num', 'Weight Case', 'Technique','Accuracy'])\n",
-    "            best_results_df = ResultMetrics.fetch_best_result_for_emb(results_df, 'Embedding', 'Accuracy', 'Iteration Num', highest=True)\n",
-    "            best_results_df.to_csv('../data/retrofitting/retro_SVM_results.' + scenario_name + '.'+ Utils.today_date +'.best.csv', index=False)\n",
-    "        else:\n",
-    "            results_df = pd.DataFrame(results, columns=['Case Name','Basis','Embedding','Weightedness', 'Iteration Num', 'Weight Case', 'Technique','MSE', 'KT Correlation'])\n",
-    "            best_results_df = ResultMetrics.fetch_best_result_for_emb(results_df, 'Embedding', 'MSE', 'Iteration Num', highest=False)\n",
-    "            best_results_df.to_csv('../data/retrofitting/retro_SVM_results.' + scenario_name + '.'+ Utils.today_date +'.best.csv', index=False)\n",
-    "            \n",
-    "        results_df.to_csv('../data/retrofitting/retro_SVM_results.' + scenario_name + '.'+ Utils.today_date +'.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "informed-voluntary",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "retained-frederick",
-   "metadata": {},
-   "source": [
-    "# The Master Controller"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "neither-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Neighbor Datasets:   0%|          | 0/9 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fetched neighbour datasets: ['bert_child_par', 'bert_siblings', 'bert_all', 'cross_enc_child_par', 'cross_enc_siblings', 'cross_enc_all', 'class_child_par', 'class_siblings', 'class_all']\n",
-      "Dataset: Wordsim-353\n",
-      "M    221\n",
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-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-9-3ea9b008be81>\u001b[0m in \u001b[0;36mexecute_all_unsupervised_scenarios\u001b[0;34m(cls, emb_list, basis_list, embed_dict_master, neigh_dict_master, eval_dataset, scenario_name, num_of_iterations, weightedness_list, weight_cases_list, get_output_values)\u001b[0m\n\u001b[1;32m     64\u001b[0m                             \u001b[0mcase_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbasis\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_case\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'_weighted'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mweightedness\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'_unweighted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                             \u001b[0mnew_embed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrofit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_dict_master\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_case\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweightedness\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                             responses_dict, result_values = ResultMetrics.compute_classification_results(\n",
-      "\u001b[0;32m<ipython-input-9-3ea9b008be81>\u001b[0m in \u001b[0;36mretrofit\u001b[0;34m(cls, embed_dict, neighbors_dict, weight_case, weight_assignment)\u001b[0m\n\u001b[1;32m     15\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mweight_assignment\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                     \u001b[0msum_of_sims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mneighb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                     \u001b[0msum_of_embs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0membed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneighb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mneighb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                     \u001b[0msum_of_sims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mneighb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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-   "source": [
-    "%%time\n",
-    "start_master_time = time()\n",
-    "new_embed_dict_master, responses_dict_master = {}, {}\n",
-    "\n",
-    "# Load all supporting files\n",
-    "# inp = InputEmbeddings()\n",
-    "basis = NeighborDatasets()\n",
-    "evalD = EvaluationDatasets()\n",
-    "\n",
-    "# get_size_of_variables()\n",
-    "\n",
-    "# Wordsim executions\n",
-    "new_embed_dict_master['wordsim'], responses_dict_master['wordsim'] = RetrofittingProcedures.execute_all_unsupervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, basis.neighbors_dict_master, \n",
-    "                                                          evalD.wordsim_df, \"wordsim_ind\")\n",
-    "# get_size_of_variables()\n",
-    "print(\"Analysed wordsim_ind\")\n",
-    "\n",
-    "SVMProcedures.execute_all_supervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, \n",
-    "                                            new_embed_dict_master['wordsim'], \n",
-    "                                            evalD.wordsim_df, \"SVC_Wordsim\",\n",
-    "                                            comb_mode = False, SVC_or_SVR = 'SVC')\n",
-    "print(\"Analysed SVC_Wordsim\")\n",
-    "\n",
-    "SVMProcedures.execute_all_supervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, \n",
-    "                                            new_embed_dict_master['wordsim'], \n",
-    "                                            evalD.wordsim_df, \"SVR_Wordsim\",\n",
-    "                                            comb_mode = False, SVC_or_SVR = 'SVR')\n",
-    "print(\"Analysed SVR_Wordsim\")\n",
-    "\n",
-    "# SVMProcedures.execute_all_supervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, \n",
-    "#                                             new_embed_dict_master['wordsim'], \n",
-    "#                                             evalD.wordsim_df, \"SVR_Wordsim\",\n",
-    "#                                             comb_mode = True, SVC_or_SVR = 'SVR')\n",
-    "# print(\"Analysed SVR_Wordsim combinatrics\")\n",
-    "\n",
-    "new_embed_dict_master, responses_dict_master = {}, {}\n",
-    "\n",
-    "# Wiki CS executions\n",
-    "new_embed_dict_master['wiki_cs'], responses_dict_master['wiki_cs'] = RetrofittingProcedures.execute_all_unsupervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, basis.neighbors_dict_master, \n",
-    "                                                          evalD.wiki_cs_df, \"wiki_cs_ind\")\n",
-    "print(\"Analysed wiki_cs_ind\")\n",
-    "\n",
-    "SVMProcedures.execute_all_supervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master,\n",
-    "                                            new_embed_dict_master['wiki_cs'],\n",
-    "                                            evalD.wordsim_df, \"SVC_Wiki_CS\",\n",
-    "                                            comb_mode = False, SVC_or_SVR = 'SVC')\n",
-    "print(\"Analysed SVC_Wiki_CS\")\n",
-    "\n",
-    "# Conceptnet executions\n",
-    "new_embed_dict_master['conceptnet'], responses_dict_master['conceptnet'] = RetrofittingProcedures.execute_all_unsupervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master, basis.neighbors_dict_master, \n",
-    "                                                          evalD.concept_net_df, \"concept_net_ind\")\n",
-    "print(\"Analysed concept_net_ind\")\n",
-    "\n",
-    "SVMProcedures.execute_all_supervised_scenarios(inp.emb_list, basis.basis_list, inp.embed_dict_master,\n",
-    "                                            new_embed_dict_master['conceptnet'],\n",
-    "                                            evalD.wordsim_df, \"SVC_Conceptnet\",\n",
-    "                                            comb_mode = False, SVC_or_SVR = 'SVC')\n",
-    "print(\"Analysed SVC_Conceptnet\")\n",
-    "\n",
-    "print(f\"Time taken for end-to-end execution: {time() - start_master_time}s\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "champion-memory",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hundred-virus",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Evaluation Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "limited-hormone",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bioDF = pd.read_csv('../data/pedersen2007measures_table1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "alien-happiness",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Term1</th>\n",
-       "      <th>Term1_kg_id</th>\n",
-       "      <th>Term1_kg_label</th>\n",
-       "      <th>Term1_description</th>\n",
-       "      <th>Term2</th>\n",
-       "      <th>Term2_kg_id</th>\n",
-       "      <th>Term2_kg_label</th>\n",
-       "      <th>Term2_description</th>\n",
-       "      <th>Physician</th>\n",
-       "      <th>Coder</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Renal failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>Kidney failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Heart</td>\n",
-       "      <td>Q1072</td>\n",
-       "      <td>heart</td>\n",
-       "      <td>organ for the circulation of blood in animal c...</td>\n",
-       "      <td>Myocardium</td>\n",
-       "      <td>Q84133</td>\n",
-       "      <td>myocardium</td>\n",
-       "      <td>middle layer of the heart wall, which consists...</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Stroke</td>\n",
-       "      <td>Q12202</td>\n",
-       "      <td>stroke</td>\n",
-       "      <td>problem with the arteries supplying blood to t...</td>\n",
-       "      <td>Infarct</td>\n",
-       "      <td>Q207550</td>\n",
-       "      <td>infarction</td>\n",
-       "      <td>tissue death caused by a local lack of oxygen,...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Abortion</td>\n",
-       "      <td>Q8452</td>\n",
-       "      <td>abortion</td>\n",
-       "      <td>intentional ending of a pregnancy</td>\n",
-       "      <td>Miscarriage</td>\n",
-       "      <td>Q28693</td>\n",
-       "      <td>miscarriage</td>\n",
-       "      <td>natural death of an embryo or fetus before it ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Delusion</td>\n",
-       "      <td>Q189643</td>\n",
-       "      <td>delusion</td>\n",
-       "      <td>firm and fixed belief based on inadequate grou...</td>\n",
-       "      <td>Schizophrenia</td>\n",
-       "      <td>Q41112</td>\n",
-       "      <td>schizophrenia</td>\n",
-       "      <td>psychotic disorder characterized by emotional ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Term1 Term1_kg_id  Term1_kg_label  \\\n",
-       "0  Renal failure     Q476921  kidney failure   \n",
-       "1          Heart       Q1072           heart   \n",
-       "2         Stroke      Q12202          stroke   \n",
-       "3       Abortion       Q8452        abortion   \n",
-       "4       Delusion     Q189643        delusion   \n",
-       "\n",
-       "                                   Term1_description           Term2  \\\n",
-       "0  disease where the kidneys fail to adequately f...  Kidney failure   \n",
-       "1  organ for the circulation of blood in animal c...      Myocardium   \n",
-       "2  problem with the arteries supplying blood to t...         Infarct   \n",
-       "3                  intentional ending of a pregnancy     Miscarriage   \n",
-       "4  firm and fixed belief based on inadequate grou...   Schizophrenia   \n",
-       "\n",
-       "  Term2_kg_id  Term2_kg_label  \\\n",
-       "0     Q476921  kidney failure   \n",
-       "1      Q84133      myocardium   \n",
-       "2     Q207550      infarction   \n",
-       "3      Q28693     miscarriage   \n",
-       "4      Q41112   schizophrenia   \n",
-       "\n",
-       "                                   Term2_description  Physician  Coder  \n",
-       "0  disease where the kidneys fail to adequately f...        4.0    4.0  \n",
-       "1  middle layer of the heart wall, which consists...        3.3    3.0  \n",
-       "2  tissue death caused by a local lack of oxygen,...        3.0    2.8  \n",
-       "3  natural death of an embryo or fetus before it ...        3.0    3.3  \n",
-       "4  psychotic disorder characterized by emotional ...        3.0    2.2  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bioDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parental-event",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bioDFNodesSet = set(bioDF.Term1_kg_id.to_list() + bioDF.Term2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "wicked-proportion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "P279childParNodesSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list())\n",
-    "P279siblingsNodesSet = set(p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "pediatric-tomato",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 1)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "transparent-diversity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 25)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probaseNodesSet = set(probDF_Qnodes_DF_WQnodes1_subset.node1.to_list() + probDF_Qnodes_DF_WQnodes1_subset.node2.to_list())\n",
-    "\n",
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in probaseNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in probaseNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "uniform-civilian",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "voluntary-logic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amended-forth",
-   "metadata": {},
-   "source": [
-    "## Retrofitting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dirty-magic",
-   "metadata": {},
-   "source": [
-    "### Retrofitting using concatenation of embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "twenty-chinese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "def generateConcatenatedEmbeddingDict(embedDictMaster, key_comb, embedding_lengths):\n",
-    "    embedDict = defaultdict(list)\n",
-    "    masterKeySet = set()\n",
-    "    for key in key_comb:\n",
-    "        for qnode in embedDictMaster[key]:\n",
-    "            masterKeySet.add(qnode)\n",
-    "    for qnode in masterKeySet:\n",
-    "        for key in key_comb:\n",
-    "            if qnode in embedDictMaster[key]:\n",
-    "                embedDict[qnode] = embedDict[qnode] + (embedDictMaster[key][qnode].tolist())\n",
-    "            else:\n",
-    "                embedDict[qnode] = embedDict[qnode] + [0]*embedding_lengths[key]\n",
-    "        embedDict[qnode] = np.array(embedDict[qnode])\n",
-    "    return dict(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "cooperative-reference",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1min 16s, sys: 5.4 s, total: 1min 21s\n",
-      "Wall time: 1min 21s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "embedDictMaster['text_7-text_2-comp-tran-abs-abs_first'] = generateConcatenatedEmbeddingDict(embedDictMaster, \n",
-    "                ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent'], \n",
-    "                embedding_lengths)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "direct-discrimination",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55c060d3c04441008eb1631416aa2a38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "880c54ab554f47c3bd423f4cd96a9652",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "782a811c6d614335a86eaadac0ce6766",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "07cfda2280d3482d8923fd4093acfe6b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "newEmbedDictMaster = {}\n",
-    "responsesDictMaster = {}\n",
-    "\n",
-    "results = []\n",
-    "NUM_ITERS = 2\n",
-    "for basis in tqdm(basisList):\n",
-    "    for weightedNess in [True]:\n",
-    "        for i in tqdm(range(2,len(embList)+1)):\n",
-    "            for embComb in tqdm(combinations(embList, i)):\n",
-    "                emb = '-'.join(embComb)\n",
-    "                start_time = time()\n",
-    "                embedDict = {}\n",
-    "                embedDict[1] = embedDict[2] = generateConcatenatedEmbeddingDict(embedDictMaster,embComb, embedding_lengths)\n",
-    "                \n",
-    "                for weightCase in [1,2]:\n",
-    "                    oldAcc = 0\n",
-    "#                     groupResults = []\n",
-    "                    for iterNum in range(1,NUM_ITERS+1):\n",
-    "                        if weightedNess:\n",
-    "                            caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                        else:\n",
-    "                            caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                        newEmbedDict = retrofit(embedDict[weightCase], neighDictMaster[basis], weightCase, weightedNess)\n",
-    "            #             dists = determine_distances(embedDict, newEmbedDict)\n",
-    "                        responsesDict = fetchCorrelationResults(embedDict[weightCase], newEmbedDict)\n",
-    "            #                 print(responsesDict.keys())\n",
-    "                        if iterNum == 1:\n",
-    "                            oldAcc = responsesDict['old_acc']*100\n",
-    "                            groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                        -1, \\\n",
-    "                                        responsesDict['old_acc']*100, \\\n",
-    "                                        0, \\\n",
-    "                                        responsesDict['coveredPairs'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                         i, \\\n",
-    "                                         time() - start_time\n",
-    "                                        ])\n",
-    "                        results.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                        -1, \\\n",
-    "                                        responsesDict['new_acc']*100, \\\n",
-    "                                        (responsesDict['new_acc'] - oldAcc)*100, \\\n",
-    "                                        responsesDict['coveredPairs'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                         i, \\\n",
-    "                                         time() - start_time\n",
-    "                                        ])\n",
-    "                        embedDict[weightCase] = newEmbedDict\n",
-    "\n",
-    "                        newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                        responsesDictMaster[caseName] = responsesDict\n",
-    "                        if responsesDict['new_acc']*100 < oldAcc:\n",
-    "                            break\n",
-    "#                 results.append(gr)\n",
-    "#                     highestOne = True\n",
-    "#                     for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "#                         results.append(gR+[rank])\n",
-    "#                     if iterNum == NUM_ITERS and highestOne:\n",
-    "#                         caseName = gR[0] + '_' + gR[1] + '_' + str(gR[2]) + '_weighted'\n",
-    "#                         newEmbedDictMaster[caseName] = serializeEmbeddingDict(newEmbedDictMaster[caseName])\n",
-    "#                         highestOne = False\n",
-    "#                         json.dump(newEmbedDictMaster[caseName],open('../data/Master_P279_dataset/embeddings/new_embedding_dict_'+caseName+'.json','w'))\n",
-    "#                         newEmbedDictMaster[caseName] = deserializeEmbeddingDict(newEmbedDictMaster[caseName])\n",
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'No. of embeddings', \\\n",
-    "                                           'Time'])\n",
-    "resultsDF.to_csv('../data/retrofitting/retro_concat_'+ today_date +'.csv', index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "elementary-console",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "korean-albania",
-   "metadata": {},
-   "source": [
-    "## SVM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "golden-melissa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4aca6f0bfc654b178bd71a5c7cfe22d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "svmCasesList = []\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            for iterNum in range(0,NUM_ITERS+1):\n",
-    "                for weightCase in [1,2]:\n",
-    "                    for svm_input in ['emb','score']:\n",
-    "                        temp_dict = {'basis': basis, 'emb': emb, 'weightedNess': weightedNess, \n",
-    "                                    'iterNum': iterNum, 'weightCase': weightCase, 'svm_input': svm_input}\n",
-    "                        svmCasesList.append(temp_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "renewable-selling",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "broadband-column",
-   "metadata": {},
-   "source": [
-    "### SVM Classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "accompanied-tuner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(results, columns=['caseName','basis','emb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy']).to_csv('../data/retrofitting/retro_SVM_test_'+ today_date +'.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "immediate-writer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyCombs(wordSim353AnnotDF_New_Merged_DF, case):\n",
-    "    X = []\n",
-    "    \n",
-    "    if case['svm_input'] == 'emb':\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += embedDictMaster[emb][row['word1_kg_id']].tolist() + embedDictMaster[emb][row['word2_kg_id']].tolist()\n",
-    "                else:\n",
-    "                    tempX += newEmbedDictMaster[caseName][row['word1_kg_id']].tolist() + newEmbedDictMaster[caseName][row['word2_kg_id']].tolist()\n",
-    "            X.append(tempX)\n",
-    "    else:\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += [(abs(cosine_similarity(embedDictMaster[emb][row['word1_kg_id']].reshape(1,-1), embedDictMaster[emb][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "                else:\n",
-    "                    tempX += [(abs(cosine_similarity(newEmbedDictMaster[caseName][row['word1_kg_id']].reshape(1,-1), newEmbedDictMaster[caseName][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "            X.append(tempX)\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    X = pd.DataFrame(X)\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return caseName, *list(case.values()), acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "insured-escape",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'basisList' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-23-f8d710c8d206>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msvmCombCasesList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbasis\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mweightedNess\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0miterNum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNUM_ITERS\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mweightCase\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'basisList' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "svmCombCasesList = []\n",
-    "for basis in tqdm(basisList):\n",
-    "        for weightedNess in [True]:\n",
-    "            for iterNum in range(0,NUM_ITERS+1):\n",
-    "                for weightCase in [1,2]:\n",
-    "                    for svm_input in ['score']:\n",
-    "                        for i in range(1,len(embList)+1):\n",
-    "                            for embComb in combinations(embList, i):\n",
-    "                                temp_dict = {'basis': basis, 'emb': embComb, 'weightedNess': weightedNess, \n",
-    "                                            'iterNum': iterNum, 'weightCase': weightCase, 'svm_input': svm_input}\n",
-    "                                svmCombCasesList.append(temp_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3024"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(svmCombCasesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "chicken-computer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3024 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 18min 7s, sys: 24.6 s, total: 18min 32s\n",
-      "Wall time: 18min 27s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindAccuracyCombs)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCombCasesList))\n",
-    "resDF = pd.DataFrame(results, columns=['caseName','basis','emb_comb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy'])\n",
-    "resDF['emb_comb'] = resDF['emb_comb'].apply(lambda p: \" & \".join(p))\n",
-    "resDF.to_csv('../data/retrofitting/retro_SVM_comb_test_'+ today_date +'.csv', index=False)\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "featured-blast",
-   "metadata": {},
-   "source": [
-    "### SVM Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "pediatric-intelligence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f5e0e7ad7fb4e5589d10aaaf55783a8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/432 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindRegressionAccuracy)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCasesList))\n",
-    "pd.DataFrame(results, columns=['caseName','basis','emb','weightedNess', 'iteration', 'weightCase', 'technique','Acc']).to_csv('../data/retrofitting/retro_SVM_Reg_test_'+ today_date +'.csv', index=False)\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "painted-objective",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindRegressionAccuracyCombs(wordSim353AnnotDF_New_Merged_DF, case):\n",
-    "    X = []\n",
-    "    \n",
-    "    if case['svm_input'] == 'emb':\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += embedDictMaster[emb][row['word1_kg_id']].tolist() + embedDictMaster[emb][row['word2_kg_id']].tolist()\n",
-    "                else:\n",
-    "                    tempX += newEmbedDictMaster[caseName][row['word1_kg_id']].tolist() + newEmbedDictMaster[caseName][row['word2_kg_id']].tolist()\n",
-    "            X.append(tempX)\n",
-    "    else:\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += [(abs(cosine_similarity(embedDictMaster[emb][row['word1_kg_id']].reshape(1,-1), embedDictMaster[emb][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "                else:\n",
-    "                    tempX += [(abs(cosine_similarity(newEmbedDictMaster[caseName][row['word1_kg_id']].reshape(1,-1), newEmbedDictMaster[caseName][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "            X.append(tempX)\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    X = pd.DataFrame(X)\n",
-    "    Y = (wordSim353AnnotDF_New_Merged_DF['Avg'] - 1) / 3\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(np_label_samples(pred * 3 + 1), np_label_samples(Y_test1 * 3 + 1))\n",
-    "#         acc += mean_squared_error(pred * 3 + 1, Y_test1 * 3 + 1)\n",
-    "\n",
-    "    return caseName, *list(case.values()), acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "circular-marine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0704145482e94140a701edcd9d8c0a6f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3024 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 17min 14s, sys: 18.4 s, total: 17min 32s\n",
-      "Wall time: 17min 26s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindRegressionAccuracyCombs)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCombCasesList))\n",
-    "resDF = pd.DataFrame(results, columns=['caseName','basis','emb_comb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy'])\n",
-    "resDF['emb_comb'] = resDF['emb_comb'].apply(lambda p: \" & \".join(p))\n",
-    "resDF.to_csv('../data/retrofitting/retro_SVM_Reg_comb_test_'+ today_date +'.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "moderate-fashion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "centered-concentration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldEmbedDictKey = 'transe'\n",
-    "newEmbedDictKey = 'transe_19k_childPar_1_weighted'\n",
-    "wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: 4 - 3 * abs(cosine_similarity(np.array(embedDictMaster[oldEmbedDictKey][p['word1_kg_id']]).reshape(1,-1), np.array(embedDictMaster[oldEmbedDictKey][p['word2_kg_id']]).reshape(1,-1))[0][0]) if p['word1_kg_id'] in embedDictMaster[oldEmbedDictKey] and p['word2_kg_id'] in embedDictMaster[oldEmbedDictKey] else -1, axis=1)\n",
-    "wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: 4 - 3 * abs(cosine_similarity(np.array(newEmbedDictMaster[newEmbedDictKey][p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDictMaster[newEmbedDictKey][p['word2_kg_id']]).reshape(1,-1))[0][0]) if p['word1_kg_id'] in embedDictMaster[oldEmbedDictKey] and p['word2_kg_id'] in embedDictMaster[oldEmbedDictKey] else -1, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New['old_diff'] = abs(wordSim353AnnotDF_New['textOld'] - wordSim353AnnotDF_New['Avg'])\n",
-    "wordSim353AnnotDF_New['new_diff'] = abs(wordSim353AnnotDF_New['textNew'] - wordSim353AnnotDF_New['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.506079\n",
-       "std        0.388162\n",
-       "min        0.000000\n",
-       "25%        0.201136\n",
-       "50%        0.430653\n",
-       "75%        0.744341\n",
-       "max        1.969069\n",
-       "Name: old_diff, dtype: float64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.sort_values(by=['old_diff'],ascending=False)['old_diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.587435\n",
-       "std        0.440362\n",
-       "min        0.000000\n",
-       "25%        0.237281\n",
-       "50%        0.494043\n",
-       "75%        0.880977\n",
-       "max        1.900974\n",
-       "Name: new_diff, dtype: float64"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.sort_values(by=['new_diff'],ascending=False)['new_diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.506079:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.587435:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New['Old_Diff_Cat'] = wordSim353AnnotDF_New['old_diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New['New_Diff_Cat'] = wordSim353AnnotDF_New['new_diff'].apply(determineFarNessOfNew)\n",
-    "wordSim353AnnotDF_New['correct_category'] = wordSim353AnnotDF_New.Avg.apply(label_samples)\n",
-    "wordSim353AnnotDF_New['old_predicted_category'] = wordSim353AnnotDF_New.textOld.apply(label_samples)\n",
-    "wordSim353AnnotDF_New['new_predicted_category'] = wordSim353AnnotDF_New.textNew.apply(label_samples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New[['Word 1', 'Word 2', 'Avg', 'textOld', 'textNew', 'old_diff', 'new_diff', 'Old_Diff_Cat', 'New_Diff_Cat', 'correct_category', 'old_predicted_category', 'new_predicted_category']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>textOld</th>\n",
-       "      <th>textNew</th>\n",
-       "      <th>old_diff</th>\n",
-       "      <th>new_diff</th>\n",
-       "      <th>Old_Diff_Cat</th>\n",
-       "      <th>New_Diff_Cat</th>\n",
-       "      <th>correct_category</th>\n",
-       "      <th>old_predicted_category</th>\n",
-       "      <th>new_predicted_category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.909414</td>\n",
-       "      <td>3.837159</td>\n",
-       "      <td>0.309414</td>\n",
-       "      <td>0.237159</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.646852</td>\n",
-       "      <td>3.593611</td>\n",
-       "      <td>0.046852</td>\n",
-       "      <td>0.006389</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.470644</td>\n",
-       "      <td>3.470644</td>\n",
-       "      <td>0.129356</td>\n",
-       "      <td>0.129356</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>3.674395</td>\n",
-       "      <td>3.625026</td>\n",
-       "      <td>0.674395</td>\n",
-       "      <td>0.625026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.514591</td>\n",
-       "      <td>2.514591</td>\n",
-       "      <td>0.314591</td>\n",
-       "      <td>0.314591</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.877362</td>\n",
-       "      <td>2.476802</td>\n",
-       "      <td>0.122638</td>\n",
-       "      <td>0.523198</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.544676</td>\n",
-       "      <td>3.078550</td>\n",
-       "      <td>0.255324</td>\n",
-       "      <td>0.721450</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.443100</td>\n",
-       "      <td>2.075806</td>\n",
-       "      <td>0.556900</td>\n",
-       "      <td>0.924194</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>3.666691</td>\n",
-       "      <td>3.692986</td>\n",
-       "      <td>1.866691</td>\n",
-       "      <td>1.892986</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>3.202513</td>\n",
-       "      <td>2.677294</td>\n",
-       "      <td>0.547487</td>\n",
-       "      <td>1.072706</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg   textOld   textNew  old_diff  new_diff  \\\n",
-       "0     Arafat          peace  3.60  3.909414  3.837159  0.309414  0.237159   \n",
-       "1     Arafat         terror  3.60  3.646852  3.593611  0.046852  0.006389   \n",
-       "2        FBI    fingerprint  3.60  3.470644  3.470644  0.129356  0.129356   \n",
-       "3        FBI  investigation  3.00  3.674395  3.625026  0.674395  0.625026   \n",
-       "4    Harvard           Yale  2.20  2.514591  2.514591  0.314591  0.314591   \n",
-       "..       ...            ...   ...       ...       ...       ...       ...   \n",
-       "339      war         troops  3.00  2.877362  2.476802  0.122638  0.523198   \n",
-       "340   weapon         secret  3.80  3.544676  3.078550  0.255324  0.721450   \n",
-       "341  weather       forecast  3.00  2.443100  2.075806  0.556900  0.924194   \n",
-       "342     wood         forest  1.80  3.666691  3.692986  1.866691  1.892986   \n",
-       "343     word     similarity  3.75  3.202513  2.677294  0.547487  1.072706   \n",
-       "\n",
-       "     Old_Diff_Cat  New_Diff_Cat correct_category old_predicted_category  \\\n",
-       "0               0             0                U                      U   \n",
-       "1               0             0                U                      U   \n",
-       "2               0             0                U                      M   \n",
-       "3               1             1                M                      U   \n",
-       "4               0             0                M                      M   \n",
-       "..            ...           ...              ...                    ...   \n",
-       "339             0             0                M                      M   \n",
-       "340             0             1                U                      U   \n",
-       "341             1             1                M                      M   \n",
-       "342             1             1                M                      U   \n",
-       "343             1             1                U                      M   \n",
-       "\n",
-       "    new_predicted_category  \n",
-       "0                        U  \n",
-       "1                        U  \n",
-       "2                        M  \n",
-       "3                        U  \n",
-       "4                        M  \n",
-       "..                     ...  \n",
-       "339                      M  \n",
-       "340                      M  \n",
-       "341                      M  \n",
-       "342                      U  \n",
-       "343                      M  \n",
-       "\n",
-       "[344 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_predicted_category'] != wordSim353AnnotDF_New_Merged_DF['correct_category']].to_csv('../data/transEmb/badBefore_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_predicted_category'] != wordSim353AnnotDF_New_Merged_DF['correct_category']].to_csv('../data/transEmb/badAfter_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "worldwide-genius",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "developing-option",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "catholic-compensation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.013016090623183913, 0.01114268771757639, -0...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.01327496060589727, 0.010965634890657862, -0...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626159341165, 0.005047111630696947, -0...</td>\n",
-       "      <td>[0.010410829393995813, 0.014776997996557633, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "expanded-uniform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
-       "      <th>text_2props_19k_2_new_cosSim</th>\n",
-       "      <th>complex_19k_old_cosSim</th>\n",
-       "      <th>complex_19k_2_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>abstract_19k_old_cosSim</th>\n",
-       "      <th>abstract_19k_2_new_cosSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>transe_probase_19k_old_cosSim</th>\n",
-       "      <th>transe_probase_19k_1_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "indian-embassy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "vocational-nurse",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(label_samples), wordSim353AnnotDF_New_Merged_DF[col].apply(label_samples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(label_samples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(label_samples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
-       "18     0.008403  2      \n",
-       "60    -0.302521  2      \n",
-       "12350 -1.470588  8      \n",
-       "12329 -0.899160  8      \n",
-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
-      ]
-     },
-     "execution_count": 328,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "id": "federal-timing",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5318</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.394958</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>1.151261</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>68.630252</td>\n",
-       "      <td>71.521008</td>\n",
-       "      <td>2.890756</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.168067</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8035</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.159664</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
-      ]
-     },
-     "execution_count": 329,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "id": "known-basement",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1041</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>69.193277</td>\n",
-       "      <td>2.907563</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2828</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.613445</td>\n",
-       "      <td>2.899160</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>68.630252</td>\n",
-       "      <td>71.521008</td>\n",
-       "      <td>2.890756</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9440</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>66.563025</td>\n",
-       "      <td>69.184874</td>\n",
-       "      <td>2.621849</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2701</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1035</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>2.579832</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "1041  2.907563  4      \n",
-       "2828  2.899160  5      \n",
-       "51    2.890756  2      \n",
-       "9440  2.621849  7      \n",
-       "2701  2.613445  5      \n",
-       "1035  2.613445  4      \n",
-       "1030  2.613445  4      \n",
-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "id": "private-conditions",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>70.092437</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.159664</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
-       "      <td>70.672269</td>\n",
-       "      <td>0.579832</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>67.470588</td>\n",
-       "      <td>67.176471</td>\n",
-       "      <td>-0.294118</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>71.260504</td>\n",
-       "      <td>0.596639</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.025210</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13048</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>70.352941</td>\n",
-       "      <td>6.100840</td>\n",
-       "      <td>9</td>\n",
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-       "      <th>13084</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>4.957983</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>14022</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>64.529412</td>\n",
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-       "      <td>9</td>\n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>64.243697</td>\n",
-       "      <td>68.890756</td>\n",
-       "      <td>4.647059</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>7551</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.722689</td>\n",
-       "      <td>4.630252</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.436975</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>7570</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.428571</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
-       "7551   4.630252  7      \n",
-       "13058  4.352941  9      \n",
-       "6500   4.344538  7      \n",
-       "7537   4.344538  7      \n",
-       "7570   4.336134  7      \n",
-       "6499   4.336134  7      "
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "id": "binary-committee",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>70.630252</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "308px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Evaluation Datasets.ipynb b/Evaluation Datasets.ipynb
deleted file mode 100644
index 67f4871..0000000
--- a/Evaluation Datasets.ipynb	
+++ /dev/null
@@ -1,2262 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ultimate-subscriber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "import gzip\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot\n",
-    "from itertools import combinations\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "democratic-memorial",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "ALL_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/claims.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "covered-flashing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikidata-20201208 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1qbbgjo7pddMdDvQzOSeSaL6lYwj_f5gi\n",
-    "SITELINKS_FILE_V2 = \"../source_dataset_files/wikidata-20201208/sitelinks.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "improving-prototype",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_SOURCE_FILE = \"../data/evaluation/source_files/wordsim353_with_r3.csv\"\n",
-    "WORDSIM_FINAL_FILE = \"../data/evaluation/wordsim353_with_r3.csv\"\n",
-    "\n",
-    "WORDSIM_OLD_SOURCE_FILE = \"../data/evaluation/source_files/wordsim_old.csv\"\n",
-    "WORDSIM_OLD_FINAL_FILE = \"../data/evaluation/wordsim_old.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "immune-blend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DBPEDIA_MC_30_SOURCE_FILE = \"../data/evaluation/source_files/mc-30_DBpedia.csv\"\n",
-    "DBPEDIA_MC_30_FINAL_FILE = \"../data/evaluation/mc-30_DBpedia.csv\"\n",
-    "\n",
-    "DBPEDIA_RG_65_SOURCE_FILE = \"../data/evaluation/source_files/rg-65_DBpedia.csv\"\n",
-    "DBPEDIA_RG_65_FINAL_FILE = \"../data/evaluation/rg-65_DBpedia.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "perceived-robert",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CONCEPTNET_SOURCE_FILE = \"../data/evaluation/source_files/kgtk_conceptnet.tsv\"\n",
-    "CONCEPTNET_INTERM_FILE = \"../data/evaluation/intermediate_files/kgtk_conceptnet.csv\"\n",
-    "CONCEPTNET_FINAL_FILE = \"../data/evaluation/kgtk_conceptnet_final.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-liberty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WIKI_CS_SOURCE_FILE = '../data/evaluation/source_files/wikidata-cs-20200504.tsv'\n",
-    "WIKI_CS_INTERM_FILE = '../data/evaluation/intermediate_files/wikidata-cs_categorized.csv'\n",
-    "WIKI_CS_FINAL_FILE = '../data/evaluation/wikidata-cs_final.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "annual-producer",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "automatic-rotation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def uniformize_dataset(wikiDF):\n",
-    "    needed_size = len(wikiDF[wikiDF.category == 'I'])\n",
-    "    M_sampled_set = wikiDF[wikiDF.category == 'M'].sample(needed_size, random_state=13)\n",
-    "    subset = wikiDF[wikiDF.category == 'M']\n",
-    "    mainset = wikiDF\n",
-    "    \n",
-    "    mainset_pairs = set([(row['word1_kg_id'], row['word2_kg_id']) for _,row in mainset.iterrows()])\n",
-    "    nodes = set([row['word1_kg_id'] for _,row in subset.iterrows()] \n",
-    "            + [row['word2_kg_id'] for _,row in subset.iterrows()])\n",
-    "    node_label_mappings = {row['word1_kg_id']: row['node1;label'] for _, row in subset.iterrows()}\n",
-    "    node_label_mappings.update({row['word2_kg_id']: row['node2;label'] for _, row in subset.iterrows()})\n",
-    "    \n",
-    "    all_pairs = set([pair for pair in random.sample(list(combinations(list(nodes), 2)), 10*needed_size)])\n",
-    "    removed_pairs = set()\n",
-    "\n",
-    "    first_line = True\n",
-    "    \n",
-    "    with gzip.open(ALL_EDGES_FILE, 'r') as claims_file:\n",
-    "        for line in tqdm(claims_file, total=491297976): #, total=1034927835):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            curr_pair = (line[1], line[3])\n",
-    "            if curr_pair in all_pairs:\n",
-    "                removed_pairs.add(curr_pair)\n",
-    "    newDF = []\n",
-    "    count = 0\n",
-    "    \n",
-    "    for pair in all_pairs - removed_pairs - mainset_pairs:\n",
-    "        if pair is None or pair[0] is None or pair[1] is None:\n",
-    "            continue\n",
-    "        try:\n",
-    "            newDF.append((pair[0], pair[1], 'U', node_label_mappings.get(pair[0],\"\"), node_label_mappings.get(pair[1],\"\"), None, None, None, None, None))\n",
-    "            count += 1\n",
-    "            if count == needed_size:\n",
-    "                break\n",
-    "        except:\n",
-    "            print(f\"Error while processing pair: {pair}\")\n",
-    "            \n",
-    "    return pd.concat([\n",
-    "            wikiDF[wikiDF.category == 'I'],\n",
-    "            M_sampled_set,\n",
-    "            pd.DataFrame(newDF, columns=subset.columns)\n",
-    "        ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compact-telling",
-   "metadata": {},
-   "source": [
-    "# Wordsim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "maritime-picnic",
-   "metadata": {},
-   "source": [
-    "## New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "touched-eclipse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_df = pd.read_csv(WORDSIM_SOURCE_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "inclusive-boxing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "335"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsim_df = wordsim_df[wordsim_df.word1_kg_id != wordsim_df.word2_kg_id]\n",
-    "len(wordsim_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "unknown-arabic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "334"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsim_df = wordsim_df[~(wordsim_df.word1_kg_id + wordsim_df.word2_kg_id).duplicated()]\n",
-    "len(wordsim_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "macro-basin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_df.to_csv(WORDSIM_FINAL_FILE,index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "painted-oklahoma",
-   "metadata": {},
-   "source": [
-    "## Old"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "engaging-clause",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df = pd.read_csv(WORDSIM_OLD_SOURCE_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "dutch-occasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df['Avg'] = (10 - old_wordsim_df['Human (Mean)']) * 3 / 10 + 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "blocked-haven",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>Avg</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.33920</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "      <td>2.76250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "      <td>2.01250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "      <td>3.38125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "      <td>1.84375</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class      Avg  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  2.33920  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  2.76250  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  2.01250  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  3.38125  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  1.84375  "
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "old_wordsim_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "better-bangladesh",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    349.000000\n",
-       "mean       2.784180\n",
-       "std        0.613491\n",
-       "min        1.000000\n",
-       "25%        2.387500\n",
-       "50%        2.818750\n",
-       "75%        3.287500\n",
-       "max        3.906250\n",
-       "Name: Avg, dtype: float64"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "old_wordsim_df.Avg.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "convinced-pride",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df['category'] = old_wordsim_df.Avg.apply(lambda score: 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "robust-pennsylvania",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_wordsim_df.to_csv(WORDSIM_OLD_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "living-darwin",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occasional-parcel",
-   "metadata": {},
-   "source": [
-    "# DBPedia sourced eval datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "matched-terminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_30_df = pd.read_csv(DBPEDIA_MC_30_SOURCE_FILE, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "measured-helen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_30_df.columns = ['Word 1', 'Word 2', 'Avg']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "biological-gentleman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_65_df = pd.read_csv(DBPEDIA_RG_65_SOURCE_FILE, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "annual-france",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_65_df.columns = ['Word 1', 'Word 2', 'Avg']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "native-allocation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cvg_nodes = set(dbpedia_30_df['Word 1'].to_list() + dbpedia_30_df['Word 2'].to_list() + \\\n",
-    "                   dbpedia_65_df['Word 1'].to_list() + dbpedia_65_df['Word 2'].to_list())\n",
-    "for elem in list(cvg_nodes):\n",
-    "    cvg_nodes.add(elem.capitalize())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "historical-scott",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ebabfa2b63b4589a033281506ca7bd3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(str,\n",
-       "            {'Brother': 'Q10861465',\n",
-       "             'Cushion': 'Q10971235',\n",
-       "             'Boy': 'Q3010',\n",
-       "             'Glass': 'Q21830626',\n",
-       "             'Hill': 'Q9131144',\n",
-       "             'Tumbler': 'Q1190389',\n",
-       "             'Fruit': 'Q1364',\n",
-       "             'Car': 'Q1420',\n",
-       "             'tool': 'Q14444884',\n",
-       "             'Food': 'Q2095',\n",
-       "             'Mound': 'Q1584134',\n",
-       "             'Jewellery': 'Q161439',\n",
-       "             'Furnace': 'Q1709184',\n",
-       "             'Signature': 'Q188675',\n",
-       "             'Stove': 'Q203789',\n",
-       "             'Oracle': 'Q217123',\n",
-       "             'Rooster': 'Q2216236',\n",
-       "             'Implementation': 'Q245962',\n",
-       "             'Magician_(paranormal)': 'Q2488257',\n",
-       "             'Crane_(bird)': 'Q25365',\n",
-       "             'Autograph': 'Q2872572',\n",
-       "             'Woodland': 'Q3241565',\n",
-       "             'Tool': 'Q39546',\n",
-       "             'Cemetery': 'Q39614',\n",
-       "             'Forest': 'Q4421',\n",
-       "             'Shore': 'Q468756',\n",
-       "             'Bird': 'Q6730492',\n",
-       "             'Monk': 'Q733786',\n",
-       "             'Gemstone': 'Q83437',\n",
-       "             'Slavery': 'Q8463',\n",
-       "             'Coast': 'Q93352',\n",
-       "             'Pillow': 'Q99895'})"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "sitelinks_map = defaultdict(str)\n",
-    "with gzip.open(SITELINKS_FILE_V2) as f:\n",
-    "    first_line = False\n",
-    "    for line in tqdm(f):\n",
-    "        if first_line:\n",
-    "            first_line = True\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        if line[2] != 'wikipedia_sitelink':\n",
-    "            continue\n",
-    "        fetch_main_string = lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2]\n",
-    "        sitelink_str = fetch_main_string(line[3])\n",
-    "        if sitelink_str not in cvg_nodes:\n",
-    "            continue\n",
-    "        sitelinks_map[sitelink_str] = line[1]\n",
-    "sitelinks_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "accepting-cherry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First check if capitalized version has a qnode else check for the original one\n",
-    "fetch_qnode = lambda p: sitelinks_map[p.capitalize()] if p.capitalize() else sitelinks_map[p] if p in sitelinks_map in sitelinks_map else None\n",
-    "dbpedia_30_df['word1_kg_id'] = dbpedia_30_df['Word 1'].apply(fetch_qnode)\n",
-    "dbpedia_30_df['word2_kg_id'] = dbpedia_30_df['Word 2'].apply(fetch_qnode)\n",
-    "dbpedia_65_df['word1_kg_id'] = dbpedia_65_df['Word 1'].apply(fetch_qnode)\n",
-    "dbpedia_65_df['word2_kg_id'] = dbpedia_65_df['Word 2'].apply(fetch_qnode)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "interim-spray",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_30_df['Avg'] = dbpedia_30_df.Avg.apply(lambda p: 4 - 3 * p / 5)\n",
-    "dbpedia_65_df['Avg'] = dbpedia_65_df.Avg.apply(lambda p: 4 - 3 * p / 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "charming-perry",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Gemstone</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>1.696</td>\n",
-       "      <td>Q83437</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>1.780</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q468756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>furnace</td>\n",
-       "      <td>stove</td>\n",
-       "      <td>2.134</td>\n",
-       "      <td>Q1709184</td>\n",
-       "      <td>Q203789</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Word 1     Word 2    Avg word1_kg_id word2_kg_id\n",
-       "0  Gemstone  Jewellery  1.696      Q83437     Q161439\n",
-       "1     coast      shore  1.780      Q93352     Q468756\n",
-       "2   furnace      stove  2.134    Q1709184     Q203789"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbpedia_30_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "southwest-overview",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Gemstone</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>1.696</td>\n",
-       "      <td>Q83437</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>1.780</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q468756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>furnace</td>\n",
-       "      <td>stove</td>\n",
-       "      <td>2.134</td>\n",
-       "      <td>Q1709184</td>\n",
-       "      <td>Q203789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>food</td>\n",
-       "      <td>fruit</td>\n",
-       "      <td>2.152</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q1364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>bird</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>2.170</td>\n",
-       "      <td>Q6730492</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>bird</td>\n",
-       "      <td>Crane_(bird)</td>\n",
-       "      <td>2.218</td>\n",
-       "      <td>Q6730492</td>\n",
-       "      <td>Q25365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>tool</td>\n",
-       "      <td>implementation</td>\n",
-       "      <td>2.230</td>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>Q245962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>brother</td>\n",
-       "      <td>monk</td>\n",
-       "      <td>2.308</td>\n",
-       "      <td>Q10861465</td>\n",
-       "      <td>Q733786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>monk</td>\n",
-       "      <td>oracle</td>\n",
-       "      <td>3.340</td>\n",
-       "      <td>Q733786</td>\n",
-       "      <td>Q217123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>cemetery</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.430</td>\n",
-       "      <td>Q39614</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>food</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.466</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>hill</td>\n",
-       "      <td>3.478</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q9131144</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>shore</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.622</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>monk</td>\n",
-       "      <td>Slavery</td>\n",
-       "      <td>3.670</td>\n",
-       "      <td>Q733786</td>\n",
-       "      <td>Q8463</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3.748</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q4421</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>glass</td>\n",
-       "      <td>magician_(paranormal)</td>\n",
-       "      <td>3.934</td>\n",
-       "      <td>Q21830626</td>\n",
-       "      <td>Q2488257</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1                 Word 2    Avg word1_kg_id word2_kg_id\n",
-       "0   Gemstone              Jewellery  1.696      Q83437     Q161439\n",
-       "1      coast                  shore  1.780      Q93352     Q468756\n",
-       "2    furnace                  stove  2.134    Q1709184     Q203789\n",
-       "3       food                  fruit  2.152       Q2095       Q1364\n",
-       "4       bird                rooster  2.170    Q6730492    Q2216236\n",
-       "5       bird           Crane_(bird)  2.218    Q6730492      Q25365\n",
-       "6       tool         implementation  2.230      Q39546     Q245962\n",
-       "7    brother                   monk  2.308   Q10861465     Q733786\n",
-       "8       monk                 oracle  3.340     Q733786     Q217123\n",
-       "9   cemetery               woodland  3.430      Q39614    Q3241565\n",
-       "10      food                rooster  3.466       Q2095    Q2216236\n",
-       "11     coast                   hill  3.478      Q93352    Q9131144\n",
-       "12     shore               woodland  3.622     Q468756    Q3241565\n",
-       "13      monk                Slavery  3.670     Q733786       Q8463\n",
-       "14     coast                 forest  3.748      Q93352       Q4421\n",
-       "15     glass  magician_(paranormal)  3.934   Q21830626    Q2488257"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbpedia_30_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "generous-hayes",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Gemstone</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>Q83437</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>cushion</td>\n",
-       "      <td>pillow</td>\n",
-       "      <td>1.696</td>\n",
-       "      <td>Q10971235</td>\n",
-       "      <td>Q99895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Implementation</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>1.804</td>\n",
-       "      <td>Q245962</td>\n",
-       "      <td>Q39546</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Word 1     Word 2    Avg word1_kg_id word2_kg_id\n",
-       "0        Gemstone  Jewellery  1.636      Q83437     Q161439\n",
-       "1         cushion     pillow  1.696   Q10971235      Q99895\n",
-       "2  Implementation       tool  1.804     Q245962      Q39546"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbpedia_65_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "outdoor-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Gemstone</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>Q83437</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>cushion</td>\n",
-       "      <td>pillow</td>\n",
-       "      <td>1.696</td>\n",
-       "      <td>Q10971235</td>\n",
-       "      <td>Q99895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Implementation</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>1.804</td>\n",
-       "      <td>Q245962</td>\n",
-       "      <td>Q39546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>forest</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>1.810</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>1.840</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q468756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>autograph</td>\n",
-       "      <td>signature</td>\n",
-       "      <td>1.846</td>\n",
-       "      <td>Q2872572</td>\n",
-       "      <td>Q188675</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>glass</td>\n",
-       "      <td>tumbler</td>\n",
-       "      <td>1.930</td>\n",
-       "      <td>Q21830626</td>\n",
-       "      <td>Q1190389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>hill</td>\n",
-       "      <td>mound</td>\n",
-       "      <td>2.026</td>\n",
-       "      <td>Q9131144</td>\n",
-       "      <td>Q1584134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>furnace</td>\n",
-       "      <td>stove</td>\n",
-       "      <td>2.134</td>\n",
-       "      <td>Q1709184</td>\n",
-       "      <td>Q203789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>brother</td>\n",
-       "      <td>monk</td>\n",
-       "      <td>2.356</td>\n",
-       "      <td>Q10861465</td>\n",
-       "      <td>Q733786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>food</td>\n",
-       "      <td>fruit</td>\n",
-       "      <td>2.386</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q1364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>bird</td>\n",
-       "      <td>Rooster</td>\n",
-       "      <td>2.422</td>\n",
-       "      <td>Q6730492</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>bird</td>\n",
-       "      <td>Crane_(bird)</td>\n",
-       "      <td>2.422</td>\n",
-       "      <td>Q6730492</td>\n",
-       "      <td>Q25365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Magician_(paranormal)</td>\n",
-       "      <td>oracle</td>\n",
-       "      <td>2.908</td>\n",
-       "      <td>Q2488257</td>\n",
-       "      <td>Q217123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>glass</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>2.932</td>\n",
-       "      <td>Q21830626</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>cemetery</td>\n",
-       "      <td>mound</td>\n",
-       "      <td>2.986</td>\n",
-       "      <td>Q39614</td>\n",
-       "      <td>Q1584134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>hill</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.112</td>\n",
-       "      <td>Q9131144</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Crane_(bird)</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.154</td>\n",
-       "      <td>Q25365</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>furnace</td>\n",
-       "      <td>Implementation</td>\n",
-       "      <td>3.178</td>\n",
-       "      <td>Q1709184</td>\n",
-       "      <td>Q245962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>hill</td>\n",
-       "      <td>3.244</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q9131144</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>bird</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.256</td>\n",
-       "      <td>Q6730492</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>cemetery</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.292</td>\n",
-       "      <td>Q39614</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>food</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.346</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>mound</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>3.418</td>\n",
-       "      <td>Q1584134</td>\n",
-       "      <td>Q468756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>car</td>\n",
-       "      <td>cushion</td>\n",
-       "      <td>3.418</td>\n",
-       "      <td>Q1420</td>\n",
-       "      <td>Q10971235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>monk</td>\n",
-       "      <td>oracle</td>\n",
-       "      <td>3.454</td>\n",
-       "      <td>Q733786</td>\n",
-       "      <td>Q217123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>shore</td>\n",
-       "      <td>woodland</td>\n",
-       "      <td>3.460</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>Q3241565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>coast</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3.490</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q4421</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>monk</td>\n",
-       "      <td>slavery</td>\n",
-       "      <td>3.658</td>\n",
-       "      <td>Q733786</td>\n",
-       "      <td>Q8463</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>cushion</td>\n",
-       "      <td>Jewellery</td>\n",
-       "      <td>3.730</td>\n",
-       "      <td>Q10971235</td>\n",
-       "      <td>Q161439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>boy</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.736</td>\n",
-       "      <td>Q3010</td>\n",
-       "      <td>Q2216236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>glass</td>\n",
-       "      <td>Magician_(paranormal)</td>\n",
-       "      <td>3.736</td>\n",
-       "      <td>Q21830626</td>\n",
-       "      <td>Q2488257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>mound</td>\n",
-       "      <td>stove</td>\n",
-       "      <td>3.916</td>\n",
-       "      <td>Q1584134</td>\n",
-       "      <td>Q203789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>fruit</td>\n",
-       "      <td>furnace</td>\n",
-       "      <td>3.970</td>\n",
-       "      <td>Q1364</td>\n",
-       "      <td>Q1709184</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   Word 1                 Word 2    Avg word1_kg_id  \\\n",
-       "0                Gemstone              Jewellery  1.636      Q83437   \n",
-       "1                 cushion                 pillow  1.696   Q10971235   \n",
-       "2          Implementation                   tool  1.804     Q245962   \n",
-       "3                  forest               woodland  1.810       Q4421   \n",
-       "4                   coast                  shore  1.840      Q93352   \n",
-       "5               autograph              signature  1.846    Q2872572   \n",
-       "6                   glass                tumbler  1.930   Q21830626   \n",
-       "7                    hill                  mound  2.026    Q9131144   \n",
-       "8                 furnace                  stove  2.134    Q1709184   \n",
-       "9                 brother                   monk  2.356   Q10861465   \n",
-       "10                   food                  fruit  2.386       Q2095   \n",
-       "11                   bird                Rooster  2.422    Q6730492   \n",
-       "12                   bird           Crane_(bird)  2.422    Q6730492   \n",
-       "13  Magician_(paranormal)                 oracle  2.908    Q2488257   \n",
-       "14                  glass              Jewellery  2.932   Q21830626   \n",
-       "15               cemetery                  mound  2.986      Q39614   \n",
-       "16                   hill               woodland  3.112    Q9131144   \n",
-       "17           Crane_(bird)                rooster  3.154      Q25365   \n",
-       "18                furnace         Implementation  3.178    Q1709184   \n",
-       "19                  coast                   hill  3.244      Q93352   \n",
-       "20                   bird               woodland  3.256    Q6730492   \n",
-       "21               cemetery               woodland  3.292      Q39614   \n",
-       "22                   food                rooster  3.346       Q2095   \n",
-       "23                  mound                  shore  3.418    Q1584134   \n",
-       "24                    car                cushion  3.418       Q1420   \n",
-       "25                   monk                 oracle  3.454     Q733786   \n",
-       "26                  shore               woodland  3.460     Q468756   \n",
-       "27                  coast                 forest  3.490      Q93352   \n",
-       "28                   monk                slavery  3.658     Q733786   \n",
-       "29                cushion              Jewellery  3.730   Q10971235   \n",
-       "30                    boy                rooster  3.736       Q3010   \n",
-       "31                  glass  Magician_(paranormal)  3.736   Q21830626   \n",
-       "32                  mound                  stove  3.916    Q1584134   \n",
-       "33                  fruit                furnace  3.970       Q1364   \n",
-       "\n",
-       "   word2_kg_id  \n",
-       "0      Q161439  \n",
-       "1       Q99895  \n",
-       "2       Q39546  \n",
-       "3     Q3241565  \n",
-       "4      Q468756  \n",
-       "5      Q188675  \n",
-       "6     Q1190389  \n",
-       "7     Q1584134  \n",
-       "8      Q203789  \n",
-       "9      Q733786  \n",
-       "10       Q1364  \n",
-       "11    Q2216236  \n",
-       "12      Q25365  \n",
-       "13     Q217123  \n",
-       "14     Q161439  \n",
-       "15    Q1584134  \n",
-       "16    Q3241565  \n",
-       "17    Q2216236  \n",
-       "18     Q245962  \n",
-       "19    Q9131144  \n",
-       "20    Q3241565  \n",
-       "21    Q3241565  \n",
-       "22    Q2216236  \n",
-       "23     Q468756  \n",
-       "24   Q10971235  \n",
-       "25     Q217123  \n",
-       "26    Q3241565  \n",
-       "27       Q4421  \n",
-       "28       Q8463  \n",
-       "29     Q161439  \n",
-       "30    Q2216236  \n",
-       "31    Q2488257  \n",
-       "32     Q203789  \n",
-       "33    Q1709184  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbpedia_65_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "regulation-frequency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dbpedia_30_df.to_csv(DBPEDIA_MC_30_FINAL_FILE, index=None)\n",
-    "dbpedia_65_df.to_csv(DBPEDIA_RG_65_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "measured-arbitration",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "awful-vatican",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "attended-tulsa",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collective-techno",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "personalized-chambers",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accredited-consistency",
-   "metadata": {},
-   "source": [
-    "# ConceptNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "steady-mitchell",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "beb4ee57be0a42afaedfece6dac99064",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No. of conceptNet nodes fetched:  1165190\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8bc3ec3c5db74a4eb8c6ba97d08a7a31",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "labelsMap = {}\n",
-    "with open(CONCEPTNET_SOURCE_FILE) as conceptFile:\n",
-    "    firstLine = True\n",
-    "    for line in tqdm(conceptFile):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = line.split('\\t')\n",
-    "        labelsMap[line[3][1:-1]] = None\n",
-    "        labelsMap[line[4][1:-1]] = None\n",
-    "print(\"No. of conceptNet nodes fetched: \",len(labelsMap))\n",
-    "with gzip.open(LABELS_FILE, 'r') as labelsFile:\n",
-    "    firstLine = True\n",
-    "    for line in tqdm(labelsFile):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        line[3] = line[3][1:-5]\n",
-    "        qnode, label = line[1], line[3]\n",
-    "#         print(qnode, label)\n",
-    "        if label in labelsMap:\n",
-    "            if labelsMap[label] == None:\n",
-    "                labelsMap[label] = []\n",
-    "            labelsMap[label].append(qnode)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "studied-pharmacology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchQnode(label):\n",
-    "    if pd.isna(label):\n",
-    "        return None\n",
-    "    label = label[1:-1]\n",
-    "    if label not in labelsMap:\n",
-    "        return None\n",
-    "    else:\n",
-    "        return labelsMap[label]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "imperial-genetics",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept Net dataset size reduced from 3423004 pairs to 99052 pairs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-54-009d05c37ef7>:6: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF1['node1;qnode;len'] = conceptDF1['node1;qnode'].apply(len)\n",
-      "<ipython-input-54-009d05c37ef7>:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF1['node2;qnode;len'] = conceptDF1['node2;qnode'].apply(len)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept Net dataset size reduced from 99052 pairs to 2084 pairs by keeping only pairs which have one-one mapping with qnodes\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "related to                   758\n",
-       "synonym                      708\n",
-       "form of                      219\n",
-       "etymologically related to     82\n",
-       "derived from                  77\n",
-       "is a                          63\n",
-       "has context                   45\n",
-       "at location                   29\n",
-       "antonym                       15\n",
-       "manner of                     15\n",
-       "similar to                    15\n",
-       "part of                       12\n",
-       "causes                         9\n",
-       "distinct from                  6\n",
-       "used for                       5\n",
-       "receives action                4\n",
-       "has prerequisite               4\n",
-       "genus                          2\n",
-       "has a                          2\n",
-       "instance of                    2\n",
-       "has subevent                   2\n",
-       "capital                        2\n",
-       "motivated by goal              2\n",
-       "has last subevent              1\n",
-       "has property                   1\n",
-       "causes desire                  1\n",
-       "genre                          1\n",
-       "has first subevent             1\n",
-       "language                       1\n",
-       "Name: relation;label, dtype: int64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptDF = pd.read_csv(CONCEPTNET_SOURCE_FILE, sep='\\t')\n",
-    "conceptDF['node1;qnode'] = conceptDF['node1;label'].apply(fetchQnode)\n",
-    "conceptDF['node2;qnode'] = conceptDF['node2;label'].apply(fetchQnode)\n",
-    "conceptDF1 = conceptDF[(~conceptDF['node1;qnode'].isna()) & (~conceptDF['node2;qnode'].isna())]\n",
-    "print(f\"Concept Net dataset size reduced from {len(conceptDF)} pairs to {len(conceptDF1)} pairs\")\n",
-    "conceptDF1['node1;qnode;len'] = conceptDF1['node1;qnode'].apply(len)\n",
-    "conceptDF1['node2;qnode;len'] = conceptDF1['node2;qnode'].apply(len)\n",
-    "conceptDF2 = conceptDF1[(conceptDF1['node1;qnode;len'] == 1) & (conceptDF1['node2;qnode;len'] == 1)]\n",
-    "print(f\"Concept Net dataset size reduced from {len(conceptDF1)} pairs to {len(conceptDF2)} pairs by keeping only pairs which have one-one mapping with qnodes\")\n",
-    "conceptDF2['relation;label'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "elder-final",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-55-ffe0073c31e5>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['category'] = conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M')\n",
-      "<ipython-input-55-ffe0073c31e5>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['word1_kg_id'] = conceptDF2['node1;qnode'].apply(lambda p: p[0])\n",
-      "<ipython-input-55-ffe0073c31e5>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  conceptDF2['word2_kg_id'] = conceptDF2['node2;qnode'].apply(lambda p: p[0])\n"
-     ]
-    }
-   ],
-   "source": [
-    "conceptDF2['category'] = conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M')\n",
-    "conceptDF2['relation;label'].apply(lambda p: 'I' if p == 'synonym' or p == 'similar to' else 'M').value_counts()\n",
-    "conceptDF2['word1_kg_id'] = conceptDF2['node1;qnode'].apply(lambda p: p[0])\n",
-    "conceptDF2['word2_kg_id'] = conceptDF2['node2;qnode'].apply(lambda p: p[0])\n",
-    "conceptDF2[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label', 'relation', 'relation;label', 'relation;dimension', 'source', 'sentence']].to_csv(CONCEPTNET_INTERM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "distinguished-mustang",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "18277bf62d5e4098a980327e5a94f8a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/491297976 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df = uniformize_dataset(pd.read_csv(CONCEPTNET_INTERM_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "exact-birthday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "conceptnet_uniform_df.to_csv(CONCEPTNET_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "equivalent-creek",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I    723\n",
-       "U    723\n",
-       "M    723\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "eastern-cargo",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1350</th>\n",
-       "      <td>Q29736947</td>\n",
-       "      <td>Q50414211</td>\n",
-       "      <td>I</td>\n",
-       "      <td>arco</td>\n",
-       "      <td>bowed</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[arco]] is similar to [[bowed]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1351</th>\n",
-       "      <td>Q29782278</td>\n",
-       "      <td>Q12835706</td>\n",
-       "      <td>I</td>\n",
-       "      <td>auxo</td>\n",
-       "      <td>thallo</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1352</th>\n",
-       "      <td>Q50414211</td>\n",
-       "      <td>Q29736947</td>\n",
-       "      <td>I</td>\n",
-       "      <td>bowed</td>\n",
-       "      <td>arco</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[bowed]] is similar to [[arco]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1353</th>\n",
-       "      <td>Q1146652</td>\n",
-       "      <td>Q12835706</td>\n",
-       "      <td>I</td>\n",
-       "      <td>dike</td>\n",
-       "      <td>thallo</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1354</th>\n",
-       "      <td>Q61504726</td>\n",
-       "      <td>Q3366001</td>\n",
-       "      <td>I</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>stripped</td>\n",
-       "      <td>/r/SimilarTo</td>\n",
-       "      <td>similar to</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[empty]] is similar to [[stripped]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025</th>\n",
-       "      <td>Q190527</td>\n",
-       "      <td>Q2655953</td>\n",
-       "      <td>I</td>\n",
-       "      <td>value</td>\n",
-       "      <td>prize</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[value]] is a synonym of [[prize]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2037</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>P3828</td>\n",
-       "      <td>I</td>\n",
-       "      <td>weary</td>\n",
-       "      <td>aweary</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[weary]] is a synonym of [[aweary]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2038</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>Q29713327</td>\n",
-       "      <td>I</td>\n",
-       "      <td>weary</td>\n",
-       "      <td>tire</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[weary]] is a synonym of [[tire]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2045</th>\n",
-       "      <td>Q103859907</td>\n",
-       "      <td>Q213449</td>\n",
-       "      <td>I</td>\n",
-       "      <td>whipping</td>\n",
-       "      <td>beating</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[whipping]] is a synonym of [[beating]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2059</th>\n",
-       "      <td>Q1128240</td>\n",
-       "      <td>Q97138958</td>\n",
-       "      <td>I</td>\n",
-       "      <td>years</td>\n",
-       "      <td>days</td>\n",
-       "      <td>/r/Synonym</td>\n",
-       "      <td>synonym</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>[[years]] is a synonym of [[days]]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>76 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     word1_kg_id word2_kg_id category node1;label node2;label      relation  \\\n",
-       "1350   Q29736947   Q50414211        I        arco       bowed  /r/SimilarTo   \n",
-       "1351   Q29782278   Q12835706        I        auxo      thallo  /r/SimilarTo   \n",
-       "1352   Q50414211   Q29736947        I       bowed        arco  /r/SimilarTo   \n",
-       "1353    Q1146652   Q12835706        I        dike      thallo  /r/SimilarTo   \n",
-       "1354   Q61504726    Q3366001        I       empty    stripped  /r/SimilarTo   \n",
-       "...          ...         ...      ...         ...         ...           ...   \n",
-       "2025     Q190527    Q2655953        I       value       prize    /r/Synonym   \n",
-       "2037    Q1128240       P3828        I       weary      aweary    /r/Synonym   \n",
-       "2038    Q1128240   Q29713327        I       weary        tire    /r/Synonym   \n",
-       "2045  Q103859907     Q213449        I    whipping     beating    /r/Synonym   \n",
-       "2059    Q1128240   Q97138958        I       years        days    /r/Synonym   \n",
-       "\n",
-       "     relation;label relation;dimension source  \\\n",
-       "1350     similar to                NaN     CN   \n",
-       "1351     similar to                NaN     CN   \n",
-       "1352     similar to                NaN     CN   \n",
-       "1353     similar to                NaN     CN   \n",
-       "1354     similar to                NaN     CN   \n",
-       "...             ...                ...    ...   \n",
-       "2025        synonym                NaN     CN   \n",
-       "2037        synonym                NaN     CN   \n",
-       "2038        synonym                NaN     CN   \n",
-       "2045        synonym                NaN     CN   \n",
-       "2059        synonym                NaN     CN   \n",
-       "\n",
-       "                                      sentence  \n",
-       "1350          [[arco]] is similar to [[bowed]]  \n",
-       "1351                                       NaN  \n",
-       "1352          [[bowed]] is similar to [[arco]]  \n",
-       "1353                                       NaN  \n",
-       "1354      [[empty]] is similar to [[stripped]]  \n",
-       "...                                        ...  \n",
-       "2025       [[value]] is a synonym of [[prize]]  \n",
-       "2037      [[weary]] is a synonym of [[aweary]]  \n",
-       "2038        [[weary]] is a synonym of [[tire]]  \n",
-       "2045  [[whipping]] is a synonym of [[beating]]  \n",
-       "2059        [[years]] is a synonym of [[days]]  \n",
-       "\n",
-       "[76 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conceptnet_uniform_df[(conceptnet_uniform_df.category == 'I') & (conceptnet_uniform_df.word1_kg_id != conceptnet_uniform_df.word2_kg_id)].head(100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "structural-league",
-   "metadata": {},
-   "source": [
-    "# Wiki-CS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "incomplete-range",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     72707\n",
-       "meronymy         6886\n",
-       "context          5541\n",
-       "distinctness     4934\n",
-       "utility          2243\n",
-       "antonymy         2184\n",
-       "ordering         2107\n",
-       "material         1426\n",
-       "synonymy         1070\n",
-       "properties       1049\n",
-       "derivation        540\n",
-       "causation         510\n",
-       "similarity        345\n",
-       "attribution       187\n",
-       "other              42\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF = pd.read_csv(WIKI_CS_SOURCE_FILE,sep='\\t')\n",
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])\n",
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')\n",
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "practical-credits",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100701\n",
-       "I      1070\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "agreed-strand",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]\n",
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "secondary-episode",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})\n",
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label', 'relation', 'relation;label', 'relation;dimension', 'source', 'sentence']].to_csv(WIKI_CS_INTERM_FILE,index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "artificial-fantasy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0dd5091b52c44849cf5dbfd30090864",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/491297976 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wikiCS_uniform_df = uniformize_dataset(pd.read_csv(WIKI_CS_INTERM_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "informative-section",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiCS_uniform_df.to_csv(WIKI_CS_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "strong-romance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    1415\n",
-       "I    1415\n",
-       "U    1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiCS_uniform_df.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "discrete-theory",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "convinced-intro",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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-   "base_numbering": 1,
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-   "toc_position": {
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-    "left": "10px",
-    "top": "150px",
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-   "toc_window_display": true
-  },
-  "varInspector": {
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-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
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-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
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-    "module",
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diff --git a/Master P279 Dataset - Subpart.ipynb b/Master P279 Dataset - Subpart.ipynb
deleted file mode 100644
index 7132a4a..0000000
--- a/Master P279 Dataset - Subpart.ipynb	
+++ /dev/null
@@ -1,407 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "faced-encoding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "metropolitan-feedback",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "unusual-mentor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>[-5.89544833e-01  1.19935878e-01  3.76383096e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.666525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>[-6.08038664e-01  5.00908047e-02  5.60190260e-...</td>\n",
-       "      <td>[ 1.79253891e-01  6.10661447e-01  8.33987534e-...</td>\n",
-       "      <td>0.700515</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>[-5.40582001e-01  1.56212196e-01  5.69522917e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.683245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>[-4.99146163e-01  6.76080063e-02  3.48868877e-...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.689516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>1974 fighter aircraft family by General Dynamics</td>\n",
-       "      <td>[-1.47221148e-01  9.54690054e-02  1.05311513e+...</td>\n",
-       "      <td>[ 1.79253638e-01  6.10661507e-01  8.33987653e-...</td>\n",
-       "      <td>0.603161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  index    node2                                 id      node1  \\\n",
-       "0           0      1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "1           1      2  Q100026  Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "2           2      3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "3           3      4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "4           4      5  Q100026   Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "\n",
-       "  label                                        node2_desc  \\\n",
-       "0  P279  1974 fighter aircraft family by General Dynamics   \n",
-       "1  P279  1974 fighter aircraft family by General Dynamics   \n",
-       "2  P279  1974 fighter aircraft family by General Dynamics   \n",
-       "3  P279  1974 fighter aircraft family by General Dynamics   \n",
-       "4  P279  1974 fighter aircraft family by General Dynamics   \n",
-       "\n",
-       "               node1_label           node2_label  \\\n",
-       "0                    YF-16  F-16 Fighting Falcon   \n",
-       "1  F-16A/B Fighting Falcon  F-16 Fighting Falcon   \n",
-       "2  F-16C/D Fighting Falcon  F-16 Fighting Falcon   \n",
-       "3    F-16E/F Desert Falcon  F-16 Fighting Falcon   \n",
-       "4               F-16 VISTA  F-16 Fighting Falcon   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                      node2_labDesc  \\\n",
-       "0  1974 fighter aircraft family by General Dynamics   \n",
-       "1  1974 fighter aircraft family by General Dynamics   \n",
-       "2  1974 fighter aircraft family by General Dynamics   \n",
-       "3  1974 fighter aircraft family by General Dynamics   \n",
-       "4  1974 fighter aircraft family by General Dynamics   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-5.89544833e-01  1.19935878e-01  3.76383096e-...   \n",
-       "1  [-6.08038664e-01  5.00908047e-02  5.60190260e-...   \n",
-       "2  [-5.40582001e-01  1.56212196e-01  5.69522917e-...   \n",
-       "3  [-4.99146163e-01  6.76080063e-02  3.48868877e-...   \n",
-       "4  [-1.47221148e-01  9.54690054e-02  1.05311513e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.666525  \n",
-       "1  [ 1.79253891e-01  6.10661447e-01  8.33987534e-...      0.700515  \n",
-       "2  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.683245  \n",
-       "3  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.689516  \n",
-       "4  [ 1.79253638e-01  6.10661507e-01  8.33987653e-...      0.603161  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "appropriate-understanding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = hierDF[['id','node1','label','node2']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "conceptual-scene",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDFCounts = hierDF.groupby('node2').count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "criminal-irish",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found P279 counts for 109631 parents from a dataset of size 623368\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Found P279 counts for {len(hierDFCounts)} parents from a dataset of size {len(hierDF)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "functional-moderator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDFCounts = hierDFCounts['node1'].rename({'node1': 'child_count'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "native-kernel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "node2\n",
-       "Q100026        7\n",
-       "Q1000371       1\n",
-       "Q100047       10\n",
-       "Q100052008     4\n",
-       "Q100052938     1\n",
-       "Name: node1, dtype: int64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDFCounts.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "international-mercury",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    109631.000000\n",
-       "mean          5.686056\n",
-       "std         124.365173\n",
-       "min           1.000000\n",
-       "25%           1.000000\n",
-       "50%           2.000000\n",
-       "75%           4.000000\n",
-       "max       39217.000000\n",
-       "Name: node1, dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDFCounts.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "internal-server",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDFCounts.to_csv('../data/Master_P279_dataset/hierDF_direct_P279_counts.csv')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
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diff --git a/Master P279 Emb Generation.ipynb b/Master P279 Emb Generation.ipynb
deleted file mode 100644
index ddee543..0000000
--- a/Master P279 Emb Generation.ipynb	
+++ /dev/null
@@ -1,5540 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "incorrect-routine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "import os\n",
-    "import h5py\n",
-    "import json\n",
-    "import gzip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "moderate-drunk",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# DWD V2 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1OIZegxxrs_Hv2ZhDsSO-zLVARCR60P01\n",
-    "# SITELINKS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/sitelinks.en.tsv.gz\"\n",
-    "CLAIMS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/claims.tsv.gz\"\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/descriptions.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "exceptional-funeral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikidata-20210215 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1NGtob1BFQ03sXf4yQyYvP13ly3u1Ul5u\n",
-    "# SITELINKS_FILE_V1 = \"../source_dataset_files/wikidata-20210215/sitelinks.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "compressed-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikidata-20201208 files\n",
-    "# https://drive.google.com/drive/u/3/folders/1qbbgjo7pddMdDvQzOSeSaL6lYwj_f5gi\n",
-    "SITELINKS_FILE_V2 = \"../source_dataset_files/wikidata-20201208/sitelinks.en.tsv.gz\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fiscal-appointment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Embedding Related Files\n",
-    "DBPEDIA_SHORT_ABSTRACTS_TTL = \"../data/evaluation/source_files/short-abstracts_lang=en.ttl\"\n",
-    "DBPEDIA_SHORT_ABSTRACTS_CSV = \"../data/evaluation/source_files/short-abstracts_lang=en.csv\"\n",
-    "ABSTRACTS_INTERMEDIATE_FILE = \"../data/embeddings/intermediate_files/abstracts.csv\"\n",
-    "\n",
-    "COMPLEX_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/wikidatadwd.complEx.graph-embeddings.txt\"\n",
-    "TRANSE_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/wikidatadwd.transE.graph-embeddings.txt\"\n",
-    "TEXT_EMB_SOURCE_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/text-embeddings-concatenated.tsv.gz\"\n",
-    "\n",
-    "COMPLEX_EMB_FINAL_FILE = \"../data/embeddings/complex_orig_embedding_dict.json\"\n",
-    "TRANSE_EMB_FINAL_FILE = \"../data/embeddings/transe_orig_embedding_dict.json\"\n",
-    "TEXT_EMB_FINAL_FILE = \"../data/embeddings/text_7_props_orig_embedding_dict.json\"\n",
-    "ABS_EMB_FINAL_FILE = \"../data/embeddings/abstract_orig_embedding_dict.json\"\n",
-    "ABS_FIRST_SENT_EMB_FINAL_FILE = \"../data/embeddings/abstract_first_sent_orig_embedding_dict.json\"\n",
-    "\n",
-    "LABELS_EMB_FINAL_FILE = \"../data/embeddings/labels_orig_embedding_dict.json\"\n",
-    "LABELS_DESC_EMB_FINAL_FILE = \"../data/embeddings/labels_n_desc_orig_embedding_dict.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "departmental-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# HAS Embedding Related Files\n",
-    "A_SOURCE_FILE = \"../source_dataset_files/A_walks_analysis/a_embeddings_10x10,min_count=0.kv\"\n",
-    "A_OP_FILE = \"../data/embeddings/has_a_orig_embedding_dict.json\"\n",
-    "\n",
-    "H_SOURCE_FILE = \"../source_dataset_files/H_walks_analysis/h_embeddings_5x8,min_count=21.kv\"\n",
-    "H_OP_FILE = \"../data/embeddings/has_h_orig_embedding_dict.json\"\n",
-    "\n",
-    "S_SOURCE_FILE = \"../source_dataset_files/S_walks_analysis/s_embeddings_5x10,min_count=0.kv\"\n",
-    "S_OP_FILE = \"../data/embeddings/has_s_orig_embedding_dict.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "failing-talent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_CLASS_SIM_FILE = '../data/embeddings/wordsim_class_sim.csv'\n",
-    "WORDSIM_JC_SIM_FILE = '../data/embeddings/wordsim_jc_sim.csv'\n",
-    "WORDSIM_TOP_SIM_FILE = '../data/embeddings/wordsim_top_sim.csv'\n",
-    "\n",
-    "WORDSIM_OLD_CLASS_SIM_FILE = '../data/embeddings/wordsim_old_class_sim.csv'\n",
-    "WORDSIM_OLD_JC_SIM_FILE = '../data/embeddings/wordsim_old_jc_sim.csv'\n",
-    "WORDSIM_OLD_TOP_SIM_FILE = '../data/embeddings/wordsim_old_top_sim.csv'\n",
-    "\n",
-    "DBPEDIA_MC_30_CLASS_SIM_FILE = '../data/embeddings/dbpedia_mc_30_class_sim.csv'\n",
-    "DBPEDIA_MC_30_JC_SIM_FILE = '../data/embeddings/dbpedia_mc_30_jc_sim.csv'\n",
-    "DBPEDIA_MC_30_TOP_SIM_FILE = '../data/embeddings/dbpedia_mc_30_top_sim.csv'\n",
-    "\n",
-    "DBPEDIA_RG_65_CLASS_SIM_FILE = '../data/embeddings/dbpedia_rg_65_class_sim.csv'\n",
-    "DBPEDIA_RG_65_JC_SIM_FILE = '../data/embeddings/dbpedia_rg_65_jc_sim.csv'\n",
-    "DBPEDIA_RG_65_TOP_SIM_FILE = '../data/embeddings/dbpedia_rg_65_top_sim.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "elementary-desktop",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_CHILD_PAR_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_ChildPar.all-distilroberta-v1.csv\"\n",
-    "WORDSIM_FILE = \"../data/evaluation/wordsim353_with_r3.csv\"\n",
-    "WORDSIM_OLD_FILE = \"../data/evaluation/wordsim_old.csv\"\n",
-    "DBPEDIA_MC_30_FINAL_FILE = \"../data/evaluation/mc-30_DBpedia.csv\"\n",
-    "DBPEDIA_RG_65_FINAL_FILE = \"../data/evaluation/rg-65_DBpedia.csv\"\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "noble-draft",
-   "metadata": {},
-   "source": [
-    "# Common Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "broadband-background",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "241698\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_all_nodes():\n",
-    "    \"\"\"\n",
-    "    This function generates the set of all nodes needed for execution\n",
-    "    \"\"\"\n",
-    "    p279ChildPar = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "    wordsim_df = pd.read_csv(WORDSIM_FILE)\n",
-    "    dbpedia_mc_30_df = pd.read_csv(DBPEDIA_MC_30_FINAL_FILE)\n",
-    "    dbpedia_rg_65_df = pd.read_csv(DBPEDIA_RG_65_FINAL_FILE)\n",
-    "#     wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "#     concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + dbpedia_mc_30_df['word1_kg_id'].to_list()\n",
-    "                        + dbpedia_mc_30_df['word2_kg_id'].to_list()\n",
-    "                        + dbpedia_rg_65_df['word1_kg_id'].to_list()\n",
-    "                        + dbpedia_rg_65_df['word2_kg_id'].to_list())\n",
-    "#                         + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "#                         + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "#                         + concept_net_df['word1_kg_id'].to_list()\n",
-    "#                         + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parliamentary-documentation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fillCoverage(embedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv(WORDSIM_FILE)\n",
-    "    wordSim353AnnotDF_set = set(wordSim353AnnotDF_New['word1_kg_id'].to_list() + wordSim353AnnotDF_New['word2_kg_id'].to_list())\n",
-    "    embed_size = len(embedDict[next(iter(embedDict))])\n",
-    "#     print(embed_size)\n",
-    "    count = 0\n",
-    "    for word in wordSim353AnnotDF_set:\n",
-    "        if word not in embedDict:\n",
-    "            embedDict[word] = np.zeros((embed_size))\n",
-    "            count += 1\n",
-    "    print(f\"Added {count} corrections\")\n",
-    "    return embedDict\n",
-    "\n",
-    "def deserializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = np.array(embedDict[key2])\n",
-    "    return embedDict\n",
-    "\n",
-    "def serializeEmbeddingDict(embedDict):\n",
-    "    for key2 in embedDict.keys():\n",
-    "        embedDict[key2] = embedDict[key2].tolist() if type(embedDict[key2]) != list else embedDict[key2]\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "established-brush",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels(node_set):\n",
-    "    labels_dict = {}\n",
-    "    first_line = True\n",
-    "    with gzip.open(LABELS_FILE, 'r') as labelsFile:\n",
-    "        firstLine = True\n",
-    "        for line in tqdm(labelsFile, total=41845781):\n",
-    "            if firstLine:\n",
-    "                firstLine = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split('\\t')\n",
-    "            line[3] = line[3][1:-5]\n",
-    "            qnode, label = line[1], line[3]\n",
-    "    #         print(qnode, label)\n",
-    "            if qnode in node_set:\n",
-    "                labels_dict[qnode] = label\n",
-    "    return labels_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automated-olive",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels_n_desc(node_set):\n",
-    "    labels_dict = get_labels(node_set)\n",
-    "    first_line = True\n",
-    "    with gzip.open(DESCRIPTIONS_FILE, 'r') as labelsFile:\n",
-    "        firstLine = True\n",
-    "        for line in tqdm(labelsFile, total=34700043):\n",
-    "            if firstLine:\n",
-    "                firstLine = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split('\\t')\n",
-    "            line[3] = line[3][1:-5]\n",
-    "            qnode, label = line[1], line[3]\n",
-    "    #         print(qnode, label)\n",
-    "            if qnode in node_set:\n",
-    "                if qnode in labels_dict:\n",
-    "                    labels_dict[qnode] += ' ' + label\n",
-    "                else:\n",
-    "                    raise \"Label not present\"\n",
-    "    return labels_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "driven-yeast",
-   "metadata": {},
-   "source": [
-    "# Complex + Transe Embeddings Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "sound-spain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "complex_emb_dict = json.load(open(COMPLEX_EMB_FINAL_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "correct-gentleman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "64deda8236084d79bce85a2fd249dec9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "complex_emb_dict = {}\n",
-    "with open(COMPLEX_EMB_SOURCE_FILE) as complex_file:\n",
-    "    for line in tqdm(complex_file, total=53002671):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.strip().split()\n",
-    "        if line[0] in allNodes and line[0] not in complex_emb_dict:\n",
-    "            complex_emb_dict[line[0]] = [float(elem) for elem in line[1:]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "proved-buffer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(complex_emb_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "hazardous-amazon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(complex_emb_dict, open(COMPLEX_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "industrial-paradise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transe_emb_dict = json.load(open(TRANSE_EMB_FINAL_FILE))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "needed-passion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8636f544aa484f6d9785723a4a96e83b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "transe_emb_dict = {}\n",
-    "with open(TRANSE_EMB_SOURCE_FILE) as complex_file:\n",
-    "    for line in tqdm(complex_file, total=53002671):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.strip().split()\n",
-    "        if line[0] in allNodes and line[0] not in transe_emb_dict:\n",
-    "            transe_emb_dict[line[0]] = [float(elem) for elem in line[1:]]\n",
-    "len(transe_emb_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "classified-chick",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(transe_emb_dict, open(TRANSE_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "steady-cliff",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suffering-zealand",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "brief-timer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "238889\n"
-     ]
-    }
-   ],
-   "source": [
-    "# p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "# print(len(set(p279ChildPar.node1.to_list() \n",
-    "#                         + p279ChildPar.node2.to_list())))\n",
-    "\n",
-    "# # Load complex, transe embedding files and entity names file\n",
-    "# compf = h5py.File('../data/complTrans/complEx.h5','r')\n",
-    "# transf = h5py.File('../data/complTrans/transE.h5','r')\n",
-    "# ent_names = json.load(open('../data/complTrans/entity_names_all_0.json'))\n",
-    "# allNodes = get_all_nodes()\n",
-    "# # json.dump(list(p279QnodesList), open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json', 'w'))\n",
-    "\n",
-    "# complexEmb = {qnode: emb for emb, qnode in zip(compf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "# transeEmb = {qnode: emb for emb, qnode in zip(transf['embeddings'], ent_names) if qnode in allNodes}\n",
-    "# print(f\"Out of {len(ent_names)} embeddings, retaining {len(transeEmb)} embeddings\")\n",
-    "\n",
-    "# def serialize_embedding_dict(embed_dict):\n",
-    "#     for key2 in embed_dict.keys():\n",
-    "#         embed_dict[key2] = embed_dict[key2].tolist() if type(embed_dict[key2]) != list else embed_dict[key2]\n",
-    "#     return embed_dict\n",
-    "\n",
-    "# json.dump(serialize_embedding_dict(complexEmb),open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json','w'))\n",
-    "# json.dump(serialize_embedding_dict(transeEmb),open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json','w'))\n",
-    "# # complexEmb = json.load(open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json'))\n",
-    "# # transeEmb = json.load(open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continued-locking",
-   "metadata": {},
-   "source": [
-    "# Text Embedding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "polished-divorce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f97b30760e841da87be90aebef9c8cd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "first_line = True\n",
-    "text_emb_dict = {}\n",
-    "with gzip.open(TEXT_EMB_SOURCE_FILE) as file:\n",
-    "    for line in tqdm(file):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        if line[1] == 'text_embedding' and line[0] in allNodes:\n",
-    "            text_emb_dict[line[0]] = [float(elem) for elem in line[2].split(',')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "raising-boost",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text_emb_dict, open(TEXT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "beautiful-drill",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_emb_dict = json.load(open('../data/embeddings/archived/text_7_props_orig_embedding_dict.json.old'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "impressed-stations",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a75bc167e9b449f88e4df7ebb19bca77",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/241698 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "missing_nodes = []\n",
-    "for node in tqdm(allNodes):\n",
-    "    if node not in text_emb_dict:\n",
-    "        missing_nodes.append(node)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "disabled-corporation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0db4a305d6dc4f1e85dfdc02ea564537",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "missing_nodes_set = set(missing_nodes)\n",
-    "new_file = []\n",
-    "with gzip.open(CLAIMS_FILE, 'r') as all_claims_file:\n",
-    "    firstLine = True\n",
-    "    for ogline in tqdm(all_claims_file, total=491297976):\n",
-    "        if firstLine:\n",
-    "            firstLine = False\n",
-    "            continue\n",
-    "        line = ogline.decode('utf-8').strip().split('\\t')\n",
-    "        line[3] = line[3][1:-5]\n",
-    "        qnode, label = line[1], line[3]\n",
-    "#         print(qnode, label)\n",
-    "        if qnode in missing_nodes_set:\n",
-    "            new_file.append(ogline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "complete-performer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allowed_props = set(['P31', 'P279', 'P106', 'P39', 'P1382', 'P373', 'P452'])\n",
-    "new_file1 = []\n",
-    "for line in new_file:\n",
-    "    line1 = line.decode('utf-8').strip().split('\\t')\n",
-    "    if line1[2] in allowed_props:\n",
-    "        new_file1.append(line.decode('utf-8'))\n",
-    "new_file1 = ['id\\tnode1\\tlabel\\tnode2\\trank\\tnode2;wikidatatype\\n'] + new_file1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "straight-internet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../output/text-embeddings/missing_nodes.tsv', 'w') as f:\n",
-    "    f.writelines(new_file1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "suited-boating",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#     --model sentence-transformers/roberta-large-nli-mean-tokens   \\\n",
-    "q1 = \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../output/text-embeddings/missing_nodes.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file \" + LABELS_FILE + \"  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence > ../output/text-embeddings/P279-text-embedding-7-props-missing-qnodes.tsv\"\n",
-    "os.system(q1 + \" \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "based-circuit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7_missingnodes = pd.read_csv(\"../output/text-embeddings/P279-text-embedding-7-props-missing-qnodes.tsv\", sep='\\t')\n",
-    "text7_missingnodes = text7_missingnodes[text7_missingnodes.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "imposed-series",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7_missingnodes['value'] = text7_missingnodes['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "compliant-locator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7EmbDict = {row['node']: row['value'] for _,row in text7_missingnodes.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "quick-voluntary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in text7EmbDict.keys():\n",
-    "    if key not in text_emb_dict:\n",
-    "        text_emb_dict[key] = text7EmbDict[key]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "special-smile",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text_emb_dict, open(TEXT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "infectious-mauritius",
-   "metadata": {},
-   "source": [
-    "## Old technique follows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "neural-gibson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "p279QnodesList = list(set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "instructional-weather",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "missingNodes = allNodes - set(p279ChildPar.node1.to_list() + p279ChildPar.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "specified-clear",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37038"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(missingNodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "conditional-brooks",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Split main file into sub-files for groups of properties for multi-processing\n",
-    "\n",
-    "# bsize = len(p279QnodesList) // 250\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(p279QnodesList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(p279QnodesList[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "# #     print(len(q1))\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "utility-fleet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for cnt in range(290,503):\n",
-    "    os.system(\"rm ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "alleged-strength",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fb2da21d7cf4241a3e52ac132a7c534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/38 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Split main file into sub-files for groups of properties for multi-processing\n",
-    "missingNodes = list(missingNodes)\n",
-    "bsize = 1000\n",
-    "cnt = 252\n",
-    "for i in tqdm(range(0, len(missingNodes), bsize)):\n",
-    "    q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(missingNodes[i:i+bsize]) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     print(len(q1))\n",
-    "    cnt += 1\n",
-    "#     print(q1)\n",
-    "    os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "signal-island",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def checkIfFileContainsLines(file):\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "fatal-broadway",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countFileLines(file):\n",
-    "    count = 0\n",
-    "    with open(file) as f:\n",
-    "        for line in f:\n",
-    "            count += 1\n",
-    "    return count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "separate-satin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "runCommCnt = 1\n",
-    "# 252\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 = \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(11*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "surprising-burning",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "for cnt in tqdm(range(1,290)):\n",
-    "    if countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\") != 4097:\n",
-    "        print(cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bottom-lodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from os.path import exists\n",
-    "\n",
-    "# roberta-large-nli-mean-tokens\n",
-    "runCommCnt = 0\n",
-    "for cnt in tqdm(range(252,290)):\n",
-    "    if exists(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") and countFileLines(\"../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\") == 4097:\n",
-    "        continue\n",
-    "    q1 = \"\"\n",
-    "#     if cnt % 10 == 0:\n",
-    "#         q1 += \"sleep 20m; \"\n",
-    "    q1 += \"~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "        --model sentence-transformers/all-distilroberta-v1   \\\n",
-    "        --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "        --isa-properties P31 P279   \\\n",
-    "        --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "    print(cnt)\n",
-    "    runCommCnt += 1\n",
-    "    os.system(q1 + \" &amp;\")\n",
-    "    if runCommCnt % 15 == 0:\n",
-    "        time.sleep(13*60)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "damaged-browse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "collective-april",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-1.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "decent-yorkshire",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.74755263,1.6350263,-0.73952675,1.0463063,-0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q99738027</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>night shift, work shift during nighttime hours...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.25261465,0.06285462,0.029052094,0.50796187,0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q99228502</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>avenue, thoroughfare named \\\"avenue\\\" is thoro...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98970128</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node            property  \\\n",
-       "0  Q99738027      text_embedding   \n",
-       "1  Q99738027  embedding_sentence   \n",
-       "2  Q99228502      text_embedding   \n",
-       "3  Q99228502  embedding_sentence   \n",
-       "4  Q98970128      text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  0.74755263,1.6350263,-0.73952675,1.0463063,-0....  \n",
-       "1  night shift, work shift during nighttime hours...  \n",
-       "2  0.25261465,0.06285462,0.029052094,0.50796187,0...  \n",
-       "3  avenue, thoroughfare named \\\"avenue\\\" is thoro...  \n",
-       "4  0.11887096,0.8598291,0.4446009,-0.5038472,-0.9...  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# temp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "satisfactory-speech",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text2EmbArr.append(temp)\n",
-    "text2Emb = pd.concat(text2EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unavailable-competition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "concerned-april",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text7EmbArr = []\n",
-    "for i in tqdm(range(1, 290)):\n",
-    "    if not(checkIfFileContainsLines('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv')):\n",
-    "        continue\n",
-    "    temp = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-'+str(i)+'.tsv', sep='\\t')\n",
-    "    temp = temp[temp.property == 'text_embedding']\n",
-    "    text7EmbArr.append(temp)\n",
-    "text7Emb = pd.concat(text7EmbArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "australian-enforcement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb = text2Emb[text2Emb.node.apply(lambda p: p in allNodes)]\n",
-    "text7Emb = text7Emb[text7Emb.node.apply(lambda p: p in allNodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "closed-treatment",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have 2prop text embeddings for 278467 nodes and 7prop for 277587 nodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"We have 2prop text embeddings for {len(text2Emb)} nodes and 7prop for {len(text7Emb)} nodes\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "duplicate-agency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2Emb['value'] = text2Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "text7Emb['value'] = text7Emb['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "framed-third",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text2EmbDict = {row['node']: row['value'] for _,row in text2Emb.iterrows()}\n",
-    "text7EmbDict = {row['node']: row['value'] for _,row in text7Emb.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "peaceful-andrew",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(text2EmbDict, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump(text7EmbDict, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "considered-river",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sustained-playback",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "requested-state",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suited-going",
-   "metadata": {},
-   "source": [
-    "# Abstract Embeddings Generation\n",
-    "\n",
-    "Downloaded short abstracts file from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)\n",
-    "\n",
-    "Then, we extract the abstracts file from the bz2 file using: `bzip2 -d short-abstracts_lang=en.ttl.bz2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "former-editor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0038713a1604ccb9c2e5499615fbc43",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# cnt = 0\n",
-    "# p1s = []\n",
-    "# p11s = []\n",
-    "# p2s = []\n",
-    "# lines = []\n",
-    "# with open(DBPEDIA_SHORT_ABSTRACTS_TTL, 'r', encoding='utf-8') as f:\n",
-    "#     for line in tqdm(f):\n",
-    "#         p1 = line[:line.find(\" \")]\n",
-    "#         p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "#         line = line[line.find(\" \")+1:]\n",
-    "#         p2 = line[:line.find(\" \")]\n",
-    "#         line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "#         p1s.append(p1)\n",
-    "#         p11s.append(p11)\n",
-    "#         p2s.append(p2)\n",
-    "#         lines.append(line)\n",
-    "# df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "# df1.to_csv(DBPEDIA_SHORT_ABSTRACTS_CSV)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "massive-camera",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n",
-      "From 116088 Qnodes, there are 5134 sitelink Qnodes which do not have a short abstract i.e 110954 have a short abstract\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51a5ed2cfc8e45c8815fac2f8855a3be",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/241698 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(DBPEDIA_SHORT_ABSTRACTS_CSV, skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "df1.loc[df1[df1.index.duplicated()].index]\n",
-    "sitelinksDF = pd.read_csv(SITELINKS_FILE_V2, sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in allNodes)]\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in allNodes)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}\n",
-    "\n",
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "\n",
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)\n",
-    "\n",
-    "currNodes = set(abstractsDF2.node1.tolist())\n",
-    "\n",
-    "correctedRows = []\n",
-    "for key in tqdm(allNodes):\n",
-    "    if key not in currNodes:\n",
-    "        correctedRows.append([None, None, key, None, None, None, None, None, None, labelsDict[key][1:-4] if key in labelsDict else None, descDict[key][1:-4] if key in descDict else None, None])\n",
-    "        \n",
-    "abstractsDF3 = pd.concat([abstractsDF2, pd.DataFrame(correctedRows, columns=abstractsDF2.columns)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "geographic-penguin",
-   "metadata": {},
-   "source": [
-    "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-    "There are 8637003 sitelinks present in the dataset corresponding to 8637003 unique node1s (Qxxx), 8563928 unique labels (text)\n",
-    "From 116430 Qnodes, there are 5707 sitelink Qnodes which do not have a short abstract i.e 110723 have a short abstract\n",
-    "\n",
-    "\n",
-    "\n",
-    "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-    "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n",
-    "\n",
-    "From 122585 Qnodes, there are 5661 sitelink Qnodes which do not have a short abstract i.e 116924 have a short abstract\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "genetic-sympathy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "241698\n"
-     ]
-    }
-   ],
-   "source": [
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] != '' and row['node1_desc'] != '' and not(pd.isna(row['node1_label'])) and not(pd.isna(row['node1_desc'])):\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']\n",
-    "    elif row['node1_label'] != '' and not(pd.isna(row['node1_label'])):\n",
-    "        return row['node1_label']\n",
-    "    else:\n",
-    "        return None\n",
-    "    \n",
-    "abstractsDF3['abstract'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF3['abstract_firstSent'] = abstractsDF3.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))\n",
-    "abstractsDF3 = abstractsDF3[~abstractsDF3.abstract.isna()]\n",
-    "abstractsDF3 = abstractsDF3.reset_index()\n",
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0']).reset_index()\n",
-    "abstractsDF3 = abstractsDF3.drop(columns=['level_0'])\n",
-    "print(len(abstractsDF3))\n",
-    "abstractsDF3.to_csv(ABSTRACTS_INTERMEDIATE_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wicked-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "desirable-fifteen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (1,2,4,5,7,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "241698"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2 = pd.read_csv(ABSTRACTS_INTERMEDIATE_FILE)\n",
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "administrative-wesley",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF2[abstractsDF2.abstract == (abstractsDF2.node1_label +  ' ' + abstractsDF2.node1_desc)].to_csv('../data/Master_P279_dataset/temppppp.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "crude-result",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:2')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "impressive-comedy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "coordinate-assurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc9488221a204b5e9558749419087c8c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "316.05097579956055 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3eb77d77acb549d6b3b91b3a31b7f4d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "260.16796946525574 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "absEmbSeries = getSentEmbeddings(abstractsDF2.abstract, modelName)\n",
-    "absFirstSentEmbSeries = getSentEmbeddings(abstractsDF2.abstract_firstSent, modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "valued-toilet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "absEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absEmbSeries)}\n",
-    "absFirstSentEmbDict = {node: emb.tolist() for node, emb in zip(abstractsDF2.node1.to_list(), absFirstSentEmbSeries)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "refined-belarus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(absEmbDict, open(ABS_EMB_FINAL_FILE, 'w'))\n",
-    "json.dump(absFirstSentEmbDict, open(ABS_FIRST_SENT_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "knowing-relay",
-   "metadata": {},
-   "source": [
-    "# H,A,S embeddings\n",
-    "\n",
-    "Fetched from `sita` `/data02/profiling/kgtk/entity_profiling/output/wikidata-20210215-dwd/`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "ideal-accreditation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def store_relevant_embeddings(wvec, fname):\n",
-    "    tempEmb = {key: wvec[key] for key in wvec.index_to_key if key in allNodes}\n",
-    "    print(f\"Original Length: {len(wvec.index_to_key)}, No. of keys stored: {len(tempEmb)}\")\n",
-    "    json.dump(serializeEmbeddingDict(tempEmb),open(fname, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "wireless-playing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gensim.models import KeyedVectors, Word2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "cordless-bachelor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Length: 12106870, No. of keys stored: 27876\n",
-      "Original Length: 19593942, No. of keys stored: 166201\n",
-      "Original Length: 39030788, No. of keys stored: 116993\n",
-      "CPU times: user 5min 54s, sys: 2min 1s, total: 7min 56s\n",
-      "Wall time: 18min 51s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "a_key_vec = KeyedVectors.load(A_SOURCE_FILE)\n",
-    "store_relevant_embeddings(a_key_vec, A_OP_FILE)\n",
-    "\n",
-    "h_key_vec = KeyedVectors.load(H_SOURCE_FILE)\n",
-    "store_relevant_embeddings(h_key_vec, H_OP_FILE)\n",
-    "\n",
-    "s_key_vec = KeyedVectors.load(S_SOURCE_FILE)\n",
-    "store_relevant_embeddings(s_key_vec, S_OP_FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "warming-raise",
-   "metadata": {},
-   "source": [
-    "# Label Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "decimal-timothy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_dict = get_labels(allNodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "tough-wallet",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99bb9a58eb3f4f97b94b592d389920c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "98.77918219566345 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "embs = getSentEmbeddings(pd.Series(labels_dict.values()), modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "obvious-tuning",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_emb_dict = {k:v.tolist() for k, v in (zip(labels_dict.keys(), embs))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "after-shoulder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(labels_emb_dict, open(LABELS_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "composed-journey",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "satisfied-liberal",
-   "metadata": {},
-   "source": [
-    "# Label + Desc Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "complete-office",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b13c4c43b704c5d82c81f2442265f62",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/41845781 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbffd7e813e04d80ac616f1650211f47",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/34700043 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "240aaa8b6c4746f2af9ad9480aa82141",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/242 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "203.93888425827026 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "labels_desc_dict = get_labels_n_desc(allNodes)\n",
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "embs = getSentEmbeddings(pd.Series(labels_desc_dict.values()), modelName)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "cordless-daily",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels_desc_emb_dict = {k:v.tolist() for k, v in (zip(labels_desc_dict.keys(), embs))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "light-yugoslavia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump(labels_desc_emb_dict, open(LABELS_DESC_EMB_FINAL_FILE, 'w'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "entitled-validity",
-   "metadata": {},
-   "source": [
-    "# Direct sim files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "standing-prevention",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a7b4867cf8064396b3c1bf6c23af761a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d295067e6607498e914fa6768217aba4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/334 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cf2c49678114c21ac442fb3c4d035e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/334 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(WORDSIM_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else None\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_class_sim_df['embedding_na'] = word_sim_class_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_class_sim_df['embedding_cos_sim'].fillna(word_sim_class_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_na'] = word_sim_jc_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'].fillna(word_sim_jc_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_na'] = word_sim_top_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_top_sim_df['embedding_cos_sim'].fillna(word_sim_top_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(WORDSIM_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(WORDSIM_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(WORDSIM_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "grateful-ukraine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "      <th>embedding_cos_sim</th>\n",
-       "      <th>Resp_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.982734</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.969884</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.951077</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "      <td>1.264601</td>\n",
-       "      <td>&lt;Response [200]&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ... P_Dim  Avg     Stdev  H_orig  H_reversed  word1_kg_id  word2_kg_id  \\\n",
-       "0  ...   NaN  3.6  0.547723  2.1250      7.8750       Q34211         Q454   \n",
-       "1  ...   NaN  3.6  0.547723  3.0625      6.9375       Q34211    Q13648784   \n",
-       "2  ...   NaN  3.6  0.547723  4.0625      5.9375        Q8333      Q178022   \n",
-       "3  ...     u  3.0  0.000000  5.0625      4.9375        Q8333    Q21004260   \n",
-       "4  ...     s  2.2  0.447214  4.8750      5.1250       Q13371       Q49112   \n",
-       "\n",
-       "  category embedding_cos_sim         Resp_code  \n",
-       "0        U          3.982734  <Response [200]>  \n",
-       "1        U          3.969884  <Response [200]>  \n",
-       "2        U          4.000000  <Response [200]>  \n",
-       "3        M          3.951077  <Response [200]>  \n",
-       "4        M          1.264601  <Response [200]>  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "word_sim_class_sim_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "operational-survival",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48386a6eaa0745e4a9eebbed1e61c72c",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "094ee6f3ee7f4566855755f5b33e0515",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "615d0754edee407cac17b7cb3fda9c73",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(WORDSIM_OLD_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else None\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_class_sim_df['embedding_na'] = word_sim_class_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_class_sim_df['embedding_cos_sim'].fillna(word_sim_class_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_na'] = word_sim_jc_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'].fillna(word_sim_jc_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_na'] = word_sim_top_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_top_sim_df['embedding_cos_sim'].fillna(word_sim_top_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(WORDSIM_OLD_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(WORDSIM_OLD_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(WORDSIM_OLD_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "useful-effectiveness",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4517a92d40bc4f2fac7cf08a47c047ad",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6dfb01e8813644e0b56d890be25eb955",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe45273ea1f34d9faabd5422160ddc67",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(DBPEDIA_MC_30_FINAL_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else None\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_class_sim_df['embedding_na'] = word_sim_class_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_class_sim_df['embedding_cos_sim'].fillna(word_sim_class_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_na'] = word_sim_jc_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'].fillna(word_sim_jc_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_na'] = word_sim_top_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_top_sim_df['embedding_cos_sim'].fillna(word_sim_top_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(DBPEDIA_MC_30_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(DBPEDIA_MC_30_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(DBPEDIA_MC_30_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "dressed-grove",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b986c2d80c6424f937b336ca871ac5e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/34 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4e945c056cf546ac860a80004b19ae1b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/34 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "624ccbdd27734b44840c3d7fd6878294",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/34 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "word_sim_df = pd.read_csv(DBPEDIA_RG_65_FINAL_FILE)\n",
-    "\n",
-    "def fetchSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['word1_kg_id']+\"&q2=\"+row['word2_kg_id']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['embedding_cos_sim'] = float(resp.json()['similarity']) if resp else None\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['embedding_cos_sim'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "word_sim_class_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'class') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_jc_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'jc') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "word_sim_top_sim_df = pd.DataFrame(Parallel(n_jobs=5)(delayed(fetchSim)(row, 'topsim') for _, row in tqdm(word_sim_df.iterrows(), total=len(word_sim_df))))\n",
-    "\n",
-    "word_sim_class_sim_df['embedding_cos_sim'] = word_sim_class_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_class_sim_df['embedding_na'] = word_sim_class_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_class_sim_df['embedding_cos_sim'].fillna(word_sim_class_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'] = word_sim_jc_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_jc_sim_df['embedding_na'] = word_sim_jc_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_jc_sim_df['embedding_cos_sim'].fillna(word_sim_jc_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "word_sim_top_sim_df['embedding_cos_sim'] = word_sim_top_sim_df['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p) if p is not None else p)\n",
-    "word_sim_top_sim_df['embedding_na'] = word_sim_top_sim_df['embedding_cos_sim'].isna()\n",
-    "word_sim_top_sim_df['embedding_cos_sim'].fillna(word_sim_top_sim_df['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "\n",
-    "\n",
-    "word_sim_class_sim_df.to_csv(DBPEDIA_RG_65_CLASS_SIM_FILE, index=None)\n",
-    "word_sim_jc_sim_df.to_csv(DBPEDIA_RG_65_JC_SIM_FILE, index=None)\n",
-    "word_sim_top_sim_df.to_csv(DBPEDIA_RG_65_TOP_SIM_FILE, index=None)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spatial-excerpt",
-   "metadata": {},
-   "source": [
-    "# Summary of embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "wooden-medicare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))\n",
-    "\n",
-    "complexEmb = json.load(open('../data/Master_P279_dataset/masterComplexEmb.json'))\n",
-    "transeEmb = json.load(open('../data/Master_P279_dataset/masterTranseEmb.json'))\n",
-    "\n",
-    "text2Emb = json.load(open('../data/Master_P279_dataset/text2Emb.json'))\n",
-    "text7Emb = json.load(open('../data/Master_P279_dataset/text7Emb.json'))\n",
-    "\n",
-    "abstractEmb = json.load(open('../data/Master_P279_dataset/abstractEmb.json'))\n",
-    "abstractFirstSentEmb = json.load(open('../data/Master_P279_dataset/abstractFirstSentEmb.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "distributed-magazine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json.dump({key:val for key, val in complexEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/complex_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in transeEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/transe_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text2Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_2_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in text7Emb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/text_7_props_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_orig_embedding_dict.json', 'w'))\n",
-    "json.dump({key:val for key, val in abstractFirstSentEmb.items() if key in p279QnodesList}, open('../data/Master_P279_dataset/embeddings/abstract_first_sent_orig_embedding_dict.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "disturbed-better",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "advised-cyprus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summArr = []\n",
-    "cnt = countOverlap(complexEmb, p279QnodesList)\n",
-    "summArr.append(['complex', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(transeEmb, p279QnodesList)\n",
-    "summArr.append(['transe', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text2Emb, p279QnodesList)\n",
-    "summArr.append(['text2', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(text7Emb, p279QnodesList)\n",
-    "summArr.append(['text7', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractEmb, p279QnodesList)\n",
-    "summArr.append(['abstract', cnt, cnt / len(p279QnodesList) * 100])\n",
-    "\n",
-    "cnt = countOverlap(abstractFirstSentEmb, p279QnodesList)\n",
-    "summArr.append(['abstractFirstSent', cnt, cnt / len(p279QnodesList) * 100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "czech-keeping",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "238889"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279QnodesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "employed-christmas",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>text2</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>text7</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstractFirstSent</td>\n",
-       "      <td>105828</td>\n",
-       "      <td>44.300072</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           embedding   count  Coverage Percentage\n",
-       "0            complex  238448            99.815395\n",
-       "1             transe  238448            99.815395\n",
-       "2              text2  238889           100.000000\n",
-       "3              text7  238889           100.000000\n",
-       "4           abstract  105828            44.300072\n",
-       "5  abstractFirstSent  105828            44.300072"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(summArr, columns=['embedding', 'count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changing-strategy",
-   "metadata": {},
-   "source": [
-    "# Embeddings correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "purple-raising",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedDictMaster = {}\n",
-    "subsetEmbedDictMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "round-product",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masterEmbedKeys = ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in masterEmbedKeys:\n",
-    "    masterEmbedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "metallic-insulin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subsetEmbedKeys = ['text_7props', 'text_2props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "for key1 in subsetEmbedKeys:\n",
-    "    subsetEmbedDictMaster[key1] = json.load(open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "assigned-parameter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "olympic-yemen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsim_pairs = {(row['word1_kg_id'], row['word2_kg_id']) for _, row in wordSim353AnnotDF_New.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "welcome-disorder",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by text_7props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by text_2props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by complex embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by transe embeddings created for 19k retrofitting: 342\n",
-      "Pair Coverage by abstract embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by abstract_first_sent embeddings created for 19k retrofitting: 343\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in subsetEmbedKeys:\n",
-    "    print(f\"Pair Coverage by {key1} embeddings created for 19k retrofitting: {sum([row[0] in subsetEmbedDictMaster[key1] and row[1] in subsetEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "northern-psychiatry",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by old text_7_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old text_2_props embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old complex embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old transe embeddings created for 19k retrofitting: 278\n",
-      "Pair Coverage by old abstract embeddings created for 19k retrofitting: 183\n",
-      "Pair Coverage by old abstract_first_sent embeddings created for 19k retrofitting: 183\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by old {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "contrary-casino",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_set = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "alleged-polish",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "masterEmbCorrections = defaultdict(list)\n",
-    "for node in wordSim353AnnotDF_New_set:\n",
-    "    for i in range(len(masterEmbedKeys)):\n",
-    "        if node not in masterEmbedDictMaster[masterEmbedKeys[i]] and node in wordSim353AnnotDF_New_set:\n",
-    "            masterEmbCorrections[masterEmbedKeys[i]].append(node)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "periodic-buffer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['abstract', 'abstract_first_sent', 'text_7_props', 'text_2_props', 'complex', 'transe'])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "masterEmbCorrections.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "awful-signal",
-   "metadata": {},
-   "source": [
-    "## Complex, Transe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "exceptional-acting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# correctedComplexEmb = {}\n",
-    "# correctedTranseEmb = {}\n",
-    "# for wordID in masterEmbCorrections['complex']:\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "#         correctedComplexEmb[wordID] = [float(p) for p in resp['graph_embedding_complex'].split(',')]\n",
-    "#         correctedTranseEmb[wordID] = [float(p) for p in resp['graph_embeddings_transe'].split(',')]\n",
-    "#     except:\n",
-    "#         print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "assigned-journey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51850ec9544547f293820bd9e94091f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4de00c52596f4ce3b5d17ee9ef73b068",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42575933 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1h 33min 17s, sys: 2min 38s, total: 1h 35min 56s\n",
-      "Wall time: 1h 35min 28s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "correctedComplexEmb = {qnode: emb for emb, qnode in tqdm(zip(f['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}\n",
-    "correctedTranseEmb = {qnode: emb for emb, qnode in tqdm(zip(transf['embeddings'], ent_names), total=len(ent_names)) if qnode in masterEmbCorrections['complex']}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "female-scope",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(list(correctedComplexEmb.items())[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "valuable-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(masterEmbedDictMaster['complex'][list(masterEmbedDictMaster['complex'].keys())[0]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "religious-celebration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for node, emb in correctedComplexEmb.items():\n",
-    "    masterEmbedDictMaster['complex'][node] = emb\n",
-    "for node, emb in correctedTranseEmb.items():\n",
-    "    masterEmbedDictMaster['transe'][node] = emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spatial-writer",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "periodic-laundry",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "motivated-soundtrack",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imported-advance",
-   "metadata": {},
-   "source": [
-    "## Text Embeddings Correction file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "historical-conservative",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv --word-separator '|' -p '\" + '|'.join(masterEmbCorrections['text_7_props']) + \";;' -o ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv -v True\"\n",
-    "os.system(\"screen -dm \" + q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "overhead-commission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "cooked-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "q1 = \"kgtk text-embedding -i ../data/Master_P279_dataset/output/wikidataos.for.text-embedding-ext-corrections.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279   \\\n",
-    "    --save-embedding-sentence > ../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv\"\n",
-    "#     print(q1)\n",
-    "os.system(q1 + \" &amp;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "static-failure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-7-props-part-corrections.tsv', sep='\\t')\n",
-    "corrected2Emb = pd.read_csv('../data/Master_P279_dataset/output/P279-text-embedding-2-props-part-corrections.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "spare-flexibility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corrected7Emb = corrected7Emb[corrected7Emb.property == 'text_embedding']\n",
-    "corrected7Emb['value'] = corrected7Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "\n",
-    "corrected2Emb = corrected2Emb[corrected2Emb.property == 'text_embedding']\n",
-    "corrected2Emb['value'] = corrected2Emb.value.apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "minute-oakland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in corrected7Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_7_props'][row['node']] = row['value']\n",
-    "for _, row in corrected2Emb.iterrows():\n",
-    "    masterEmbedDictMaster['text_2_props'][row['node']] = row['value']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documentary-fluid",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "analyzed-naples",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "psychological-brighton",
-   "metadata": {},
-   "source": [
-    "## Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "meaning-spanking",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n",
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "df1 = df1.set_index('node1')\n",
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')\n",
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "masterEmbCorrections_abs_set = set(masterEmbCorrections['abstract'])\n",
-    "sitelinksDF2 = sitelinksDF2[sitelinksDF2.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "offensive-enclosure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows()}\n",
-    "descriptionsDF = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in masterEmbCorrections_abs_set)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "better-tuner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 58 Qnodes, there are 16 sitelink Qnodes which do not have a short abstract i.e 42 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "sdf_set = set(sitelinksDF2.index.to_list())\n",
-    "df1 = df1[df1.index.map(lambda p: p in sdf_set)]\n",
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "# abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "invalid-fiction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['node1_label'] = abstractsDF2.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else \"\")\n",
-    "abstractsDF2['node1_desc'] = abstractsDF2.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else \"\")\n",
-    "def combineAbsLabDesc(row, parameter):\n",
-    "    if not(pd.isna(row[parameter])) and row[parameter] != 'nan' and row[parameter] != \"\":\n",
-    "        return row[parameter]\n",
-    "    elif row['node1_label'] == \"\" and row['node1_desc'] == \"\":\n",
-    "        return None\n",
-    "    else:\n",
-    "        return row['node1_label'] + ' ' + row['node1_desc']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "opened-drink",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.abstract.apply(lambda p: sent_tokenize(str(p))[0] if p else None)\n",
-    "abstractsDF2 = abstractsDF2.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "affected-reproduction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2['abstract'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract',))\n",
-    "abstractsDF2['abstract_firstSent'] = abstractsDF2.apply(combineAbsLabDesc, axis=1, args=('abstract_firstSent',))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "actual-communication",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "quantitative-tumor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2[~abstractsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "turned-retail",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "58"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "heard-freedom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = abstractsDF2.drop(columns=['index']).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "forty-southeast",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>level_0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Luxuries</td>\n",
-       "      <td>Q10953913-wikipedia_sitelink-538fe3-0</td>\n",
-       "      <td>Q10953913</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Luxuries</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>luxuryBehavior, expenses or equipment that far...</td>\n",
-       "      <td>luxury</td>\n",
-       "      <td>Behavior, expenses or equipment that far excee...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Potato</td>\n",
-       "      <td>Q10998-wikipedia_sitelink-56b85c-0</td>\n",
-       "      <td>Q10998</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Potato</td>\n",
-       "      <td>10709.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Potato&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "      <td>potato</td>\n",
-       "      <td>species of plant</td>\n",
-       "      <td>The potato is a root vegetable native to the A...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>Q111-wikipedia_sitelink-9ff296-0</td>\n",
-       "      <td>Q111</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Mars</td>\n",
-       "      <td>1803088.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Mars&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "      <td>Mars</td>\n",
-       "      <td>fourth planet from the Sun</td>\n",
-       "      <td>Mars is the fourth planet from the Sun and the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Dawn</td>\n",
-       "      <td>Q11326182-wikipedia_sitelink-ae2918-0</td>\n",
-       "      <td>Q11326182</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dawn</td>\n",
-       "      <td>97544.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Dawn&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "      <td>dawn</td>\n",
-       "      <td>time that marks the beginning of the twilight ...</td>\n",
-       "      <td>Dawn is the time that marks the beginning of t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Change_(philosophy)</td>\n",
-       "      <td>Q1150070-wikipedia_sitelink-81cf5f-0</td>\n",
-       "      <td>Q1150070</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Change_(philosophy)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>changeprocess, event or action that deviates f...</td>\n",
-       "      <td>change</td>\n",
-       "      <td>process, event or action that deviates from th...</td>\n",
-       "      <td>nan</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level_0  index         trimmedNode2                                     id  \\\n",
-       "0        0      0             Luxuries  Q10953913-wikipedia_sitelink-538fe3-0   \n",
-       "1        1      1               Potato     Q10998-wikipedia_sitelink-56b85c-0   \n",
-       "2        2      2                 Mars       Q111-wikipedia_sitelink-9ff296-0   \n",
-       "3        3      3                 Dawn  Q11326182-wikipedia_sitelink-ae2918-0   \n",
-       "4        4      4  Change_(philosophy)   Q1150070-wikipedia_sitelink-81cf5f-0   \n",
-       "\n",
-       "       node1               label  \\\n",
-       "0  Q10953913  wikipedia_sitelink   \n",
-       "1     Q10998  wikipedia_sitelink   \n",
-       "2       Q111  wikipedia_sitelink   \n",
-       "3  Q11326182  wikipedia_sitelink   \n",
-       "4   Q1150070  wikipedia_sitelink   \n",
-       "\n",
-       "                                              node2     ignore  \\\n",
-       "0             http://en.wikipedia.org/wiki/Luxuries        NaN   \n",
-       "1               http://en.wikipedia.org/wiki/Potato    10709.0   \n",
-       "2                 http://en.wikipedia.org/wiki/Mars  1803088.0   \n",
-       "3                 http://en.wikipedia.org/wiki/Dawn    97544.0   \n",
-       "4  http://en.wikipedia.org/wiki/Change_(philosophy)        NaN   \n",
-       "\n",
-       "                                    url  \\\n",
-       "0                                   NaN   \n",
-       "1  <http://dbpedia.org/resource/Potato>   \n",
-       "2    <http://dbpedia.org/resource/Mars>   \n",
-       "3    <http://dbpedia.org/resource/Dawn>   \n",
-       "4                                   NaN   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "                                            abstract node1_label  \\\n",
-       "0  luxuryBehavior, expenses or equipment that far...      luxury   \n",
-       "1  The potato is a root vegetable native to the A...      potato   \n",
-       "2  Mars is the fourth planet from the Sun and the...        Mars   \n",
-       "3  Dawn is the time that marks the beginning of t...        dawn   \n",
-       "4  changeprocess, event or action that deviates f...      change   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0  Behavior, expenses or equipment that far excee...   \n",
-       "1                                   species of plant   \n",
-       "2                         fourth planet from the Sun   \n",
-       "3  time that marks the beginning of the twilight ...   \n",
-       "4  process, event or action that deviates from th...   \n",
-       "\n",
-       "                                  abstract_firstSent  \n",
-       "0                                                nan  \n",
-       "1  The potato is a root vegetable native to the A...  \n",
-       "2  Mars is the fourth planet from the Sun and the...  \n",
-       "3  Dawn is the time that marks the beginning of t...  \n",
-       "4                                                nan  "
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "multiple-offer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getIndSentEmbeddings(sent, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode([sent])\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "sustainable-breakdown",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.6419482231140137 s\n",
-      "0.5260367393493652 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "abstractsDF2['abs_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract, 'bert-base-nli-mean-tokens')))\n",
-    "abstractsDF2['abs_firstSent_emb'] = pd.Series(list(getSentEmbeddings(abstractsDF2.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "usual-selling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in abstractsDF2.iterrows():\n",
-    "    masterEmbedDictMaster['abstract'][row['node1']] = row['abs_emb']\n",
-    "    masterEmbedDictMaster['abstract_first_sent'][row['node1']] = row['abs_firstSent_emb']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "promising-owner",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.37706875801086426 s\n",
-      "0.3001420497894287 s\n",
-      "0.370746374130249 s\n",
-      "0.6896324157714844 s\n",
-      "0.33779358863830566 s\n",
-      "0.3965473175048828 s\n",
-      "0.3200962543487549 s\n",
-      "0.3489806652069092 s\n",
-      "0.3413431644439697 s\n",
-      "0.32114505767822266 s\n",
-      "0.3811838626861572 s\n",
-      "0.34630370140075684 s\n",
-      "0.37790727615356445 s\n",
-      "0.26860570907592773 s\n",
-      "0.3601953983306885 s\n",
-      "0.3713240623474121 s\n",
-      "0.34137582778930664 s\n",
-      "0.33736181259155273 s\n",
-      "0.37023448944091797 s\n",
-      "0.31382036209106445 s\n",
-      "0.35136938095092773 s\n",
-      "0.37309718132019043 s\n",
-      "0.33543896675109863 s\n",
-      "0.38199710845947266 s\n",
-      "0.3740067481994629 s\n",
-      "0.3278031349182129 s\n",
-      "0.32283997535705566 s\n",
-      "0.34000563621520996 s\n",
-      "0.31502628326416016 s\n",
-      "0.34996771812438965 s\n",
-      "0.3871273994445801 s\n",
-      "0.3487060070037842 s\n",
-      "0.35172486305236816 s\n",
-      "0.3280646800994873 s\n",
-      "0.3519773483276367 s\n",
-      "0.3354451656341553 s\n",
-      "0.3633551597595215 s\n",
-      "0.3226644992828369 s\n",
-      "0.33882975578308105 s\n",
-      "0.36072254180908203 s\n",
-      "0.3833494186401367 s\n",
-      "0.2929043769836426 s\n",
-      "0.32875680923461914 s\n",
-      "0.36334872245788574 s\n",
-      "0.34148168563842773 s\n",
-      "0.3569769859313965 s\n",
-      "0.37468576431274414 s\n",
-      "0.399524450302124 s\n",
-      "0.3516504764556885 s\n",
-      "0.333402156829834 s\n",
-      "0.3851203918457031 s\n",
-      "0.34867238998413086 s\n",
-      "0.3607771396636963 s\n",
-      "0.38669753074645996 s\n",
-      "0.33347272872924805 s\n",
-      "0.36278390884399414 s\n",
-      "0.3602781295776367 s\n",
-      "0.3322322368621826 s\n",
-      "0.36807823181152344 s\n",
-      "0.3407411575317383 s\n",
-      "0.3837134838104248 s\n",
-      "0.38958096504211426 s\n",
-      "0.3332521915435791 s\n",
-      "0.3331124782562256 s\n",
-      "0.35001134872436523 s\n",
-      "0.32433485984802246 s\n",
-      "0.36315059661865234 s\n",
-      "0.34323906898498535 s\n",
-      "0.3112339973449707 s\n",
-      "0.30588483810424805 s\n",
-      "0.30704236030578613 s\n",
-      "0.31201720237731934 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "acquired-manitoba",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.32213783264160156 s\n",
-      "0.357776403427124 s\n",
-      "0.37949395179748535 s\n",
-      "0.35210466384887695 s\n",
-      "0.28103041648864746 s\n",
-      "0.3626406192779541 s\n",
-      "0.35109710693359375 s\n",
-      "0.34203338623046875 s\n",
-      "0.32386112213134766 s\n",
-      "0.3354361057281494 s\n",
-      "0.3063056468963623 s\n",
-      "0.3441202640533447 s\n",
-      "0.32869935035705566 s\n",
-      "0.42442989349365234 s\n",
-      "0.37239527702331543 s\n",
-      "0.38650059700012207 s\n",
-      "0.3191685676574707 s\n",
-      "0.3609733581542969 s\n",
-      "0.3115823268890381 s\n",
-      "0.36015963554382324 s\n",
-      "0.3338603973388672 s\n",
-      "0.3487727642059326 s\n",
-      "0.3250617980957031 s\n",
-      "0.35145044326782227 s\n",
-      "0.33944034576416016 s\n",
-      "0.31502413749694824 s\n",
-      "0.3611795902252197 s\n",
-      "0.35285043716430664 s\n",
-      "0.3575010299682617 s\n",
-      "0.304781436920166 s\n",
-      "0.4003562927246094 s\n",
-      "0.3315858840942383 s\n",
-      "0.36008763313293457 s\n",
-      "0.36187100410461426 s\n",
-      "0.32981252670288086 s\n",
-      "0.3378865718841553 s\n",
-      "0.31662964820861816 s\n",
-      "0.32143092155456543 s\n",
-      "0.3152732849121094 s\n",
-      "0.38222813606262207 s\n",
-      "0.3846759796142578 s\n",
-      "0.33153700828552246 s\n",
-      "0.37013936042785645 s\n",
-      "0.33272790908813477 s\n",
-      "0.29526567459106445 s\n",
-      "0.3218040466308594 s\n",
-      "0.3795340061187744 s\n",
-      "0.3576061725616455 s\n",
-      "0.35764193534851074 s\n",
-      "0.36867713928222656 s\n",
-      "0.3807237148284912 s\n",
-      "0.33266758918762207 s\n",
-      "0.33878159523010254 s\n",
-      "0.38289546966552734 s\n",
-      "0.38695788383483887 s\n",
-      "0.33074188232421875 s\n",
-      "0.32749414443969727 s\n",
-      "0.33860039710998535 s\n",
-      "0.36585235595703125 s\n",
-      "0.33011841773986816 s\n",
-      "0.3293156623840332 s\n",
-      "0.3491702079772949 s\n",
-      "0.3720529079437256 s\n",
-      "0.3078622817993164 s\n",
-      "0.3844125270843506 s\n",
-      "0.32468104362487793 s\n",
-      "0.3186354637145996 s\n",
-      "0.3438723087310791 s\n",
-      "0.36643028259277344 s\n",
-      "0.34279680252075195 s\n",
-      "0.3625810146331787 s\n",
-      "0.35865354537963867 s\n",
-      "0.3503103256225586 s\n",
-      "0.37160682678222656 s\n",
-      "0.3268110752105713 s\n",
-      "0.2564544677734375 s\n",
-      "0.37343525886535645 s\n",
-      "0.33298277854919434 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "for node in masterEmbCorrections_abs_set:\n",
-    "    if node not in masterEmbedDictMaster['abstract_first_sent']:\n",
-    "        if node in labelsDict and node in descDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node] + ' ' + descDict[node], 'bert-base-nli-mean-tokens')[0]\n",
-    "        elif node in labelsDict:\n",
-    "            masterEmbedDictMaster['abstract_first_sent'][node] = getIndSentEmbeddings(labelsDict[node], 'bert-base-nli-mean-tokens')[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "veterinary-thailand",
-   "metadata": {},
-   "source": [
-    "## Updated coverage details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "intimate-campus",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pair Coverage by new text_7_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new text_2_props embeddings created for 19k retrofitting: 325\n",
-      "Pair Coverage by new complex embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new transe embeddings created for 19k retrofitting: 343\n",
-      "Pair Coverage by new abstract embeddings created for 19k retrofitting: 339\n",
-      "Pair Coverage by new abstract_first_sent embeddings created for 19k retrofitting: 339\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in masterEmbedKeys:\n",
-    "    print(f\"Pair Coverage by new {key1} embeddings created for 19k retrofitting: {sum([row[0] in masterEmbedDictMaster[key1] and row[1] in masterEmbedDictMaster[key1] for row in wordsim_pairs])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "lovely-token",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in masterEmbedDictMaster.keys():\n",
-    "    for key2 in masterEmbedDictMaster[key1].keys():\n",
-    "        if type(masterEmbedDictMaster[key1][key2]) != list:\n",
-    "            masterEmbedDictMaster[key1][key2] = masterEmbedDictMaster[key1][key2].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "exact-surfing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in ['complex', 'transe']:\n",
-    "    json.dump(masterEmbedDictMaster[key1], open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json', 'w'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "behavioral-spain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countOverlap(source, target):\n",
-    "    cnt = 0\n",
-    "    for key1 in source:\n",
-    "        if key1 in target:\n",
-    "            cnt += 1\n",
-    "    return cnt\n",
-    "p279QnodesList = set(json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "hawaiian-brain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>embedding</th>\n",
-       "      <th>total count</th>\n",
-       "      <th>overlap count</th>\n",
-       "      <th>Coverage Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>238930</td>\n",
-       "      <td>238889</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>238500</td>\n",
-       "      <td>238448</td>\n",
-       "      <td>99.815395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>105964</td>\n",
-       "      <td>105916</td>\n",
-       "      <td>44.336910</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             embedding  total count  overlap count  Coverage Percentage\n",
-       "0         text_7_props       238930         238889           100.000000\n",
-       "1         text_2_props       238930         238889           100.000000\n",
-       "2              complex       238500         238448            99.815395\n",
-       "3               transe       238500         238448            99.815395\n",
-       "4             abstract       105964         105916            44.336910\n",
-       "5  abstract_first_sent       105964         105916            44.336910"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summArr = []\n",
-    "for key1 in masterEmbedDictMaster:\n",
-    "    cnt = countOverlap(masterEmbedDictMaster[key1], p279QnodesList)\n",
-    "    summArr.append([key1, len(masterEmbedDictMaster[key1]), cnt, cnt / len(p279QnodesList) * 100])\n",
-    "pd.DataFrame(summArr, columns=['embedding', 'total count', 'overlap count', 'Coverage Percentage'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-namibia",
-   "metadata": {},
-   "source": [
-    "# Concatenated Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fifth-associate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "embedDictMaster = {}\n",
-    "for key1 in ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']:\n",
-    "    embedDictMaster[key1] = json.load(open('../data/Master_P279_dataset/embeddings/'+key1+'_orig_embedding_dict_updated.json'))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "egyptian-sentence",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props :  1024\n",
-      "text_2_props :  1024\n",
-      "complex :  200\n",
-      "transe :  200\n",
-      "abstract :  768\n",
-      "abstract_first_sent :  768\n"
-     ]
-    }
-   ],
-   "source": [
-    "def determineEmbeddingLengths(embedDictMaster):\n",
-    "    for key in embedDictMaster.keys():\n",
-    "        embed_size = len(next(iter(embedDictMaster[key].values())))\n",
-    "        print(key,\": \",embed_size)\n",
-    "determineEmbeddingLengths(embedDictMaster)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "removable-point",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Added 11 corrections\n",
-      "Added 11 corrections\n",
-      "Added 0 corrections\n",
-      "Added 0 corrections\n",
-      "Added 4 corrections\n",
-      "Added 4 corrections\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = deserializeEmbeddingDict(embedDictMaster[key1])\n",
-    "# Fill Coverage of embedding dictionaries\n",
-    "for key1 in embedDictMaster.keys():\n",
-    "    embedDictMaster[key1] = fillCoverage(embedDictMaster[key1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "productive-indiana",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text_7_props 238941\n",
-      "text_2_props 238941\n",
-      "complex 238941\n",
-      "transe 238941\n",
-      "abstract 238941\n",
-      "abstract_first_sent 238941\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    print(key1, len(next(iter(embedDictMaster.values()))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mechanical-retro",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ready-financing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prime-hometown",
-   "metadata": {},
-   "source": [
-    "# Retrofitting sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "tight-civilization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "exciting-circle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase, weightAssignment=False):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            if weightAssignment:\n",
-    "                sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            else:\n",
-    "                sumOfSims = sum([1 for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "                \n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "hollywood-prisoner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    responseDict = {}\n",
-    "    responseDict['wordSimMissingSet'] = wordSimMissingSet\n",
-    "    responseDict['coveredPairs'] = len(wordSim353AnnotDF_New2)\n",
-    "    responseDict['totalPairs'] = len(wordSim353AnnotDF_New)\n",
-    "    \n",
-    "#     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    \n",
-    "    # Logic 1: Scale min,max value to 1,4 strictly\n",
-    "#     min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "#     min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "#     wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    \n",
-    "    # Logic 2: Scale abs value to 1,4 strictly\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * abs(p))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "    \n",
-    "#     print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "#     print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "#     print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    responseDict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['old_acc'] = accuracy_score(wordSim353AnnotDF_New['textOld'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    responseDict['new_acc'] = accuracy_score(wordSim353AnnotDF_New['textNew'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    \n",
-    "    responseDict['class_rep_old'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), output_dict=True)\n",
-    "    responseDict['class_rep_new'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), output_dict=True)\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    responseDict['cm_old'] = cm_old\n",
-    "    responseDict['cm_new'] = cm_new\n",
-    "    \n",
-    "    return responseDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "severe-explosion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDictMaster, embedDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "decreased-syndication",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "39218\n"
-     ]
-    }
-   ],
-   "source": [
-    "neighDictMaster['19k_childPar'] = fetchNeighbours(p279ChildPar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "rocky-criterion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['complex'] = complexEmb\n",
-    "embedDictMaster['transe'] = transeEmb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "apparent-sapphire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for key2 in embedDictMaster[key1].keys():\n",
-    "        embedDictMaster[key1][key2] = np.array(embedDictMaster[key1][key2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "precise-oxygen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embList = list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "identical-keyboard",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basisList = list(neighDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "aging-flavor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['19k_childPar'])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neighDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "amended-remove",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster, responsesDictMaster = {}, {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "surgical-insurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7817a00dcf3c412b92a7c5ac75517168",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "results = []\n",
-    "NUM_ITERS = 10\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            groupResults = []\n",
-    "            for weightCase in [1,2]:\n",
-    "                embedDict = embedDictMaster[emb]\n",
-    "                if weightedNess:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                else:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                for iterNum in range(1,NUM_ITERS+1):\n",
-    "                    newEmbedDict = retrofit(embedDict, neighDictMaster[basis], weightCase, weightedNess)\n",
-    "        #             dists = determineDistances(embedDict, newEmbedDict)\n",
-    "                    responsesDict = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        #                 print(responsesDict.keys())\n",
-    "                    groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                    responsesDict['old_acc']*100, \\\n",
-    "                                    responsesDict['new_acc']*100, \\\n",
-    "                                    (responsesDict['new_acc'] - responsesDict['old_acc'])*100, \\\n",
-    "                                    responsesDict['coveredPairs'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                    ])\n",
-    "                    embedDict = newEmbedDict\n",
-    "\n",
-    "                newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                responsesDictMaster[caseName] = responsesDict\n",
-    "            for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "                results.append(gR+[rank])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "assigned-stations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'Rank'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "built-mumbai",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Embedding</th>\n",
-       "      <th>Basis</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Weightedness</th>\n",
-       "      <th>Iteration Num</th>\n",
-       "      <th>Old Acc</th>\n",
-       "      <th>New Acc</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>Pairs Covered</th>\n",
-       "      <th>Old I Precision</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Old U Precision</th>\n",
-       "      <th>Old U Recall</th>\n",
-       "      <th>Old U F1-Score</th>\n",
-       "      <th>New I Precision</th>\n",
-       "      <th>New I Recall</th>\n",
-       "      <th>New I F1-Score</th>\n",
-       "      <th>New U Precision</th>\n",
-       "      <th>New U Recall</th>\n",
-       "      <th>New U F1-Score</th>\n",
-       "      <th>Rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>3.488372</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>3.197674</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>2.906977</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.463415</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.504425</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>1.744186</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>61.918605</td>\n",
-       "      <td>1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433121</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.660194</td>\n",
-       "      <td>0.531250</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.495050</td>\n",
-       "      <td>0.485437</td>\n",
-       "      <td>0.490196</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.397059</td>\n",
-       "      <td>0.262136</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.365385</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.245161</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.372549</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.246753</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.383333</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.282209</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.180451</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.380000</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.248366</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.455224</td>\n",
-       "      <td>0.592233</td>\n",
-       "      <td>0.514768</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.621359</td>\n",
-       "      <td>0.526749</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.458904</td>\n",
-       "      <td>0.650485</td>\n",
-       "      <td>0.538153</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.457746</td>\n",
-       "      <td>0.631068</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>63.662791</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.453846</td>\n",
-       "      <td>0.572816</td>\n",
-       "      <td>0.506438</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.448819</td>\n",
-       "      <td>0.553398</td>\n",
-       "      <td>0.495652</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>62.500000</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.184466</td>\n",
-       "      <td>0.242038</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.203883</td>\n",
-       "      <td>0.264151</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.253165</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.309524</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.233010</td>\n",
-       "      <td>0.290909</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.491803</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>62.790698</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.543689</td>\n",
-       "      <td>0.489083</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.535714</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>60.465116</td>\n",
-       "      <td>59.883721</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.365854</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.115702</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.116667</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>67.151163</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.511905</td>\n",
-       "      <td>0.417476</td>\n",
-       "      <td>0.459893</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.492958</td>\n",
-       "      <td>0.339806</td>\n",
-       "      <td>0.402299</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>65.988372</td>\n",
-       "      <td>65.116279</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.464286</td>\n",
-       "      <td>0.252427</td>\n",
-       "      <td>0.327044</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.223301</td>\n",
-       "      <td>0.296774</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>64.825581</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.450000</td>\n",
-       "      <td>0.174757</td>\n",
-       "      <td>0.251748</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.126214</td>\n",
-       "      <td>0.191176</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8</td>\n",
-       "      <td>61.627907</td>\n",
-       "      <td>60.755814</td>\n",
-       "      <td>-0.872093</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.077670</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>0.394737</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.517241</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.114754</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>62.209302</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.545455</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.087379</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>66.569767</td>\n",
-       "      <td>-1.162791</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.507937</td>\n",
-       "      <td>0.310680</td>\n",
-       "      <td>0.385542</td>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.475410</td>\n",
-       "      <td>0.281553</td>\n",
-       "      <td>0.353659</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>10</td>\n",
-       "      <td>64.534884</td>\n",
-       "      <td>63.081395</td>\n",
-       "      <td>-1.453488</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.194175</td>\n",
-       "      <td>0.264901</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>0.377778</td>\n",
-       "      <td>0.165049</td>\n",
-       "      <td>0.229730</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>63.372093</td>\n",
-       "      <td>-2.034884</td>\n",
-       "      <td>291</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.116505</td>\n",
-       "      <td>0.184615</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.097087</td>\n",
-       "      <td>0.160000</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>40 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Embedding         Basis  Weight  Weightedness  Iteration Num    Old Acc  \\\n",
-       "0    complex  19k_childPar       1          True              1  60.755814   \n",
-       "20    transe  19k_childPar       1          True              1  62.500000   \n",
-       "1    complex  19k_childPar       1          True              2  64.244186   \n",
-       "11   complex  19k_childPar       2          True              2  61.918605   \n",
-       "10   complex  19k_childPar       2          True              1  60.755814   \n",
-       "2    complex  19k_childPar       1          True              3  67.151163   \n",
-       "4    complex  19k_childPar       1          True              5  67.151163   \n",
-       "36    transe  19k_childPar       2          True              7  62.500000   \n",
-       "30    transe  19k_childPar       2          True              1  62.500000   \n",
-       "22    transe  19k_childPar       1          True              3  64.825581   \n",
-       "38    transe  19k_childPar       2          True              9  63.081395   \n",
-       "33    transe  19k_childPar       2          True              4  62.790698   \n",
-       "18   complex  19k_childPar       2          True              9  63.081395   \n",
-       "37    transe  19k_childPar       2          True              8  63.081395   \n",
-       "32    transe  19k_childPar       2          True              3  62.790698   \n",
-       "23    transe  19k_childPar       1          True              4  65.406977   \n",
-       "39    transe  19k_childPar       2          True             10  63.372093   \n",
-       "15   complex  19k_childPar       2          True              6  63.662791   \n",
-       "14   complex  19k_childPar       2          True              5  63.662791   \n",
-       "13   complex  19k_childPar       2          True              4  63.662791   \n",
-       "12   complex  19k_childPar       2          True              3  63.662791   \n",
-       "17   complex  19k_childPar       2          True              8  63.372093   \n",
-       "16   complex  19k_childPar       2          True              7  63.662791   \n",
-       "35    transe  19k_childPar       2          True              6  62.790698   \n",
-       "34    transe  19k_childPar       2          True              5  63.081395   \n",
-       "31    transe  19k_childPar       2          True              2  63.081395   \n",
-       "28    transe  19k_childPar       1          True              9  60.755814   \n",
-       "19   complex  19k_childPar       2          True             10  63.081395   \n",
-       "26    transe  19k_childPar       1          True              7  62.209302   \n",
-       "29    transe  19k_childPar       1          True             10  60.465116   \n",
-       "8    complex  19k_childPar       1          True              9  65.116279   \n",
-       "3    complex  19k_childPar       1          True              4  67.732558   \n",
-       "6    complex  19k_childPar       1          True              7  66.569767   \n",
-       "7    complex  19k_childPar       1          True              8  65.988372   \n",
-       "21    transe  19k_childPar       1          True              2  65.697674   \n",
-       "27    transe  19k_childPar       1          True              8  61.627907   \n",
-       "25    transe  19k_childPar       1          True              6  63.372093   \n",
-       "5    complex  19k_childPar       1          True              6  67.732558   \n",
-       "9    complex  19k_childPar       1          True             10  64.534884   \n",
-       "24    transe  19k_childPar       1          True              5  65.406977   \n",
-       "\n",
-       "      New Acc  Increase  Pairs Covered  Old I Precision  ...  Old U Precision  \\\n",
-       "0   64.244186  3.488372            291         1.000000  ...         0.433121   \n",
-       "20  65.697674  3.197674            291         0.888889  ...         0.397059   \n",
-       "1   67.151163  2.906977            291         1.000000  ...         0.463415   \n",
-       "11  63.662791  1.744186            291         1.000000  ...         0.444444   \n",
-       "10  61.918605  1.162791            291         1.000000  ...         0.433121   \n",
-       "2   67.732558  0.581395            291         1.000000  ...         0.495050   \n",
-       "4   67.732558  0.581395            291         0.916667  ...         0.492958   \n",
-       "36  63.081395  0.581395            291         0.846154  ...         0.351852   \n",
-       "30  63.081395  0.581395            291         0.888889  ...         0.397059   \n",
-       "22  65.406977  0.581395            291         0.750000  ...         0.393939   \n",
-       "38  63.372093  0.290698            291         0.785714  ...         0.372549   \n",
-       "33  63.081395  0.290698            291         0.900000  ...         0.383333   \n",
-       "18  63.081395  0.000000            291         0.909091  ...         0.444444   \n",
-       "37  63.081395  0.000000            291         0.846154  ...         0.365385   \n",
-       "32  62.790698  0.000000            291         0.900000  ...         0.387097   \n",
-       "23  65.406977  0.000000            291         0.750000  ...         0.400000   \n",
-       "39  63.372093  0.000000            291         0.785714  ...         0.380000   \n",
-       "15  63.662791  0.000000            291         1.000000  ...         0.455224   \n",
-       "14  63.662791  0.000000            291         1.000000  ...         0.457143   \n",
-       "13  63.662791  0.000000            291         1.000000  ...         0.457746   \n",
-       "12  63.662791  0.000000            291         1.000000  ...         0.458904   \n",
-       "17  63.081395 -0.290698            291         0.909091  ...         0.448819   \n",
-       "16  63.372093 -0.290698            291         1.000000  ...         0.453846   \n",
-       "35  62.500000 -0.290698            291         0.846154  ...         0.363636   \n",
-       "34  62.790698 -0.290698            291         0.846154  ...         0.375000   \n",
-       "31  62.790698 -0.290698            291         0.900000  ...         0.400000   \n",
-       "28  60.465116 -0.290698            291         0.394737  ...         0.368421   \n",
-       "19  62.790698 -0.290698            291         0.909091  ...         0.444444   \n",
-       "26  61.627907 -0.581395            291         0.428571  ...         0.428571   \n",
-       "29  59.883721 -0.581395            291         0.365854  ...         0.388889   \n",
-       "8   64.534884 -0.581395            291         0.785714  ...         0.442308   \n",
-       "3   67.151163 -0.581395            291         0.909091  ...         0.511905   \n",
-       "6   65.988372 -0.581395            291         0.916667  ...         0.475410   \n",
-       "7   65.116279 -0.872093            291         0.846154  ...         0.464286   \n",
-       "21  64.825581 -0.872093            291         0.833333  ...         0.450000   \n",
-       "27  60.755814 -0.872093            291         0.416667  ...         0.400000   \n",
-       "25  62.209302 -1.162791            291         0.468750  ...         0.454545   \n",
-       "5   66.569767 -1.162791            291         0.916667  ...         0.507937   \n",
-       "9   63.081395 -1.453488            291         0.785714  ...         0.416667   \n",
-       "24  63.372093 -2.034884            291         0.652174  ...         0.444444   \n",
-       "\n",
-       "    Old U Recall  Old U F1-Score  New I Precision  New I Recall  \\\n",
-       "0       0.660194        0.523077         1.000000          0.40   \n",
-       "20      0.262136        0.315789         0.833333          0.50   \n",
-       "1       0.553398        0.504425         1.000000          0.50   \n",
-       "11      0.660194        0.531250         1.000000          0.45   \n",
-       "10      0.660194        0.523077         1.000000          0.40   \n",
-       "2       0.485437        0.490196         0.909091          0.50   \n",
-       "4       0.339806        0.402299         0.916667          0.55   \n",
-       "36      0.184466        0.242038         0.846154          0.55   \n",
-       "30      0.262136        0.315789         0.900000          0.45   \n",
-       "22      0.126214        0.191176         0.750000          0.75   \n",
-       "38      0.184466        0.246753         0.785714          0.55   \n",
-       "33      0.223301        0.282209         0.846154          0.55   \n",
-       "18      0.543689        0.489083         0.909091          0.50   \n",
-       "37      0.184466        0.245161         0.785714          0.55   \n",
-       "32      0.233010        0.290909         0.900000          0.45   \n",
-       "23      0.116505        0.180451         0.652174          0.75   \n",
-       "39      0.184466        0.248366         0.785714          0.55   \n",
-       "15      0.592233        0.514768         1.000000          0.45   \n",
-       "14      0.621359        0.526749         1.000000          0.45   \n",
-       "13      0.631068        0.530612         1.000000          0.45   \n",
-       "12      0.650485        0.538153         1.000000          0.45   \n",
-       "17      0.553398        0.495652         0.909091          0.50   \n",
-       "16      0.572816        0.506438         0.909091          0.50   \n",
-       "35      0.194175        0.253165         0.846154          0.55   \n",
-       "34      0.203883        0.264151         0.846154          0.55   \n",
-       "31      0.252427        0.309524         0.900000          0.45   \n",
-       "28      0.067961        0.114754         0.365854          0.75   \n",
-       "19      0.543689        0.489083         0.900000          0.45   \n",
-       "26      0.087379        0.145161         0.416667          0.75   \n",
-       "29      0.067961        0.115702         0.333333          0.75   \n",
-       "8       0.223301        0.296774         0.785714          0.55   \n",
-       "3       0.417476        0.459893         0.916667          0.55   \n",
-       "6       0.281553        0.353659         0.846154          0.55   \n",
-       "7       0.252427        0.327044         0.785714          0.55   \n",
-       "21      0.174757        0.251748         0.750000          0.60   \n",
-       "27      0.077670        0.130081         0.394737          0.75   \n",
-       "25      0.097087        0.160000         0.428571          0.75   \n",
-       "5       0.310680        0.385542         0.916667          0.55   \n",
-       "9       0.194175        0.264901         0.687500          0.55   \n",
-       "24      0.116505        0.184615         0.468750          0.75   \n",
-       "\n",
-       "    New I F1-Score  New U Precision  New U Recall  New U F1-Score  Rank  \n",
-       "0         0.571429         0.463415      0.553398        0.504425     2  \n",
-       "20        0.625000         0.450000      0.174757        0.251748     0  \n",
-       "1         0.666667         0.495050      0.485437        0.490196     4  \n",
-       "11        0.620690         0.458904      0.650485        0.538153    13  \n",
-       "10        0.571429         0.444444      0.660194        0.531250    12  \n",
-       "2         0.645161         0.511905      0.417476        0.459893     1  \n",
-       "4         0.687500         0.507937      0.310680        0.385542     5  \n",
-       "36        0.666667         0.365385      0.184466        0.245161     6  \n",
-       "30        0.600000         0.400000      0.252427        0.309524    17  \n",
-       "22        0.750000         0.400000      0.116505        0.180451     3  \n",
-       "38        0.647059         0.380000      0.184466        0.248366     8  \n",
-       "33        0.666667         0.375000      0.203883        0.264151    14  \n",
-       "18        0.645161         0.444444      0.543689        0.489083    19  \n",
-       "37        0.647059         0.372549      0.184466        0.246753     7  \n",
-       "32        0.600000         0.383333      0.223301        0.282209    12  \n",
-       "23        0.697674         0.444444      0.116505        0.184615     1  \n",
-       "39        0.647059         0.380000      0.184466        0.248366     9  \n",
-       "15        0.620690         0.453846      0.572816        0.506438    17  \n",
-       "14        0.620690         0.455224      0.592233        0.514768    16  \n",
-       "13        0.620690         0.457143      0.621359        0.526749    15  \n",
-       "12        0.620690         0.457746      0.631068        0.530612    14  \n",
-       "17        0.645161         0.444444      0.543689        0.489083    18  \n",
-       "16        0.645161         0.448819      0.553398        0.495652     9  \n",
-       "35        0.666667         0.351852      0.184466        0.242038     5  \n",
-       "34        0.666667         0.363636      0.194175        0.253165    15  \n",
-       "31        0.600000         0.387097      0.233010        0.290909    11  \n",
-       "28        0.491803         0.388889      0.067961        0.115702    13  \n",
-       "19        0.600000         0.444444      0.543689        0.489083    10  \n",
-       "26        0.535714         0.400000      0.077670        0.130081     4  \n",
-       "29        0.461538         0.411765      0.067961        0.116667    16  \n",
-       "8         0.647059         0.416667      0.194175        0.264901     0  \n",
-       "3         0.687500         0.492958      0.339806        0.402299     3  \n",
-       "6         0.666667         0.464286      0.252427        0.327044     7  \n",
-       "7         0.647059         0.442308      0.223301        0.296774     8  \n",
-       "21        0.666667         0.393939      0.126214        0.191176     2  \n",
-       "27        0.517241         0.368421      0.067961        0.114754    10  \n",
-       "25        0.545455         0.428571      0.087379        0.145161    19  \n",
-       "5         0.687500         0.475410      0.281553        0.353659     6  \n",
-       "9         0.611111         0.377778      0.165049        0.229730    11  \n",
-       "24        0.576923         0.454545      0.097087        0.160000    18  \n",
-       "\n",
-       "[40 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resultsDF.sort_values(by=['Increase'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "utility-globe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.to_csv('../data/retrofitting/masterRetro_Aug20_2021.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "crazy-scene",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv2",
-   "language": "python",
-   "name": "kgtkenv2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "288px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Master P279-Dataset Generation.ipynb b/Master P279-Dataset Generation.ipynb
deleted file mode 100644
index 0477d95..0000000
--- a/Master P279-Dataset Generation.ipynb	
+++ /dev/null
@@ -1,5117 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "individual-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import scipy.stats as stats\n",
-    "import json\n",
-    "from tqdm.notebook import tqdm\n",
-    "import gzip\n",
-    "from collections import defaultdict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "necessary-shopper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Filepaths\n",
-    "\n",
-    "# DWD Files\n",
-    "DERIVED_IS_COUNTS_FILE = '../source_dataset_files/wikidata-20210215-dwd-v2/dwd_isa_class_count.compact.tsv.gz'\n",
-    "LABELS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\"\n",
-    "DESCRIPTIONS_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/descriptions.en.tsv.gz\"\n",
-    "P279_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/derived.P279.tsv.gz\"\n",
-    "P279STAR_EDGES_FILE = \"../source_dataset_files/wikidata-20210215-dwd-v2/derived.P279star.tsv.gz\"\n",
-    "CLASS_COUNTS_FILE = \"../data/supplementary_files/class-counts.tsv\"\n",
-    "CLASS_TRANSITIVE_P279_COUNTS_FILE = \"../data/supplementary_files/class-counts-P279star.tsv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "chubby-vehicle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_CHILD_PAR_INTERM_FILE = \"../data/basis/intermediate_files/P279_ChildPar.labDesc.csv\"\n",
-    "P279_CHILD_PAR_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_ChildPar.all-distilroberta-v1.csv\"\n",
-    "P279_CHILD_PAR_CLASSSIM_FILE = \"../data/basis/P279_ChildPar.classSim.csv\"\n",
-    "\n",
-    "P279_SIBLINGS_INTERM_FILE = \"../data/basis/intermediate_files/P279_Siblings.all_combinations.csv\"\n",
-    "P279_SIBLINGS_DISTILBERT_COSSIM_FILE = \"../data/basis/P279_Siblings.all-distilroberta-v1.csv\"\n",
-    "P279_SIBLINGS_CLASSSIM_FILE = \"../data/basis/P279_Siblings.classSim.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "parental-liverpool",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WORDSIM_DF = '../data/evaluation/wordsim353_with_r3.csv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "subsequent-phrase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PROBASE_SOURCE_DIR = \"../source_dataset_files/probase/\"\n",
-    "PROBASE_INTERM_FILE = \"../data/basis/intermediate_files/probase_WQnodes.csv\"\n",
-    "PROBASE_FINAL_FILE = '../data/basis/intermediate_files/probase_WQnodes_subset_and_sim.csv'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "joint-wrestling",
-   "metadata": {},
-   "source": [
-    "# Utilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "respiratory-average",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71f276d6b5b642609801caf1fa94bb41",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/41845781 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def fetch_labels_to_qnodes_dict():\n",
-    "    labels_to_qnodes_dict = defaultdict(str)\n",
-    "    with gzip.open(LABELS_FILE) as lab_f:\n",
-    "        first_line = True\n",
-    "        for line in tqdm(lab_f, total=41845781):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode('utf-8').strip().split()\n",
-    "            str1 = line[3][1:-4]\n",
-    "            qnode = line[1]\n",
-    "            if str1 in labels_to_qnodes_dict:\n",
-    "                if qnode < labels_to_qnodes_dict[str1]:\n",
-    "                    labels_to_qnodes_dict[str1] = qnode\n",
-    "            else:\n",
-    "                labels_to_qnodes_dict[str1] = qnode\n",
-    "    return labels_to_qnodes_dict\n",
-    "labels_to_qnodes_dict = fetch_labels_to_qnodes_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "damaged-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_worsim_labels_to_qnodes_dict():\n",
-    "    wordsim_DF = pd.read_csv(WORDSIM_DF)\n",
-    "    labels_to_qnodes_dict = {row['Word 1']: row['word1_kg_id'] for _, row in wordsim_DF.iterrows()}\n",
-    "    temp2 = {row['Word 2']: row['word2_kg_id'] for _, row in wordsim_DF.iterrows()}\n",
-    "    labels_to_qnodes_dict.update(temp2)\n",
-    "    return labels_to_qnodes_dict\n",
-    "\n",
-    "wordsim_labels_to_qnodes_dict = fetch_worsim_labels_to_qnodes_dict()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removable-number",
-   "metadata": {},
-   "source": [
-    "# Class Counts File Construction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "genuine-memorabilia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Took almost 1.5 hours to execute\n",
-    "# !zcat ../source_dataset_files/wikidata-20210215-dwd-v2/dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > ../data/supplementary_files/class-counts.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "pressed-influence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = defaultdict(int)\n",
-    "with gzip.open(P279STAR_EDGES_FILE, 'r') as f:\n",
-    "    first_line = True\n",
-    "    for line in tqdm(f):\n",
-    "        if first_line:\n",
-    "            first_line = False\n",
-    "            continue\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        class_counts[line[2]] += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "centered-school",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts_df = pd.DataFrame.from_dict(class_counts, orient='index',columns=['class_count'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "catholic-criminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts_df.reset_index().rename(columns={'index': '0'}).to_csv(CLASS_TRANSITIVE_P279_COUNTS_FILE, sep='\\t', header=None, index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "thorough-murray",
-   "metadata": {},
-   "source": [
-    "# P279 Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "threatened-pacific",
-   "metadata": {},
-   "source": [
-    "## P279 ChildPar Dataset\n",
-    "\n",
-    "Use descriptions wherever available, if not use labels, if not skip rows\n",
-    "\n",
-    "**Old Version Details:**\n",
-    "\n",
-    "[Wikidata OS File (Wikidata 2021-02-15 DWD version)](https://drive.google.com/drive/folders/168j3OfdVGXMTKcs6VyH8rq_p0n6w0GGj?usp=sharing)\n",
-    "\n",
-    "There are 721983 rows in P279 dataset, 606996 unique number of nodes in this dataset, 511841 labels and 299844 descriptions\n",
-    "\n",
-    "There are 373463 rows in P279 dataset s.t. both nodes have either a label or a description.     There are 373463 rows where both nodes have labels.     There are 373463 rows where both nodes have descriptions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "polyphonic-county",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz -p \";P279;\" -o ../data/P279_dataset/wikidata-P279.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "silent-liver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv(P279_EDGES_FILE,sep='\\t')\n",
-    "p279DFNodesSet = set(p279DF.node1.to_list() + p279DF.node2.to_list())\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, sep='\\t')\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in p279DFNodesSet)]\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in p279DFNodesSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cubic-taxation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3077831 rows in P279 dataset, 2503943 unique number of nodes in this dataset, 2406580 labels and 1369514 descriptions\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(p279DF)} rows in P279 dataset, {len(p279DFNodesSet)} unique number of nodes in this dataset, {len(labelsDF)} labels and {len(descriptionsDF)} descriptions\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "damaged-driving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DFNew = p279DF\n",
-    "temp1 = p279DFNew.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')\n",
-    "temp1 = temp1[['id','label','node2','node2_label']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]\n",
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()\n",
-    "\n",
-    "p279DFNew = hierDF\n",
-    "temp1 = p279DFNew.set_index('node1').join(descriptionsDF.set_index('node1'), rsuffix='_desc')\n",
-    "temp1 = temp1[['id','label','node2','node1_label','node2_label', 'node2_desc']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(descriptionsDF.set_index('node1'), rsuffix='_desc2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node1_label', 'node2_label', 'node2_desc']].rename(columns={'node2_desc': 'node1_desc', 'node2':'node2_desc'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3['node1_desc'] = temp3['node1_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "temp3['node2_desc'] = temp3['node2_desc'].apply(lambda p: p[1:-4] if type(p) == str else None)\n",
-    "hierDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "dress-yellow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combineLabDesc(row, nodeNum):\n",
-    "    if type(row['node'+str(nodeNum)+'_desc']) == str and type(row['node'+str(nodeNum)+'_label']) == str :\n",
-    "        return row['node'+str(nodeNum)+'_label'] + ' ' + row['node'+str(nodeNum)+'_desc']\n",
-    "    else:\n",
-    "        return None\n",
-    "hierDF['node1_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(1,))\n",
-    "hierDF['node2_labDesc'] = hierDF.apply(combineLabDesc, axis=1, args=(2,))\n",
-    "hierDF = hierDF[~hierDF['node1_labDesc'].isna()]\n",
-    "hierDF = hierDF[~hierDF['node2_labDesc'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "adaptive-measure",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 1898589 rows in P279 dataset s.t. both nodes have either a label or a description.     There are 1898589 rows where both nodes have labels.     There are 1898589 rows where both nodes have descriptions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(hierDF)} rows in P279 dataset s.t. both nodes have either a label or a description. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_label.isna()) & (~hierDF.node2_label.isna())])} rows where both nodes have labels. \\\n",
-    "    There are {len(hierDF[(~hierDF.node1_desc.isna()) & (~hierDF.node2_desc.isna())])} rows where both nodes have descriptions.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "disturbed-adaptation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     node2                                 id      node1 label  \\\n",
-       "1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "2  Q100026  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279   \n",
-       "3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279   \n",
-       "4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279   \n",
-       "5  Q100026   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279   \n",
-       "\n",
-       "                                          node2_desc              node1_label  \\\n",
-       "1  1944 fighter aircraft family by Oberst R. Lehmann                    YF-16   \n",
-       "2  1944 fighter aircraft family by Oberst R. Lehmann  F-16A/B Fighting Falcon   \n",
-       "3  1944 fighter aircraft family by Oberst R. Lehmann  F-16C/D Fighting Falcon   \n",
-       "4  1944 fighter aircraft family by Oberst R. Lehmann    F-16E/F Desert Falcon   \n",
-       "5  1944 fighter aircraft family by Oberst R. Lehmann               F-16 VISTA   \n",
-       "\n",
-       "  node2_label                                         node1_desc  \\\n",
-       "1        F-16     initial prototype of the F-16 fighter aircraft   \n",
-       "2        F-16        initial series of the F-16 fighter aircraft   \n",
-       "3        F-16      multirole series of the F-16 fighter aircraft   \n",
-       "4        F-16  export strike fighter series of the F-16 fight...   \n",
-       "5        F-16                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "1  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "2  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "3  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "4  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "5                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "5  F-16 1944 fighter aircraft family by Oberst R....  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-jurisdiction",
-   "metadata": {},
-   "source": [
-    "### Filter based on transitive P279 counts\n",
-    "\n",
-    "6769\n",
-    "(310053, 303284)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "broken-intermediate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hierDF = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "trained-parent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transitive_P279Counts = pd.read_csv(CLASS_COUNTS_FILE, sep='\\t', header=None)\n",
-    "transitive_P279Counts = transitive_P279Counts.set_index(0)[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "great-engine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def extract_pairs_with_top_k_class_counts(transitive_P279Counts, k, hierDF):\n",
-    "    transitive_P279Counts = transitive_P279Counts[transitive_P279Counts >= k]\n",
-    "    transitive_P279CountsNeededSet = set(transitive_P279Counts.index.to_list())\n",
-    "    hierDF_transP279_filtered = hierDF[hierDF.node2.apply(lambda p: p in transitive_P279CountsNeededSet)]\n",
-    "#     print(\"No. of pairs with identical desc:\", (hierDF_transP279_filtered.node1_desc == hierDF_transP279_filtered.node2_desc).sum())\n",
-    "    hierDF_transP279_filtered1 = hierDF_transP279_filtered[hierDF_transP279_filtered.node1_desc != hierDF_transP279_filtered.node2_desc]\n",
-    "#     print(len(hierDF_transP279_filtered), len(hierDF_transP279_filtered1))\n",
-    "    hierDF_transP279_filtered1 = hierDF_transP279_filtered1.groupby(\"node2\").apply(lambda g: g if len(g) <= 500 else g.sample(500))\n",
-    "    return hierDF_transP279_filtered1, k, len(hierDF_transP279_filtered1), hierDF_transP279_filtered1.node2.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "worldwide-designation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 304654, 28511)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1, k, cnt, n_pars = extract_pairs_with_top_k_class_counts(transitive_P279Counts, 10, hierDF)\n",
-    "k, cnt, n_pars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "medical-hierarchy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# tempRes = []\n",
-    "# for k in range(10000,100000,10000):\n",
-    "#     _, k, cnt, n_pars = extract_pairs_with_top_k_class_counts(transitive_P279Counts, k, hierDF)\n",
-    "#     tempRes.append((k, cnt, n_pars))\n",
-    "# pd.DataFrame(tempRes, columns=['Thresh', 'No. of rows', 'No. of unique parents'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "peaceful-protocol",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    28511.000000\n",
-       "mean        10.685490\n",
-       "std         28.508339\n",
-       "min          1.000000\n",
-       "25%          2.000000\n",
-       "50%          4.000000\n",
-       "75%         10.000000\n",
-       "max        500.000000\n",
-       "Name: node2, dtype: float64"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered1.node2.value_counts().describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "spiritual-ballot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered1.to_csv(P279_CHILD_PAR_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "english-alfred",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv(P279_CHILD_PAR_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "id": "interpreted-institute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>Unnamed: 1</th>\n",
-       "      <th>node2.1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     node2  Unnamed: 1  node2.1                                 id      node1  \\\n",
-       "0  Q100026           1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "1  Q100026           2  Q100026  Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "2  Q100026           3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "3  Q100026           4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "4  Q100026           5  Q100026   Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "\n",
-       "  label                                         node2_desc  \\\n",
-       "0  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "1  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "2  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "3  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "4  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "\n",
-       "               node1_label node2_label  \\\n",
-       "0                    YF-16        F-16   \n",
-       "1  F-16A/B Fighting Falcon        F-16   \n",
-       "2  F-16C/D Fighting Falcon        F-16   \n",
-       "3    F-16E/F Desert Falcon        F-16   \n",
-       "4               F-16 VISTA        F-16   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \n",
-       "0  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....  \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....  "
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "committed-diamond",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:0')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=500)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "alpine-virus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelName = 'sentence-transformers/all-distilroberta-v1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "funny-asian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = hierDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "stunning-consolidation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_labDesc.isna().sum(), hierDF.node2_labDesc.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "warming-dinner",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "149.29957628250122 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "103.25607371330261 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node1_labDesc, modelName)))\n",
-    "hierDF['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF.node2_labDesc, modelName)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "noticed-electric",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>Unnamed: 1</th>\n",
-       "      <th>node2.1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node1_labDesc</th>\n",
-       "      <th>node2_labDesc</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial prototype of the F-16 fighter aircraft</td>\n",
-       "      <td>YF-16 initial prototype of the F-16 fighter ai...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.0003189822, -0.31257084, 0.4071587, 0.4445...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>initial series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16A/B Fighting Falcon initial series of the ...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.066473946, -0.17350475, 0.24638157, 0.4571...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>multirole series of the F-16 fighter aircraft</td>\n",
-       "      <td>F-16C/D Fighting Falcon multirole series of th...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.110435985, -0.09184393, 0.24768817, 0.2641...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>export strike fighter series of the F-16 fight...</td>\n",
-       "      <td>F-16E/F Desert Falcon export strike fighter se...</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.40679273, -0.026886985, 0.032887716, 0.641...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>1944 fighter aircraft family by Oberst R. Lehmann</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>F-16</td>\n",
-       "      <td>experimental aircraft</td>\n",
-       "      <td>F-16 VISTA experimental aircraft</td>\n",
-       "      <td>F-16 1944 fighter aircraft family by Oberst R....</td>\n",
-       "      <td>[-0.18904628, 0.10738095, 0.3032565, 0.6507382...</td>\n",
-       "      <td>[-0.074766316, -0.20337783, 0.23880175, 0.4511...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index    node2  Unnamed: 1  node2.1                                 id  \\\n",
-       "0      0  Q100026           1  Q100026  Q17372279-P279-Q100026-beba8cd1-0   \n",
-       "1      1  Q100026           2  Q100026  Q17372377-P279-Q100026-fd42bd71-0   \n",
-       "2      2  Q100026           3  Q100026  Q17372444-P279-Q100026-ca0fc4bd-0   \n",
-       "3      3  Q100026           4  Q100026  Q17372455-P279-Q100026-c2b1bf36-0   \n",
-       "4      4  Q100026           5  Q100026   Q2029940-P279-Q100026-ceba4380-0   \n",
-       "\n",
-       "       node1 label                                         node2_desc  \\\n",
-       "0  Q17372279  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "1  Q17372377  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "2  Q17372444  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "3  Q17372455  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "4   Q2029940  P279  1944 fighter aircraft family by Oberst R. Lehmann   \n",
-       "\n",
-       "               node1_label node2_label  \\\n",
-       "0                    YF-16        F-16   \n",
-       "1  F-16A/B Fighting Falcon        F-16   \n",
-       "2  F-16C/D Fighting Falcon        F-16   \n",
-       "3    F-16E/F Desert Falcon        F-16   \n",
-       "4               F-16 VISTA        F-16   \n",
-       "\n",
-       "                                          node1_desc  \\\n",
-       "0     initial prototype of the F-16 fighter aircraft   \n",
-       "1        initial series of the F-16 fighter aircraft   \n",
-       "2      multirole series of the F-16 fighter aircraft   \n",
-       "3  export strike fighter series of the F-16 fight...   \n",
-       "4                              experimental aircraft   \n",
-       "\n",
-       "                                       node1_labDesc  \\\n",
-       "0  YF-16 initial prototype of the F-16 fighter ai...   \n",
-       "1  F-16A/B Fighting Falcon initial series of the ...   \n",
-       "2  F-16C/D Fighting Falcon multirole series of th...   \n",
-       "3  F-16E/F Desert Falcon export strike fighter se...   \n",
-       "4                   F-16 VISTA experimental aircraft   \n",
-       "\n",
-       "                                       node2_labDesc  \\\n",
-       "0  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "1  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "2  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "3  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "4  F-16 1944 fighter aircraft family by Oberst R....   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-0.0003189822, -0.31257084, 0.4071587, 0.4445...   \n",
-       "1  [-0.066473946, -0.17350475, 0.24638157, 0.4571...   \n",
-       "2  [-0.110435985, -0.09184393, 0.24768817, 0.2641...   \n",
-       "3  [-0.40679273, -0.026886985, 0.032887716, 0.641...   \n",
-       "4  [-0.18904628, 0.10738095, 0.3032565, 0.6507382...   \n",
-       "\n",
-       "                                           node2_emb  \n",
-       "0  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "1  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "2  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "3  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  \n",
-       "4  [-0.074766316, -0.20337783, 0.23880175, 0.4511...  "
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "surrounded-thinking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF.node1_emb.isna().sum(), hierDF.node2_emb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "frozen-console",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF['similarity_value'] = hierDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "optimum-sewing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF[['id', 'node1', 'label', 'node2', 'similarity_value']].to_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "selective-nashville",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    304654.000000\n",
-       "mean          0.499092\n",
-       "std           0.186887\n",
-       "min          -0.130950\n",
-       "25%           0.371610\n",
-       "50%           0.499928\n",
-       "75%           0.626436\n",
-       "max           0.989182\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "interior-treat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba6879c669da4812919bee809747ece9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/747 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Alt scoring\n",
-    "# from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "# crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "# model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "# crossEncSimVals = model.predict(list(zip(hierDF.node1_labDesc.to_list(), hierDF.node2_labDesc.to_list())), show_progress_bar=True, batch_size=500)\n",
-    "# hierDF['similarity_value'] = crossEncSimVals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consolidated-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "secret-buddy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confirmed-canberra",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "controlled-perfume",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "through-penalty",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "broke-tolerance",
-   "metadata": {},
-   "source": [
-    "## P279 Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "going-custom",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  id      node1 label    node2  \\\n",
-       "0  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279  Q100026   \n",
-       "1  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  P279  Q100026   \n",
-       "2  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  P279  Q100026   \n",
-       "3  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455  P279  Q100026   \n",
-       "4   Q2029940-P279-Q100026-ceba4380-0   Q2029940  P279  Q100026   \n",
-       "\n",
-       "   similarity_value  \n",
-       "0          0.592530  \n",
-       "1          0.660948  \n",
-       "2          0.634662  \n",
-       "3          0.552555  \n",
-       "4          0.504576  "
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "hierDF_transP279_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "normal-playback",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_left = hierDF_transP279_filtered.set_index('node2')\n",
-    "hierDF_transP279_filtered_right = hierDF_transP279_filtered.copy().set_index('node2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "interpreted-elimination",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.04 s, sys: 684 ms, total: 2.72 s\n",
-      "Wall time: 2.72 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "hierDF_transP279_filtered_sibs = hierDF_transP279_filtered_left.join(hierDF_transP279_filtered_right, rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "id": "consolidated-hampshire",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26426178"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "blessed-microwave",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100026</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        id      node1 label  similarity_value  \\\n",
-       "node2                                                                           \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279           0.59253   \n",
-       "\n",
-       "                                  id_right node1_right label_right  \\\n",
-       "node2                                                                \n",
-       "Q100026  Q17372279-P279-Q100026-beba8cd1-0   Q17372279        P279   \n",
-       "Q100026  Q17372377-P279-Q100026-fd42bd71-0   Q17372377        P279   \n",
-       "Q100026  Q17372444-P279-Q100026-ca0fc4bd-0   Q17372444        P279   \n",
-       "Q100026  Q17372455-P279-Q100026-c2b1bf36-0   Q17372455        P279   \n",
-       "Q100026   Q2029940-P279-Q100026-ceba4380-0    Q2029940        P279   \n",
-       "\n",
-       "         similarity_value_right  \n",
-       "node2                            \n",
-       "Q100026                0.592530  \n",
-       "Q100026                0.660948  \n",
-       "Q100026                0.634662  \n",
-       "Q100026                0.552555  \n",
-       "Q100026                0.504576  "
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "compliant-measure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.drop(columns=['label_right']).reset_index().rename(columns={'node2': 'par_node', 'node1_right': 'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "anticipated-occasion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>0.592530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>0.660948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>0.634662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>0.552555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.59253</td>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>0.504576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  par_node                                 id      node1 label  \\\n",
-       "0  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "1  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "2  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "3  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "4  Q100026  Q17372279-P279-Q100026-beba8cd1-0  Q17372279  P279   \n",
-       "\n",
-       "   similarity_value                           id_right      node2  \\\n",
-       "0           0.59253  Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "1           0.59253  Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "2           0.59253  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "3           0.59253  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "4           0.59253   Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "\n",
-       "   similarity_value_right  \n",
-       "0                0.592530  \n",
-       "1                0.660948  \n",
-       "2                0.634662  \n",
-       "3                0.552555  \n",
-       "4                0.504576  "
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "steady-jacket",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26426178"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "italian-slovak",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1 != hierDF_transP279_filtered_sibs1.node2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "digital-oxygen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26121338"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF_transP279_filtered_sibs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "fewer-rebel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1.to_csv(P279_SIBLINGS_INTERM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "failing-accent",
-   "metadata": {},
-   "source": [
-    "### Add labels + descs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "controlled-width",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "id": "prompt-grade",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs = pd.read_csv(P279_SIBLINGS_INTERM_FILE)\n",
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs.iloc[sample_without_replacement(len(hierDF_transP279_filtered_sibs), 1000000, random_state=13)]\n",
-    "hierDF_transP279_filtered_sibs1 = hierDF_transP279_filtered_sibs1.reset_index()\n",
-    "print(len(hierDF_transP279_filtered_sibs1))\n",
-    "labelsDF = pd.read_csv(LABELS_FILE, compression='gzip', sep='\\t')\n",
-    "hierDF_transP279_filtered_set = set(hierDF_transP279_filtered_sibs1.node1.to_list() + hierDF_transP279_filtered_sibs1.node2.to_list() + hierDF_transP279_filtered_sibs1.par_node.to_list())\n",
-    "labelsDF = labelsDF[labelsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]\n",
-    "labelsDict = {row['node1']: row['node2'] for _, row in labelsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}\n",
-    "descriptionsDF = pd.read_csv(DESCRIPTIONS_FILE, compression='gzip', sep='\\t')\n",
-    "descriptionsDF = descriptionsDF[descriptionsDF.node1.apply(lambda p: p in hierDF_transP279_filtered_set)]\n",
-    "descDict = {row['node1']: row['node2'] for _, row in descriptionsDF.iterrows() if row['node1'] in hierDF_transP279_filtered_set}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "id": "american-porter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs1['node1_label'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_label'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['par_label'] = hierDF_transP279_filtered_sibs1.par_node.apply(lambda p: labelsDict[p][1:-4] if p in labelsDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node1_desc'] = hierDF_transP279_filtered_sibs1.node1.apply(lambda p: descDict[p][1:-4] if p in descDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node2_desc'] = hierDF_transP279_filtered_sibs1.node2.apply(lambda p: descDict[p][1:-4] if p in descDict else None)\n",
-    "hierDF_transP279_filtered_sibs1['node1_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node1_label'] + ' ' + p['node1_desc'] + ' is ' + p['par_label'],axis=1)\n",
-    "hierDF_transP279_filtered_sibs1['node2_sent'] = hierDF_transP279_filtered_sibs1.apply(lambda p: p['node2_label'] + ' ' + p['node2_desc'] + ' is ' + p['par_label'],axis=1)\n",
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs1[hierDF_transP279_filtered_sibs1.node1_desc != hierDF_transP279_filtered_sibs1.node2_desc]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "official-separation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset reduced from 1000000 to 785418 by eliminating rows where node1_desc == node2_desc\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset reduced from {len(hierDF_transP279_filtered_sibs1)} to {len(hierDF_transP279_filtered_sibs2)} by eliminating rows where node1_desc == node2_desc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "id": "weighted-mirror",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>par_node</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>similarity_value</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>similarity_value_right</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_desc</th>\n",
-       "      <th>node2_desc</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>24998287</td>\n",
-       "      <td>Q89</td>\n",
-       "      <td>Q96627253-P279-Q89-51483924-0</td>\n",
-       "      <td>Q96627253</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.460166</td>\n",
-       "      <td>Q595003-P279-Q89-ddc59387-0</td>\n",
-       "      <td>Q595003</td>\n",
-       "      <td>0.553182</td>\n",
-       "      <td>Sevillana (manzana)</td>\n",
-       "      <td>Calville Blanc d\\'hiver</td>\n",
-       "      <td>apple</td>\n",
-       "      <td>apple cultivar</td>\n",
-       "      <td>apple cultivar</td>\n",
-       "      <td>Sevillana (manzana) apple cultivar is apple</td>\n",
-       "      <td>Calville Blanc d\\'hiver apple cultivar is apple</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6348351</td>\n",
-       "      <td>Q17589470</td>\n",
-       "      <td>Q56942-P279-Q17589470-d0932570-0</td>\n",
-       "      <td>Q56942</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.527086</td>\n",
-       "      <td>Q69974281-P279-Q17589470-ed5eb339-0</td>\n",
-       "      <td>Q69974281</td>\n",
-       "      <td>0.466616</td>\n",
-       "      <td>Wii U</td>\n",
-       "      <td>Gamatic 7600</td>\n",
-       "      <td>home video game console</td>\n",
-       "      <td>eight-generation home video game console by Ni...</td>\n",
-       "      <td>Pong console. The same of Otron Gamatic 7600</td>\n",
-       "      <td>Wii U eight-generation home video game console...</td>\n",
-       "      <td>Gamatic 7600 Pong console. The same of Otron G...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3244723</td>\n",
-       "      <td>Q13357858</td>\n",
-       "      <td>Q63845311-P279-Q13357858-c40a679c-0</td>\n",
-       "      <td>Q63845311</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.695520</td>\n",
-       "      <td>Q79132633-P279-Q13357858-4ccf52ce-0</td>\n",
-       "      <td>Q79132633</td>\n",
-       "      <td>0.679767</td>\n",
-       "      <td>2013 World Senior Badminton Championships O35</td>\n",
-       "      <td>Slovak Badminton Championships U17</td>\n",
-       "      <td>badminton tournament</td>\n",
-       "      <td>badminton championships</td>\n",
-       "      <td>badminton championships</td>\n",
-       "      <td>2013 World Senior Badminton Championships O35 ...</td>\n",
-       "      <td>Slovak Badminton Championships U17 badminton c...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5923963</td>\n",
-       "      <td>Q174736</td>\n",
-       "      <td>Q583164-P279-Q174736-262a2fe0-0</td>\n",
-       "      <td>Q583164</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.459580</td>\n",
-       "      <td>Q2692256-P279-Q174736-e1333ac9-0</td>\n",
-       "      <td>Q2692256</td>\n",
-       "      <td>0.592024</td>\n",
-       "      <td>Project 30bis</td>\n",
-       "      <td>Bagley-class destroyer</td>\n",
-       "      <td>destroyer</td>\n",
-       "      <td>1949 Soviet destroyer class</td>\n",
-       "      <td>class of U.S. destroyers</td>\n",
-       "      <td>Project 30bis 1949 Soviet destroyer class is d...</td>\n",
-       "      <td>Bagley-class destroyer class of U.S. destroyer...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10867070</td>\n",
-       "      <td>Q215627</td>\n",
-       "      <td>Q19840821-P279-Q215627-eb7ec03a-0</td>\n",
-       "      <td>Q19840821</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.201813</td>\n",
-       "      <td>Q40554819-P279-Q215627-95d720c4-0</td>\n",
-       "      <td>Q40554819</td>\n",
-       "      <td>0.104633</td>\n",
-       "      <td>Singaporeans</td>\n",
-       "      <td>Sicilians</td>\n",
-       "      <td>person</td>\n",
-       "      <td>citizens or residents of Singapore</td>\n",
-       "      <td>ethnic group indigenous to the island of Sicil...</td>\n",
-       "      <td>Singaporeans citizens or residents of Singapor...</td>\n",
-       "      <td>Sicilians ethnic group indigenous to the islan...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999995</th>\n",
-       "      <td>15478058</td>\n",
-       "      <td>Q3382119</td>\n",
-       "      <td>Q75086913-P279-Q3382119-a9ff163a-0</td>\n",
-       "      <td>Q75086913</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.769255</td>\n",
-       "      <td>Q75828417-P279-Q3382119-59b8950c-0</td>\n",
-       "      <td>Q75828417</td>\n",
-       "      <td>0.621310</td>\n",
-       "      <td>Sub-prefect of Nogent-le-Rotrou</td>\n",
-       "      <td>Sub-prefect of Pont-Audemer</td>\n",
-       "      <td>sub-prefect</td>\n",
-       "      <td>French official position</td>\n",
-       "      <td>former French official position (1800-1926)</td>\n",
-       "      <td>Sub-prefect of Nogent-le-Rotrou French officia...</td>\n",
-       "      <td>Sub-prefect of Pont-Audemer former French offi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999996</th>\n",
-       "      <td>23394433</td>\n",
-       "      <td>Q785745</td>\n",
-       "      <td>Q23022056-P279-Q785745-f48ca89b-0</td>\n",
-       "      <td>Q23022056</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.405016</td>\n",
-       "      <td>Q3511974-P279-Q785745-42da354d-0</td>\n",
-       "      <td>Q3511974</td>\n",
-       "      <td>0.306730</td>\n",
-       "      <td>Tasmanian Government Railways J class</td>\n",
-       "      <td>Alsace-Lorraine E 6</td>\n",
-       "      <td>tank locomotive</td>\n",
-       "      <td>class of 1 Australian 2-6-4-0T locomotive</td>\n",
-       "      <td>class of 13 German (later French) metre-gauge ...</td>\n",
-       "      <td>Tasmanian Government Railways J class class of...</td>\n",
-       "      <td>Alsace-Lorraine E 6 class of 13 German (later ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999997</th>\n",
-       "      <td>19035228</td>\n",
-       "      <td>Q483373</td>\n",
-       "      <td>Q682971-P279-Q483373-6ff4adf5-0</td>\n",
-       "      <td>Q682971</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.314112</td>\n",
-       "      <td>Q4598091-P279-Q483373-fd15b986-0</td>\n",
-       "      <td>Q4598091</td>\n",
-       "      <td>0.213652</td>\n",
-       "      <td>Rhaetian Railway ABe 4/16</td>\n",
-       "      <td>CTA 2000 series</td>\n",
-       "      <td>electric multiple unit</td>\n",
-       "      <td>multiple unit</td>\n",
-       "      <td>class of Chicago Transit Authority cars</td>\n",
-       "      <td>Rhaetian Railway ABe 4/16 multiple unit is ele...</td>\n",
-       "      <td>CTA 2000 series class of Chicago Transit Autho...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999998</th>\n",
-       "      <td>20618370</td>\n",
-       "      <td>Q625151</td>\n",
-       "      <td>Q841350-P279-Q625151-bd2c0728-0</td>\n",
-       "      <td>Q841350</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.459560</td>\n",
-       "      <td>Q463577-P279-Q625151-4cfe245b-0</td>\n",
-       "      <td>Q463577</td>\n",
-       "      <td>0.466203</td>\n",
-       "      <td>OSE class 120</td>\n",
-       "      <td>BLS Re 4/4</td>\n",
-       "      <td>electric locomotive</td>\n",
-       "      <td>class of Greek electric locomotives</td>\n",
-       "      <td>class of 35 Swiss electric locomoties</td>\n",
-       "      <td>OSE class 120 class of Greek electric locomoti...</td>\n",
-       "      <td>BLS Re 4/4 class of 35 Swiss electric locomoti...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999999</th>\n",
-       "      <td>2023217</td>\n",
-       "      <td>Q11513337</td>\n",
-       "      <td>Q13382122-P279-Q11513337-2d44cd4f-0</td>\n",
-       "      <td>Q13382122</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>0.559249</td>\n",
-       "      <td>Q13854733-P279-Q11513337-8c2aca91-0</td>\n",
-       "      <td>Q13854733</td>\n",
-       "      <td>0.626328</td>\n",
-       "      <td>high jumper</td>\n",
-       "      <td>steeplechase runner</td>\n",
-       "      <td>athletics competitor</td>\n",
-       "      <td>sportsperson taking part in high jump competit...</td>\n",
-       "      <td>sportsperson taking part in steeplechasing com...</td>\n",
-       "      <td>high jumper sportsperson taking part in high j...</td>\n",
-       "      <td>steeplechase runner sportsperson taking part i...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000000 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           index   par_node                                   id      node1  \\\n",
-       "0       24998287        Q89        Q96627253-P279-Q89-51483924-0  Q96627253   \n",
-       "1        6348351  Q17589470     Q56942-P279-Q17589470-d0932570-0     Q56942   \n",
-       "2        3244723  Q13357858  Q63845311-P279-Q13357858-c40a679c-0  Q63845311   \n",
-       "3        5923963    Q174736      Q583164-P279-Q174736-262a2fe0-0    Q583164   \n",
-       "4       10867070    Q215627    Q19840821-P279-Q215627-eb7ec03a-0  Q19840821   \n",
-       "...          ...        ...                                  ...        ...   \n",
-       "999995  15478058   Q3382119   Q75086913-P279-Q3382119-a9ff163a-0  Q75086913   \n",
-       "999996  23394433    Q785745    Q23022056-P279-Q785745-f48ca89b-0  Q23022056   \n",
-       "999997  19035228    Q483373      Q682971-P279-Q483373-6ff4adf5-0    Q682971   \n",
-       "999998  20618370    Q625151      Q841350-P279-Q625151-bd2c0728-0    Q841350   \n",
-       "999999   2023217  Q11513337  Q13382122-P279-Q11513337-2d44cd4f-0  Q13382122   \n",
-       "\n",
-       "       label  similarity_value                             id_right  \\\n",
-       "0       P279          0.460166          Q595003-P279-Q89-ddc59387-0   \n",
-       "1       P279          0.527086  Q69974281-P279-Q17589470-ed5eb339-0   \n",
-       "2       P279          0.695520  Q79132633-P279-Q13357858-4ccf52ce-0   \n",
-       "3       P279          0.459580     Q2692256-P279-Q174736-e1333ac9-0   \n",
-       "4       P279          0.201813    Q40554819-P279-Q215627-95d720c4-0   \n",
-       "...      ...               ...                                  ...   \n",
-       "999995  P279          0.769255   Q75828417-P279-Q3382119-59b8950c-0   \n",
-       "999996  P279          0.405016     Q3511974-P279-Q785745-42da354d-0   \n",
-       "999997  P279          0.314112     Q4598091-P279-Q483373-fd15b986-0   \n",
-       "999998  P279          0.459560      Q463577-P279-Q625151-4cfe245b-0   \n",
-       "999999  P279          0.559249  Q13854733-P279-Q11513337-8c2aca91-0   \n",
-       "\n",
-       "            node2  similarity_value_right  \\\n",
-       "0         Q595003                0.553182   \n",
-       "1       Q69974281                0.466616   \n",
-       "2       Q79132633                0.679767   \n",
-       "3        Q2692256                0.592024   \n",
-       "4       Q40554819                0.104633   \n",
-       "...           ...                     ...   \n",
-       "999995  Q75828417                0.621310   \n",
-       "999996   Q3511974                0.306730   \n",
-       "999997   Q4598091                0.213652   \n",
-       "999998    Q463577                0.466203   \n",
-       "999999  Q13854733                0.626328   \n",
-       "\n",
-       "                                          node1_label  \\\n",
-       "0                                 Sevillana (manzana)   \n",
-       "1                                               Wii U   \n",
-       "2       2013 World Senior Badminton Championships O35   \n",
-       "3                                       Project 30bis   \n",
-       "4                                        Singaporeans   \n",
-       "...                                               ...   \n",
-       "999995                Sub-prefect of Nogent-le-Rotrou   \n",
-       "999996          Tasmanian Government Railways J class   \n",
-       "999997                      Rhaetian Railway ABe 4/16   \n",
-       "999998                                  OSE class 120   \n",
-       "999999                                    high jumper   \n",
-       "\n",
-       "                               node2_label                par_label  \\\n",
-       "0                  Calville Blanc d\\'hiver                    apple   \n",
-       "1                             Gamatic 7600  home video game console   \n",
-       "2       Slovak Badminton Championships U17     badminton tournament   \n",
-       "3                   Bagley-class destroyer                destroyer   \n",
-       "4                                Sicilians                   person   \n",
-       "...                                    ...                      ...   \n",
-       "999995         Sub-prefect of Pont-Audemer              sub-prefect   \n",
-       "999996                 Alsace-Lorraine E 6          tank locomotive   \n",
-       "999997                     CTA 2000 series   electric multiple unit   \n",
-       "999998                          BLS Re 4/4      electric locomotive   \n",
-       "999999                 steeplechase runner     athletics competitor   \n",
-       "\n",
-       "                                               node1_desc  \\\n",
-       "0                                          apple cultivar   \n",
-       "1       eight-generation home video game console by Ni...   \n",
-       "2                                 badminton championships   \n",
-       "3                             1949 Soviet destroyer class   \n",
-       "4                      citizens or residents of Singapore   \n",
-       "...                                                   ...   \n",
-       "999995                           French official position   \n",
-       "999996          class of 1 Australian 2-6-4-0T locomotive   \n",
-       "999997                                      multiple unit   \n",
-       "999998                class of Greek electric locomotives   \n",
-       "999999  sportsperson taking part in high jump competit...   \n",
-       "\n",
-       "                                               node2_desc  \\\n",
-       "0                                          apple cultivar   \n",
-       "1            Pong console. The same of Otron Gamatic 7600   \n",
-       "2                                 badminton championships   \n",
-       "3                                class of U.S. destroyers   \n",
-       "4       ethnic group indigenous to the island of Sicil...   \n",
-       "...                                                   ...   \n",
-       "999995        former French official position (1800-1926)   \n",
-       "999996  class of 13 German (later French) metre-gauge ...   \n",
-       "999997            class of Chicago Transit Authority cars   \n",
-       "999998              class of 35 Swiss electric locomoties   \n",
-       "999999  sportsperson taking part in steeplechasing com...   \n",
-       "\n",
-       "                                               node1_sent  \\\n",
-       "0             Sevillana (manzana) apple cultivar is apple   \n",
-       "1       Wii U eight-generation home video game console...   \n",
-       "2       2013 World Senior Badminton Championships O35 ...   \n",
-       "3       Project 30bis 1949 Soviet destroyer class is d...   \n",
-       "4       Singaporeans citizens or residents of Singapor...   \n",
-       "...                                                   ...   \n",
-       "999995  Sub-prefect of Nogent-le-Rotrou French officia...   \n",
-       "999996  Tasmanian Government Railways J class class of...   \n",
-       "999997  Rhaetian Railway ABe 4/16 multiple unit is ele...   \n",
-       "999998  OSE class 120 class of Greek electric locomoti...   \n",
-       "999999  high jumper sportsperson taking part in high j...   \n",
-       "\n",
-       "                                               node2_sent  \n",
-       "0         Calville Blanc d\\'hiver apple cultivar is apple  \n",
-       "1       Gamatic 7600 Pong console. The same of Otron G...  \n",
-       "2       Slovak Badminton Championships U17 badminton c...  \n",
-       "3       Bagley-class destroyer class of U.S. destroyer...  \n",
-       "4       Sicilians ethnic group indigenous to the islan...  \n",
-       "...                                                   ...  \n",
-       "999995  Sub-prefect of Pont-Audemer former French offi...  \n",
-       "999996  Alsace-Lorraine E 6 class of 13 German (later ...  \n",
-       "999997  CTA 2000 series class of Chicago Transit Autho...  \n",
-       "999998  BLS Re 4/4 class of 35 Swiss electric locomoti...  \n",
-       "999999  steeplechase runner sportsperson taking part i...  \n",
-       "\n",
-       "[1000000 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered_sibs1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "id": "third-canon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff153c8a9ba8458a8c4edf70cee2648d",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "693.1311120986938 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception when trying to download https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip. Response 404\n",
-      "SentenceTransformer-Model https://sbert.net/models/sentence-transformers/all-distilroberta-v1.zip not found. Try to create it from scratch\n",
-      "Try to create Transformer Model sentence-transformers/all-distilroberta-v1 with mean pooling\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4beaeda275a64f0b867ab8f25b138435",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "688.8171112537384 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "count    785418.000000\n",
-       "mean          0.512485\n",
-       "std           0.160710\n",
-       "min          -0.145408\n",
-       "25%           0.399946\n",
-       "50%           0.515756\n",
-       "75%           0.632409\n",
-       "max           0.997993\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName, device='cuda:0')\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list(), show_progress_bar=True, batch_size=1000)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "modelName = 'sentence-transformers/all-distilroberta-v1'\n",
-    "hierDF_transP279_filtered_sibs2 = hierDF_transP279_filtered_sibs2.reset_index()\n",
-    "hierDF_transP279_filtered_sibs2['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node1_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs2['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF_transP279_filtered_sibs2.node2_sent, modelName)))\n",
-    "hierDF_transP279_filtered_sibs2['similarity_value'] = hierDF_transP279_filtered_sibs2.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)\n",
-    "hierDF_transP279_filtered_sibs2['similarity_value'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "id": "invalid-yahoo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered_sibs2[['id', 'node1', 'label', 'node2', 'par_node', 'similarity_value']].to_csv(P279_SIBLINGS_DISTILBERT_COSSIM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "infrared-success",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-    },
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-      "application/vnd.jupyter.widget-view+json": {
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-    },
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fea59ce42344dfaa116390c720ade24",
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4f09ee455fd4edcb3d7cde46fa6b141",
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-    },
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ed068be329844b696662aaa852d938c",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84eafe207915471b804c37f346d69474",
-       "version_major": 2,
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56aa42aa7cbb4980992bbe5feb3078db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# # Alt scoring\n",
-    "# from sentence_transformers.cross_encoder import CrossEncoder\n",
-    "# crossEncModel = 'cross-encoder/stsb-roberta-large'\n",
-    "# model = CrossEncoder(crossEncModel, device='cuda:3')\n",
-    "# crossEncSimVals = model.predict(list(zip(hierDF_transP279_filtered_sibs2.node1_sent.to_list(), hierDF_transP279_filtered_sibs2.node2_sent.to_list())), show_progress_bar=True, batch_size=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "olive-exercise",
-   "metadata": {},
-   "source": [
-    "# Probase Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "frozen-warrant",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>size</td>\n",
-       "      <td>33222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>revenue</td>\n",
-       "      <td>33185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>state</td>\n",
-       "      <td>california</td>\n",
-       "      <td>18062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>supplement</td>\n",
-       "      <td>msm glucosamine sulfate</td>\n",
-       "      <td>15942</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               node1_label              node2_label  no_of_relations\n",
-       "0                   factor                      age            35167\n",
-       "1  free rich company datum                     size            33222\n",
-       "2  free rich company datum                  revenue            33185\n",
-       "3                    state               california            18062\n",
-       "4               supplement  msm glucosamine sulfate            15942"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF = pd.read_csv(PROBASE_SOURCE_DIR + 'data-concept/data-concept-instance-relations.txt',header=None, sep='\\t')\n",
-    "probDF.columns=['node1_label', 'node2_label', 'no_of_relations']\n",
-    "probDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "multiple-official",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF['node1'] = probDF.node1_label.apply(lambda p: labels_to_qnodes_dict[p] if p in labels_to_qnodes_dict else None)\n",
-    "probDF['node2'] = probDF.node2_label.apply(lambda p: labels_to_qnodes_dict[p] if p in labels_to_qnodes_dict else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "front-bryan",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "acc4b8e178af4e5ebb4358c8d92fb7b4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/33377320 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def fix_qnodes_to_wsim(row):\n",
-    "    if row.node1_label in wordsim_labels_to_qnodes_dict:\n",
-    "        row['node1'] = wordsim_labels_to_qnodes_dict[row.node1_label]\n",
-    "    if row.node2_label in wordsim_labels_to_qnodes_dict:\n",
-    "        row['node2'] = wordsim_labels_to_qnodes_dict[row.node2_label]\n",
-    "    return row\n",
-    "tqdm.pandas()\n",
-    "probDF1 = probDF.progress_apply(fix_qnodes_to_wsim, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "associate-count",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>Q103858669</td>\n",
-       "      <td>Q100343219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>size</td>\n",
-       "      <td>33222</td>\n",
-       "      <td>None</td>\n",
-       "      <td>Q322481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>free rich company datum</td>\n",
-       "      <td>revenue</td>\n",
-       "      <td>33185</td>\n",
-       "      <td>None</td>\n",
-       "      <td>Q850210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>state</td>\n",
-       "      <td>california</td>\n",
-       "      <td>18062</td>\n",
-       "      <td>P1310</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>supplement</td>\n",
-       "      <td>msm glucosamine sulfate</td>\n",
-       "      <td>15942</td>\n",
-       "      <td>Q2915731</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377315</th>\n",
-       "      <td>popular legacy datum structure</td>\n",
-       "      <td>binary search tree</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377316</th>\n",
-       "      <td>norwegian food</td>\n",
-       "      <td>lefse</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377317</th>\n",
-       "      <td>freeze skill</td>\n",
-       "      <td>new fot</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377318</th>\n",
-       "      <td>enhanced enforcement initiative</td>\n",
-       "      <td>monthly impact inspections of problem mine</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33377319</th>\n",
-       "      <td>enhanced enforcement initiative</td>\n",
-       "      <td>pattern of violation notification</td>\n",
-       "      <td>1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>33377320 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              node1_label  \\\n",
-       "0                                  factor   \n",
-       "1                 free rich company datum   \n",
-       "2                 free rich company datum   \n",
-       "3                                   state   \n",
-       "4                              supplement   \n",
-       "...                                   ...   \n",
-       "33377315   popular legacy datum structure   \n",
-       "33377316                   norwegian food   \n",
-       "33377317                     freeze skill   \n",
-       "33377318  enhanced enforcement initiative   \n",
-       "33377319  enhanced enforcement initiative   \n",
-       "\n",
-       "                                         node2_label  no_of_relations  \\\n",
-       "0                                                age            35167   \n",
-       "1                                               size            33222   \n",
-       "2                                            revenue            33185   \n",
-       "3                                         california            18062   \n",
-       "4                            msm glucosamine sulfate            15942   \n",
-       "...                                              ...              ...   \n",
-       "33377315                          binary search tree                1   \n",
-       "33377316                                       lefse                1   \n",
-       "33377317                                     new fot                1   \n",
-       "33377318  monthly impact inspections of problem mine                1   \n",
-       "33377319           pattern of violation notification                1   \n",
-       "\n",
-       "               node1       node2  \n",
-       "0         Q103858669  Q100343219  \n",
-       "1               None     Q322481  \n",
-       "2               None     Q850210  \n",
-       "3              P1310        None  \n",
-       "4           Q2915731        None  \n",
-       "...              ...         ...  \n",
-       "33377315        None        None  \n",
-       "33377316        None        None  \n",
-       "33377317        None        None  \n",
-       "33377318        None        None  \n",
-       "33377319        None        None  \n",
-       "\n",
-       "[33377320 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "tribal-teens",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33377320"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(probDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "capable-charm",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "955210"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "((~probDF1.node1.isna()) & (~probDF1.node2.isna())).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "southwest-fortune",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "factor                         364111\n",
-       "feature                        203549\n",
-       "issue                          201986\n",
-       "product                        172106\n",
-       "item                           158829\n",
-       "                                ...  \n",
-       "non opec supplier                   1\n",
-       "proactive industry group            1\n",
-       "car s important information         1\n",
-       "mesofronts                          1\n",
-       "bucket related offer                1\n",
-       "Name: node1_label, Length: 5376524, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF1.node1_label.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "aggressive-richardson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF1.to_csv(PROBASE_INTERM_FILE,index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "received-unemployment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes = pd.read_csv(PROBASE_INTERM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "amino-sarah",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes[(~probDF_Qnodes_DF_WQnodes.node1.isna()) & (~probDF_Qnodes_DF_WQnodes.node2.isna())]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "hindu-roberts",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    955210.000000\n",
-       "mean         10.028577\n",
-       "std          85.410631\n",
-       "min           1.000000\n",
-       "25%           1.000000\n",
-       "50%           2.000000\n",
-       "75%           5.000000\n",
-       "max       35167.000000\n",
-       "Name: no_of_relations, dtype: float64"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "contained-tamil",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-36-38e5ab73861c>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  probDF_Qnodes_DF_WQnodes1_subset['similarity_value'] = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].apply(np.log10) / np.log10(maxNR)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "count    955210.000000\n",
-       "mean          0.091717\n",
-       "std           0.112667\n",
-       "min           0.000000\n",
-       "25%           0.000000\n",
-       "50%           0.066217\n",
-       "75%           0.153750\n",
-       "max           1.000000\n",
-       "Name: similarity_value, dtype: float64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "maxNR = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].max()\n",
-    "probDF_Qnodes_DF_WQnodes1_subset['similarity_value'] = probDF_Qnodes_DF_WQnodes1_subset['no_of_relations'].apply(np.log10) / np.log10(maxNR)\n",
-    "probDF_Qnodes_DF_WQnodes1_subset.similarity_value.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "coastal-composition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.to_csv(PROBASE_FINAL_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "polyphonic-louisiana",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "neither-consumer",
-   "metadata": {},
-   "source": [
-    "# Class Similarity Datasets\n",
-    "\n",
-    "`zcat derived.dwd_isa_class_count.compact.tsv.gz | cut -f 3 | tail -n +2 | sed -e 's/|/\\n/g' -e 's/:/\\t/g' | sort | uniq > class-counts.tsv`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "short-heritage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row, similarity_type):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=\"+similarity_type)\n",
-    "    try:\n",
-    "        row['similarity_value'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['similarity_value'] = None\n",
-    "    return row"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "interested-boxing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childPar_class = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "alternate-trance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e5667a15f08d470ca3907d5200ca13e7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/304654 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-205-cc4159d2906b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mP279childPar_class_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mP279childPar_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetchClassSim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    808\u001b[0m                 \u001b[0;31m# on the df using our wrapper (which provides bar updating)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    811\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7763\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7764\u001b[0m         )\n\u001b[0;32m-> 7765\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    803\u001b[0m                     \u001b[0;31m# take a fast or slow code path; so stop when t.total==t.n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m                 \u001b[0;31m# Apply the provided function (in **kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-201-4df33f71c8eb>\u001b[0m in \u001b[0;36mfetchClassSim\u001b[0;34m(row, similarity_type)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfetchClassSim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimilarity_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://kgtk.isi.edu/similarity_api?q1=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"&q2=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"&embedding_type=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msimilarity_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'similarity_value'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'similarity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    540\u001b[0m         }\n\u001b[1;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sock\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_default_certs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         self.sock = ssl_wrap_socket(\n\u001b[0m\u001b[1;32m    412\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m             \u001b[0mkeyfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/urllib3/util/ssl_.py\u001b[0m in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mca_certs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mca_cert_dir\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mca_cert_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m             \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_verify_locations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mca_certs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mca_cert_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mca_cert_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "tqdm.pandas()\n",
-    "P279childPar_class_new = P279childPar_class.progress_apply(fetchClassSim, axis=1, args=('class',))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adolescent-nurse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childPar_class_new.to_csv(P279_CHILD_PAR_CLASSSIM_FILE, index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "warming-vertical",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "id": "imported-mounting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0f3c043918e4634a2dc5bdc7f767c7a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/304654 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-202-93d379bb189a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mP279childPar_class_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetchClassSim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'class'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP279childPar_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP279childPar_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1055\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    931\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    432\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "P279childPar_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row, 'class') for _, row in tqdm(P279childPar_class.iterrows(), total=len(P279childPar_class)))\n",
-    "                                                                                                                                                                                                                                                                                  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spread-snapshot",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "supposed-newport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "reliable-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2[['node1', 'node2']].rename(columns={'node1':'q1', 'node2':'q2'}).to_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_min_cols_FOR_CLASS.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "fifty-garden",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21f6e6799b7d425fa5c2d3d79d6ae2dc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "215bbc5968f94456a3a50e8d182f06d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "class_dict = {}\n",
-    "hierDF_transP279_filtered = pd.read_csv(P279_CHILD_PAR_DISTILBERT_COSSIM_FILE)\n",
-    "p279QnodesList = set(hierDF_transP279_filtered.node1.to_list() + hierDF_transP279_filtered.node2.to_list())\n",
-    "with gzip.open(DERIVED_IS_COUNTS_FILE) as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.decode('utf-8').strip().split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            class_dict[line[0]] = line[2].strip()\n",
-    "all_class_count_dict = {}\n",
-    "with open(CLASS_COUNTS_FILE) as class_file:\n",
-    "    for line in tqdm(class_file):\n",
-    "        line = line.split('\\t')\n",
-    "        if line[0] in p279QnodesList:\n",
-    "            all_class_count_dict[line[0]] = line[1].strip()\n",
-    "\n",
-    "\n",
-    "class SemanticSimilarity(object):\n",
-    "    def __init__(self):\n",
-    "#         self.config = config\n",
-    "#         self.embeddings_type = ['class']\n",
-    "        self.N = float(52546967)\n",
-    "        self.all_class_count_dict = all_class_count_dict\n",
-    "        \n",
-    "    def get_qnode_details(self, qnodes: list):\n",
-    "        global class_dict\n",
-    "        qnodes_dict = {}\n",
-    "        qnodes_dict[qnodes[0]] = class_dict.get(qnodes[0],'')\n",
-    "        qnodes_dict[qnodes[1]] = class_dict.get(qnodes[1],'')\n",
-    "        return qnodes_dict\n",
-    "        \n",
-    "    def compute_class_similarity(self, q1, q2):\n",
-    "        qnodes_dict = self.get_qnode_details([q1, q2])\n",
-    "#         print(qnodes_dict)\n",
-    "        feature_dict, feature_count_dict = self.build_qnode_feature_dict(qnodes_dict)\n",
-    "#         print(feature_dict, feature_count_dict)\n",
-    "        normalized_classes_idf = self.normalize_idf_classes(feature_dict, feature_count_dict)\n",
-    "        if q1 in feature_dict and q2 in feature_dict:\n",
-    "            q1_cl = set(feature_dict[q1])\n",
-    "            q2_cl = set(feature_dict[q2])\n",
-    "            q1_q2_intersection = q1_cl.intersection(q2_cl)\n",
-    "\n",
-    "            _similarity = sum([normalized_classes_idf.get(c) for c in q1_q2_intersection])\n",
-    "            return {\n",
-    "                'similarity': _similarity\n",
-    "            }\n",
-    "        return {\n",
-    "            'similarity': None\n",
-    "        }\n",
-    "\n",
-    "    def build_qnode_feature_dict(self, qnodes_dict: dict) -> (dict, dict):\n",
-    "        feature_dict = {}\n",
-    "        feature_count_dict = {}\n",
-    "\n",
-    "        for qnode in qnodes_dict:\n",
-    "            feature_val = []\n",
-    "            \n",
-    "            if '|' in qnodes_dict[qnode]:\n",
-    "                cl = qnodes_dict[qnode].split(\"|\")\n",
-    "\n",
-    "                for c in cl:\n",
-    "                    vals = c.split(\":\")\n",
-    "                    feature_val.append(vals[0])\n",
-    "                    feature_count_dict[vals[0]] = float(vals[1])\n",
-    "            if qnode not in feature_val:\n",
-    "                feature_val.append(qnode)\n",
-    "            if qnode not in feature_count_dict:\n",
-    "                feature_count_dict[qnode] = float(self.all_class_count_dict.get(qnode, 1.0))\n",
-    "            feature_dict[qnode] = feature_val\n",
-    "\n",
-    "        return feature_dict, feature_count_dict\n",
-    "\n",
-    "    def normalize_idf_classes(self, feature_dict, feature_count_dict):\n",
-    "\n",
-    "        classes_count = {}\n",
-    "        for qnode in feature_dict:\n",
-    "\n",
-    "            classes = feature_dict[qnode]\n",
-    "            for c in classes:\n",
-    "                if c not in classes_count:\n",
-    "                    classes_count[c] = 0\n",
-    "                classes_count[c] += 1\n",
-    "\n",
-    "        classes_idf = self.calculate_idf_features(feature_count_dict)\n",
-    "\n",
-    "        # multiply class count with idf\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_count[c] * classes_idf[c]\n",
-    "\n",
-    "        # normalize the idf scores so that they sum to 1\n",
-    "        classes_idf_sum = sum([classes_idf[x] for x in classes_idf])\n",
-    "        for c in classes_idf:\n",
-    "            classes_idf[c] = classes_idf[c] / classes_idf_sum\n",
-    "\n",
-    "        return classes_idf\n",
-    "\n",
-    "    def calculate_idf_features(self, feature_count_dict):\n",
-    "        _ = {}\n",
-    "        for c in feature_count_dict:\n",
-    "            _[c] = math.log(self.N / feature_count_dict[c])\n",
-    "        return _"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "streaming-match",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flying-perception",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "# import sys\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "#     try:\n",
-    "#         row['classSim'] = float(resp.json()['similarity']) if resp else -1\n",
-    "#     except Exception as exc:\n",
-    "#         print(exc)\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     row['Resp_code'] = resp\n",
-    "#     return row\n",
-    "\n",
-    "# hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "empirical-suspect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "helpful-medline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "activated-excuse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "baking-input",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cognitive-decade",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "pacific-warehouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outdoor-sense",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_3M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "handy-coral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "# from tqdm.notebook import tqdm\n",
-    "# import json\n",
-    "# from joblib import Parallel, delayed\n",
-    "\n",
-    "# P279_10M_data_class_arr = []\n",
-    "\n",
-    "# def fetchClassSim(row):\n",
-    "#     try:\n",
-    "#         resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "#         row['classSim'] = float(resp) if resp else -1\n",
-    "#     except:\n",
-    "#         print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "#         row['classSim'] = None\n",
-    "#     return row\n",
-    "\n",
-    "# P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wired-treatment",
-   "metadata": {},
-   "source": [
-    "# JC Similarity Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "textile-productivity",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "aggregate-tiger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2 = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_filtered.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "cardiac-fence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "978c7892341342029d881b0bf31a6f66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/303284 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "could not convert string to float: ''\n",
-      "Resp not found for Q102108504, Q100171002\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "import sys\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = []\n",
-    "\n",
-    "def fetchJCSim(row):\n",
-    "    resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\")\n",
-    "    try:\n",
-    "        row['similarity_value'] = float(resp.json()['similarity']) if resp else -1\n",
-    "    except Exception as exc:\n",
-    "        print(exc)\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['similarity_value'] = None\n",
-    "    row['Resp_code'] = resp\n",
-    "    return row\n",
-    "\n",
-    "hierDF_transP279_filtered2_class_arr = Parallel(n_jobs=5)(delayed(fetchClassSim)(row) for _, row in tqdm(hierDF_transP279_filtered2.iloc[:100].iterrows(), total=len(hierDF_transP279_filtered2)))\n",
-    "                                                                                                                                          \n",
-    "                                                                                                                                          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "attached-novelty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Unnamed: 0                                      0\n",
-       " id              Q17372279-P279-Q100026-beba8cd1-0\n",
-       " node1                                   Q17372279\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.666525\n",
-       " classSim                                 0.835782\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 0, dtype: object,\n",
-       " Unnamed: 0                                      1\n",
-       " id              Q17372377-P279-Q100026-fd42bd71-0\n",
-       " node1                                   Q17372377\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.700515\n",
-       " classSim                                  0.94674\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 1, dtype: object,\n",
-       " Unnamed: 0                                      2\n",
-       " id              Q17372444-P279-Q100026-ca0fc4bd-0\n",
-       " node1                                   Q17372444\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.683245\n",
-       " classSim                                 0.946647\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 2, dtype: object,\n",
-       " Unnamed: 0                                      3\n",
-       " id              Q17372455-P279-Q100026-c2b1bf36-0\n",
-       " node1                                   Q17372455\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                             0.689516\n",
-       " classSim                                 0.943256\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 3, dtype: object,\n",
-       " Unnamed: 0                                     4\n",
-       " id              Q2029940-P279-Q100026-ceba4380-0\n",
-       " node1                                   Q2029940\n",
-       " label                                       P279\n",
-       " node2                                    Q100026\n",
-       " bert2SentSim                            0.603161\n",
-       " classSim                                0.809487\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 4, dtype: object,\n",
-       " Unnamed: 0                                    5\n",
-       " id              Q209480-P279-Q100026-a218e96b-0\n",
-       " node1                                   Q209480\n",
-       " label                                      P279\n",
-       " node2                                   Q100026\n",
-       " bert2SentSim                            0.61352\n",
-       " classSim                               0.975716\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 5, dtype: object,\n",
-       " Unnamed: 0                                      6\n",
-       " id              Q77929250-P279-Q100026-964ec021-0\n",
-       " node1                                   Q77929250\n",
-       " label                                        P279\n",
-       " node2                                     Q100026\n",
-       " bert2SentSim                              0.76767\n",
-       " classSim                                 0.961308\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 6, dtype: object,\n",
-       " Unnamed: 0                                      7\n",
-       " id              Q1087043-P279-Q1000371-945e7797-0\n",
-       " node1                                    Q1087043\n",
-       " label                                        P279\n",
-       " node2                                    Q1000371\n",
-       " bert2SentSim                             0.699843\n",
-       " classSim                                 0.671266\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 7, dtype: object,\n",
-       " Unnamed: 0                                    8\n",
-       " id              Q384656-P279-Q100047-6147b9a8-0\n",
-       " node1                                   Q384656\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 8, dtype: object,\n",
-       " Unnamed: 0                                    9\n",
-       " id              Q471089-P279-Q100047-473b504b-0\n",
-       " node1                                   Q471089\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.679593\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 9, dtype: object,\n",
-       " Unnamed: 0                                   10\n",
-       " id              Q471094-P279-Q100047-14b1b1df-0\n",
-       " node1                                   Q471094\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 10, dtype: object,\n",
-       " Unnamed: 0                                   11\n",
-       " id              Q471102-P279-Q100047-886a847c-0\n",
-       " node1                                   Q471102\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 11, dtype: object,\n",
-       " Unnamed: 0                                   12\n",
-       " id              Q471127-P279-Q100047-6738fc46-0\n",
-       " node1                                   Q471127\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 12, dtype: object,\n",
-       " Unnamed: 0                                   13\n",
-       " id              Q471144-P279-Q100047-766e1fa3-0\n",
-       " node1                                   Q471144\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.811462\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 13, dtype: object,\n",
-       " Unnamed: 0                                   14\n",
-       " id              Q471158-P279-Q100047-7955aa40-0\n",
-       " node1                                   Q471158\n",
-       " label                                      P279\n",
-       " node2                                   Q100047\n",
-       " bert2SentSim                           0.681549\n",
-       " classSim                               0.943953\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 14, dtype: object,\n",
-       " Unnamed: 0                                         15\n",
-       " id              Q100052562-P279-Q100052008-1a4fa432-0\n",
-       " node1                                      Q100052562\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.515993\n",
-       " classSim                                     0.913344\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 15, dtype: object,\n",
-       " Unnamed: 0                                         16\n",
-       " id              Q100052791-P279-Q100052008-1d15b1ae-0\n",
-       " node1                                      Q100052791\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                 0.478483\n",
-       " classSim                                     0.907455\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 16, dtype: object,\n",
-       " Unnamed: 0                                         17\n",
-       " id              Q100119502-P279-Q100052008-d2c330a4-0\n",
-       " node1                                      Q100119502\n",
-       " label                                            P279\n",
-       " node2                                      Q100052008\n",
-       " bert2SentSim                                  0.51221\n",
-       " classSim                                     0.913229\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 17, dtype: object,\n",
-       " Unnamed: 0                                        18\n",
-       " id              Q88594442-P279-Q100052008-badba21b-0\n",
-       " node1                                      Q88594442\n",
-       " label                                           P279\n",
-       " node2                                     Q100052008\n",
-       " bert2SentSim                                0.424672\n",
-       " classSim                                     0.91916\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 18, dtype: object,\n",
-       " Unnamed: 0                                     19\n",
-       " id              Q1133024-P279-Q1000660-6d8a1a17-0\n",
-       " node1                                    Q1133024\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.556017\n",
-       " classSim                                  0.96031\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 19, dtype: object,\n",
-       " Unnamed: 0                                     20\n",
-       " id              Q1231309-P279-Q1000660-656bad41-0\n",
-       " node1                                    Q1231309\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.473025\n",
-       " classSim                                 0.987153\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 20, dtype: object,\n",
-       " Unnamed: 0                                     21\n",
-       " id              Q1892554-P279-Q1000660-b60addd4-0\n",
-       " node1                                    Q1892554\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.710084\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 21, dtype: object,\n",
-       " Unnamed: 0                                     22\n",
-       " id              Q2835946-P279-Q1000660-ff0a5cf9-0\n",
-       " node1                                    Q2835946\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                              0.69158\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 22, dtype: object,\n",
-       " Unnamed: 0                                     23\n",
-       " id              Q3413373-P279-Q1000660-9d565f5e-0\n",
-       " node1                                    Q3413373\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.676028\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 23, dtype: object,\n",
-       " Unnamed: 0                                     24\n",
-       " id              Q5611316-P279-Q1000660-6b051008-0\n",
-       " node1                                    Q5611316\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.652025\n",
-       " classSim                                 0.986632\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 24, dtype: object,\n",
-       " Unnamed: 0                                    25\n",
-       " id              Q649977-P279-Q1000660-cc4475c9-0\n",
-       " node1                                    Q649977\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.548393\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 25, dtype: object,\n",
-       " Unnamed: 0                                     26\n",
-       " id              Q7269432-P279-Q1000660-8976e0ff-0\n",
-       " node1                                    Q7269432\n",
-       " label                                        P279\n",
-       " node2                                    Q1000660\n",
-       " bert2SentSim                             0.439381\n",
-       " classSim                                 0.989483\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 26, dtype: object,\n",
-       " Unnamed: 0                                    27\n",
-       " id              Q844980-P279-Q1000660-5f020e98-0\n",
-       " node1                                    Q844980\n",
-       " label                                       P279\n",
-       " node2                                   Q1000660\n",
-       " bert2SentSim                            0.678084\n",
-       " classSim                                0.986632\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 27, dtype: object,\n",
-       " Unnamed: 0                                     28\n",
-       " id              Q1051606-P279-Q1000809-995e5221-0\n",
-       " node1                                    Q1051606\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.740599\n",
-       " classSim                                 0.912943\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 28, dtype: object,\n",
-       " Unnamed: 0                                     29\n",
-       " id              Q4984171-P279-Q1000809-95749f41-0\n",
-       " node1                                    Q4984171\n",
-       " label                                        P279\n",
-       " node2                                    Q1000809\n",
-       " bert2SentSim                             0.808338\n",
-       " classSim                                 0.957835\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 29, dtype: object,\n",
-       " Unnamed: 0                                     30\n",
-       " id              Q2047562-P279-Q1001041-45feee81-0\n",
-       " node1                                    Q2047562\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.359032\n",
-       " classSim                                 0.707243\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 30, dtype: object,\n",
-       " Unnamed: 0                                     31\n",
-       " id              Q8069073-P279-Q1001041-3f13f5bb-0\n",
-       " node1                                    Q8069073\n",
-       " label                                        P279\n",
-       " node2                                    Q1001041\n",
-       " bert2SentSim                             0.769952\n",
-       " classSim                                 0.849692\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 31, dtype: object,\n",
-       " Unnamed: 0                                     32\n",
-       " id              Q1070955-P279-Q1001051-5df03dac-0\n",
-       " node1                                    Q1070955\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.800508\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 32, dtype: object,\n",
-       " Unnamed: 0                                     33\n",
-       " id              Q1153367-P279-Q1001051-34783d55-0\n",
-       " node1                                    Q1153367\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.837581\n",
-       " classSim                                 0.876525\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 33, dtype: object,\n",
-       " Unnamed: 0                                     34\n",
-       " id              Q1958065-P279-Q1001051-d6eb5968-0\n",
-       " node1                                    Q1958065\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.848892\n",
-       " classSim                                 0.707989\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 34, dtype: object,\n",
-       " Unnamed: 0                                    35\n",
-       " id              Q209680-P279-Q1001051-072eeb1b-0\n",
-       " node1                                    Q209680\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.656872\n",
-       " classSim                                0.724838\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 35, dtype: object,\n",
-       " Unnamed: 0                                     36\n",
-       " id              Q2986204-P279-Q1001051-7b21695a-0\n",
-       " node1                                    Q2986204\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.816212\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 36, dtype: object,\n",
-       " Unnamed: 0                                    37\n",
-       " id              Q385530-P279-Q1001051-17db3704-0\n",
-       " node1                                    Q385530\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.75061\n",
-       " classSim                                0.780191\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 37, dtype: object,\n",
-       " Unnamed: 0                                      38\n",
-       " id              Q41795401-P279-Q1001051-5747dcc4-0\n",
-       " node1                                    Q41795401\n",
-       " label                                         P279\n",
-       " node2                                     Q1001051\n",
-       " bert2SentSim                              0.787191\n",
-       " classSim                                   0.88381\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 38, dtype: object,\n",
-       " Unnamed: 0                                    39\n",
-       " id              Q612405-P279-Q1001051-bb6fa17c-0\n",
-       " node1                                    Q612405\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                             0.72366\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 39, dtype: object,\n",
-       " Unnamed: 0                                    40\n",
-       " id              Q668439-P279-Q1001051-bbba9ea3-0\n",
-       " node1                                    Q668439\n",
-       " label                                       P279\n",
-       " node2                                   Q1001051\n",
-       " bert2SentSim                            0.698926\n",
-       " classSim                                 0.86936\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 40, dtype: object,\n",
-       " Unnamed: 0                                     41\n",
-       " id              Q7133779-P279-Q1001051-4fc71627-0\n",
-       " node1                                    Q7133779\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.576617\n",
-       " classSim                                  0.86936\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 41, dtype: object,\n",
-       " Unnamed: 0                                     42\n",
-       " id              Q7630946-P279-Q1001051-2e78f5b5-0\n",
-       " node1                                    Q7630946\n",
-       " label                                        P279\n",
-       " node2                                    Q1001051\n",
-       " bert2SentSim                             0.632298\n",
-       " classSim                                 0.796208\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 42, dtype: object,\n",
-       " Unnamed: 0                                     43\n",
-       " id              Q1056352-P279-Q1001059-c1976180-0\n",
-       " node1                                    Q1056352\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.717131\n",
-       " classSim                                 0.886053\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 43, dtype: object,\n",
-       " Unnamed: 0                                     44\n",
-       " id              Q2123051-P279-Q1001059-18b4564b-0\n",
-       " node1                                    Q2123051\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.606046\n",
-       " classSim                                 0.465094\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 44, dtype: object,\n",
-       " Unnamed: 0                                    45\n",
-       " id              Q444278-P279-Q1001059-0c74af28-0\n",
-       " node1                                    Q444278\n",
-       " label                                       P279\n",
-       " node2                                   Q1001059\n",
-       " bert2SentSim                            0.481163\n",
-       " classSim                                0.881922\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 45, dtype: object,\n",
-       " Unnamed: 0                                     46\n",
-       " id              Q7240696-P279-Q1001059-31dfa177-0\n",
-       " node1                                    Q7240696\n",
-       " label                                        P279\n",
-       " node2                                    Q1001059\n",
-       " bert2SentSim                             0.752948\n",
-       " classSim                                 0.881922\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 46, dtype: object,\n",
-       " Unnamed: 0                                      47\n",
-       " id              Q85776190-P279-Q1001059-b7a4aad6-0\n",
-       " node1                                    Q85776190\n",
-       " label                                         P279\n",
-       " node2                                     Q1001059\n",
-       " bert2SentSim                              0.747289\n",
-       " classSim                                   0.53963\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 47, dtype: object,\n",
-       " Unnamed: 0                                         48\n",
-       " id              Q100052008-P279-Q100116222-d1597eca-0\n",
-       " node1                                      Q100052008\n",
-       " label                                            P279\n",
-       " node2                                      Q100116222\n",
-       " bert2SentSim                                 0.725062\n",
-       " classSim                                     0.909364\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 48, dtype: object,\n",
-       " Unnamed: 0                                        49\n",
-       " id              Q27303706-P279-Q100116222-526530c6-0\n",
-       " node1                                      Q27303706\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.537653\n",
-       " classSim                                    0.917887\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 49, dtype: object,\n",
-       " Unnamed: 0                                        50\n",
-       " id              Q99565932-P279-Q100116222-139f958c-0\n",
-       " node1                                      Q99565932\n",
-       " label                                           P279\n",
-       " node2                                     Q100116222\n",
-       " bert2SentSim                                0.691907\n",
-       " classSim                                    0.892978\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 50, dtype: object,\n",
-       " Unnamed: 0                                     51\n",
-       " id              Q1416523-P279-Q1001329-2b6d0c1e-0\n",
-       " node1                                    Q1416523\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.160931\n",
-       " classSim                                 0.871646\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 51, dtype: object,\n",
-       " Unnamed: 0                                     52\n",
-       " id              Q2963918-P279-Q1001329-c85a352f-0\n",
-       " node1                                    Q2963918\n",
-       " label                                        P279\n",
-       " node2                                    Q1001329\n",
-       " bert2SentSim                             0.469497\n",
-       " classSim                                 0.878703\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 52, dtype: object,\n",
-       " Unnamed: 0                                      53\n",
-       " id              Q56756850-P279-Q1001329-6cb334b1-0\n",
-       " node1                                    Q56756850\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.412103\n",
-       " classSim                                  0.363595\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 53, dtype: object,\n",
-       " Unnamed: 0                                      54\n",
-       " id              Q60782200-P279-Q1001329-0b4da903-0\n",
-       " node1                                    Q60782200\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.239958\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 54, dtype: object,\n",
-       " Unnamed: 0                                      55\n",
-       " id              Q78841106-P279-Q1001329-d474c822-0\n",
-       " node1                                    Q78841106\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.496099\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 55, dtype: object,\n",
-       " Unnamed: 0                                    56\n",
-       " id              Q798863-P279-Q1001329-c284c2ec-0\n",
-       " node1                                    Q798863\n",
-       " label                                       P279\n",
-       " node2                                   Q1001329\n",
-       " bert2SentSim                            0.597623\n",
-       " classSim                                0.871646\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 56, dtype: object,\n",
-       " Unnamed: 0                                      57\n",
-       " id              Q96337569-P279-Q1001329-b8b30fb9-0\n",
-       " node1                                    Q96337569\n",
-       " label                                         P279\n",
-       " node2                                     Q1001329\n",
-       " bert2SentSim                              0.210435\n",
-       " classSim                                  0.871646\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 57, dtype: object,\n",
-       " Unnamed: 0                                       58\n",
-       " id              Q3403683-P279-Q100135295-fd0a0a92-0\n",
-       " node1                                      Q3403683\n",
-       " label                                          P279\n",
-       " node2                                    Q100135295\n",
-       " bert2SentSim                               0.371629\n",
-       " classSim                                   0.811291\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 58, dtype: object,\n",
-       " Unnamed: 0                                         59\n",
-       " id              Q100095237-P279-Q100136951-a7f6f753-0\n",
-       " node1                                      Q100095237\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.909817\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 59, dtype: object,\n",
-       " Unnamed: 0                                         60\n",
-       " id              Q100136066-P279-Q100136951-1e2368cd-0\n",
-       " node1                                      Q100136066\n",
-       " label                                            P279\n",
-       " node2                                      Q100136951\n",
-       " bert2SentSim                                 0.883361\n",
-       " classSim                                     0.843155\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 60, dtype: object,\n",
-       " Unnamed: 0                                      61\n",
-       " id              Q235041-P279-Q100136951-07d30679-0\n",
-       " node1                                      Q235041\n",
-       " label                                         P279\n",
-       " node2                                   Q100136951\n",
-       " bert2SentSim                              0.607752\n",
-       " classSim                                  0.719618\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 61, dtype: object,\n",
-       " Unnamed: 0                                      62\n",
-       " id              Q22006653-P279-Q1001378-98eddb62-0\n",
-       " node1                                    Q22006653\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.366579\n",
-       " classSim                                    0.5263\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 62, dtype: object,\n",
-       " Unnamed: 0                                     63\n",
-       " id              Q2895476-P279-Q1001378-9d15228e-0\n",
-       " node1                                    Q2895476\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.682378\n",
-       " classSim                                  0.75066\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 63, dtype: object,\n",
-       " Unnamed: 0                                     64\n",
-       " id              Q2918907-P279-Q1001378-39c09697-0\n",
-       " node1                                    Q2918907\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.813292\n",
-       " classSim                                 0.775328\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 64, dtype: object,\n",
-       " Unnamed: 0                                     65\n",
-       " id              Q3516833-P279-Q1001378-30384a7c-0\n",
-       " node1                                    Q3516833\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.684669\n",
-       " classSim                                 0.916831\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 65, dtype: object,\n",
-       " Unnamed: 0                                     66\n",
-       " id              Q4129855-P279-Q1001378-3ad464f7-0\n",
-       " node1                                    Q4129855\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.740775\n",
-       " classSim                                 0.905545\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 66, dtype: object,\n",
-       " Unnamed: 0                                     67\n",
-       " id              Q5197004-P279-Q1001378-513f9939-0\n",
-       " node1                                    Q5197004\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                             0.642596\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 67, dtype: object,\n",
-       " Unnamed: 0                                     68\n",
-       " id              Q7237336-P279-Q1001378-55e73e1a-0\n",
-       " node1                                    Q7237336\n",
-       " label                                        P279\n",
-       " node2                                    Q1001378\n",
-       " bert2SentSim                              0.58928\n",
-       " classSim                                 0.892815\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 68, dtype: object,\n",
-       " Unnamed: 0                                    69\n",
-       " id              Q738160-P279-Q1001378-b69af0cb-0\n",
-       " node1                                    Q738160\n",
-       " label                                       P279\n",
-       " node2                                   Q1001378\n",
-       " bert2SentSim                            0.813292\n",
-       " classSim                                0.892815\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 69, dtype: object,\n",
-       " Unnamed: 0                                  70\n",
-       " id              Q8317-P279-Q1001378-a56c669f-0\n",
-       " node1                                    Q8317\n",
-       " label                                     P279\n",
-       " node2                                 Q1001378\n",
-       " bert2SentSim                          0.636886\n",
-       " classSim                              0.541961\n",
-       " Resp_code                     <Response [200]>\n",
-       " Name: 70, dtype: object,\n",
-       " Unnamed: 0                                      71\n",
-       " id              Q86908314-P279-Q1001378-7a81e91b-0\n",
-       " node1                                    Q86908314\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.421366\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 71, dtype: object,\n",
-       " Unnamed: 0                                      72\n",
-       " id              Q86910224-P279-Q1001378-b427331c-0\n",
-       " node1                                    Q86910224\n",
-       " label                                         P279\n",
-       " node2                                     Q1001378\n",
-       " bert2SentSim                              0.584772\n",
-       " classSim                                  0.892815\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 72, dtype: object,\n",
-       " Unnamed: 0                                         73\n",
-       " id              Q100148346-P279-Q100148352-003654ac-0\n",
-       " node1                                      Q100148346\n",
-       " label                                            P279\n",
-       " node2                                      Q100148352\n",
-       " bert2SentSim                                 0.872339\n",
-       " classSim                                     0.951283\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 73, dtype: object,\n",
-       " Unnamed: 0                                        74\n",
-       " id              Q27147769-P279-Q100148352-8254b7c4-0\n",
-       " node1                                      Q27147769\n",
-       " label                                           P279\n",
-       " node2                                     Q100148352\n",
-       " bert2SentSim                                0.886007\n",
-       " classSim                                    0.924902\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 74, dtype: object,\n",
-       " Unnamed: 0                                     75\n",
-       " id              Q14747611-P279-Q100159-567e45a7-0\n",
-       " node1                                   Q14747611\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                              0.50114\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 75, dtype: object,\n",
-       " Unnamed: 0                                     76\n",
-       " id              Q17089549-P279-Q100159-3247e0d4-0\n",
-       " node1                                   Q17089549\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.705335\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 76, dtype: object,\n",
-       " Unnamed: 0                                     77\n",
-       " id              Q18923049-P279-Q100159-04db261f-0\n",
-       " node1                                   Q18923049\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                             0.861815\n",
-       " classSim                                 0.719858\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 77, dtype: object,\n",
-       " Unnamed: 0                                    78\n",
-       " id              Q2056084-P279-Q100159-94d1b7b8-0\n",
-       " node1                                   Q2056084\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.782052\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 78, dtype: object,\n",
-       " Unnamed: 0                                    79\n",
-       " id              Q5097845-P279-Q100159-c2bd0b24-0\n",
-       " node1                                   Q5097845\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.551256\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 79, dtype: object,\n",
-       " Unnamed: 0                                    80\n",
-       " id              Q6061403-P279-Q100159-d5101564-0\n",
-       " node1                                   Q6061403\n",
-       " label                                       P279\n",
-       " node2                                    Q100159\n",
-       " bert2SentSim                            0.565718\n",
-       " classSim                                0.866312\n",
-       " Resp_code                       <Response [200]>\n",
-       " Name: 80, dtype: object,\n",
-       " Unnamed: 0                                   81\n",
-       " id              Q652653-P279-Q100159-bd6c5d3a-0\n",
-       " node1                                   Q652653\n",
-       " label                                      P279\n",
-       " node2                                   Q100159\n",
-       " bert2SentSim                           0.501037\n",
-       " classSim                               0.837687\n",
-       " Resp_code                      <Response [200]>\n",
-       " Name: 81, dtype: object,\n",
-       " Unnamed: 0                                     82\n",
-       " id              Q66293226-P279-Q100159-ad4b8016-0\n",
-       " node1                                   Q66293226\n",
-       " label                                        P279\n",
-       " node2                                     Q100159\n",
-       " bert2SentSim                               0.8089\n",
-       " classSim                                 0.866312\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 82, dtype: object,\n",
-       " Unnamed: 0                                         83\n",
-       " id              Q100166380-P279-Q100166391-4684ea65-0\n",
-       " node1                                      Q100166380\n",
-       " label                                            P279\n",
-       " node2                                      Q100166391\n",
-       " bert2SentSim                                 0.752609\n",
-       " classSim                                     0.799499\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 83, dtype: object,\n",
-       " Unnamed: 0                                      84\n",
-       " id              Q244326-P279-Q100166391-35359099-0\n",
-       " node1                                      Q244326\n",
-       " label                                         P279\n",
-       " node2                                   Q100166391\n",
-       " bert2SentSim                              0.522545\n",
-       " classSim                                  0.470975\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 84, dtype: object,\n",
-       " Unnamed: 0                                     85\n",
-       " id              Q40551-P279-Q100166391-7242baf9-0\n",
-       " node1                                      Q40551\n",
-       " label                                        P279\n",
-       " node2                                  Q100166391\n",
-       " bert2SentSim                              0.74986\n",
-       " classSim                                 0.781795\n",
-       " Resp_code                        <Response [200]>\n",
-       " Name: 85, dtype: object,\n",
-       " Unnamed: 0                                         86\n",
-       " id              Q102108504-P279-Q100171002-7ccf3934-0\n",
-       " node1                                      Q102108504\n",
-       " label                                            P279\n",
-       " node2                                      Q100171002\n",
-       " bert2SentSim                                 0.680867\n",
-       " classSim                                         None\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 86, dtype: object,\n",
-       " Unnamed: 0                                       87\n",
-       " id              Q1150647-P279-Q100171002-fb1cad26-0\n",
-       " node1                                      Q1150647\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.664551\n",
-       " classSim                                   0.795416\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 87, dtype: object,\n",
-       " Unnamed: 0                                       88\n",
-       " id              Q1503443-P279-Q100171002-8282ae74-0\n",
-       " node1                                      Q1503443\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.748802\n",
-       " classSim                                   0.822611\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 88, dtype: object,\n",
-       " Unnamed: 0                                       89\n",
-       " id              Q2616305-P279-Q100171002-49dc8346-0\n",
-       " node1                                      Q2616305\n",
-       " label                                          P279\n",
-       " node2                                    Q100171002\n",
-       " bert2SentSim                               0.646817\n",
-       " classSim                                   0.887266\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 89, dtype: object,\n",
-       " Unnamed: 0                                        90\n",
-       " id              Q24050179-P279-Q100195948-90966900-0\n",
-       " node1                                      Q24050179\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.652214\n",
-       " classSim                                    0.698738\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 90, dtype: object,\n",
-       " Unnamed: 0                                        91\n",
-       " id              Q29479539-P279-Q100195948-e222789a-0\n",
-       " node1                                      Q29479539\n",
-       " label                                           P279\n",
-       " node2                                     Q100195948\n",
-       " bert2SentSim                                0.689148\n",
-       " classSim                                    0.681145\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 91, dtype: object,\n",
-       " Unnamed: 0                                      92\n",
-       " id              Q308859-P279-Q100195948-a5841742-0\n",
-       " node1                                      Q308859\n",
-       " label                                         P279\n",
-       " node2                                   Q100195948\n",
-       " bert2SentSim                              0.750328\n",
-       " classSim                                  0.279686\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 92, dtype: object,\n",
-       " Unnamed: 0                                         93\n",
-       " id              Q100251305-P279-Q100251275-02ddd08e-0\n",
-       " node1                                      Q100251305\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.486286\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 93, dtype: object,\n",
-       " Unnamed: 0                                         94\n",
-       " id              Q100251705-P279-Q100251275-fa0c89c7-0\n",
-       " node1                                      Q100251705\n",
-       " label                                            P279\n",
-       " node2                                      Q100251275\n",
-       " bert2SentSim                                 0.370606\n",
-       " classSim                                      0.94603\n",
-       " Resp_code                            <Response [200]>\n",
-       " Name: 94, dtype: object,\n",
-       " Unnamed: 0                                        95\n",
-       " id              Q16007968-P279-Q100251275-318d5598-0\n",
-       " node1                                      Q16007968\n",
-       " label                                           P279\n",
-       " node2                                     Q100251275\n",
-       " bert2SentSim                                0.478886\n",
-       " classSim                                      0.9303\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 95, dtype: object,\n",
-       " Unnamed: 0                                       96\n",
-       " id              Q3310017-P279-Q100251275-772909ea-0\n",
-       " node1                                      Q3310017\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                                0.57202\n",
-       " classSim                                   0.929488\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 96, dtype: object,\n",
-       " Unnamed: 0                                       97\n",
-       " id              Q4855428-P279-Q100251275-fbf2100a-0\n",
-       " node1                                      Q4855428\n",
-       " label                                          P279\n",
-       " node2                                    Q100251275\n",
-       " bert2SentSim                               0.545835\n",
-       " classSim                                   0.931676\n",
-       " Resp_code                          <Response [200]>\n",
-       " Name: 97, dtype: object,\n",
-       " Unnamed: 0                                      98\n",
-       " id              Q862086-P279-Q100251275-faf57342-0\n",
-       " node1                                      Q862086\n",
-       " label                                         P279\n",
-       " node2                                   Q100251275\n",
-       " bert2SentSim                              0.630551\n",
-       " classSim                                  0.948207\n",
-       " Resp_code                         <Response [200]>\n",
-       " Name: 98, dtype: object,\n",
-       " Unnamed: 0                                        99\n",
-       " id              Q15209696-P279-Q100257358-c4b60f40-0\n",
-       " node1                                      Q15209696\n",
-       " label                                           P279\n",
-       " node2                                     Q100257358\n",
-       " bert2SentSim                                0.484455\n",
-       " classSim                                    0.610338\n",
-       " Resp_code                           <Response [200]>\n",
-       " Name: 99, dtype: object]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "worth-croatia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF_transP279_filtered2_class = pd.DataFrame(hierDF_transP279_filtered2_class_arr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "changing-burning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "portuguese-practitioner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "302108"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF_transP279_filtered2_class.classSim.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "breathing-bicycle",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "historic-maria",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279_3M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "P279_10M_data = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spanish-killer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_3M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_3M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_3M_data.iterrows(), total=len(P279_3M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collectible-modern",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm.notebook import tqdm\n",
-    "import json\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "P279_10M_data_class_arr = []\n",
-    "\n",
-    "def fetchClassSim(row):\n",
-    "    try:\n",
-    "        resp = requests.get(\"https://kgtk.isi.edu/similarity_api?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "        row['classSim'] = float(resp) if resp else -1\n",
-    "    except:\n",
-    "        print(f\"Resp not found for {row['node1']}, {row['node2']}\")\n",
-    "        row['classSim'] = None\n",
-    "    return row\n",
-    "\n",
-    "P279_10M_data_class_arr = Parallel(n_jobs=20)(delayed(fetchClassSim)(row) for _, row in tqdm(P279_10M_data.iterrows(), total=len(P279_10M_data)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "removable-watershed",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fossil-psychiatry",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "rapid-drink",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "liquid-symphony",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intellectual-rainbow",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integrated-intention",
-   "metadata": {},
-   "source": [
-    "# Wiki-CS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "functioning-scholarship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unique-weekend",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs-20200504.tsv',sep='\\t')\n",
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])\n",
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')\n",
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "located-swaziland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sharing-scientist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]\n",
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fewer-place",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})\n",
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label']].to_csv('../data/wikidata-cs_categorized.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "better-planet",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "enhanced-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs_categorized.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "complex-character",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "exciting-anderson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I_set_size = len(wikiDF[wikiDF.category == 'I'])\n",
-    "M_sampled_set = wikiDF[wikiDF.category == 'M'].sample(I_set_size, random_state=13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "thrown-graham",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import combinations\n",
-    "\n",
-    "def find_relation_mapping(mainset, subset, needed_size):\n",
-    "    nodes = set([row['word1_kg_id'] for _,row in subset.iterrows()] \n",
-    "            + [row['word2_kg_id'] for _,row in subset.iterrows()])\n",
-    "    node_label_mappings = {row['word1_kg_id']: row['node1;label'] for _, row in subset.iterrows()}.update(\n",
-    "                                {row['word2_kg_id']: row['node2;label'] for _, row in subset.iterrows()}\n",
-    "                        )\n",
-    "    mainset_pairs = set([(row['word1_kg_id'], row['word2_kg_id']) for _,row in mainset.iterrows()])\n",
-    "\n",
-    "    all_pairs = set([pair for pair in list(combinations(list(nodes), 2))[10*needed_size]])\n",
-    "    \n",
-    "    removed_pairs = set()\n",
-    "    \n",
-    "    first_line = True\n",
-    "    with gzip.open('../data/wikidata-20210215-dwd/claims.tsv.gz', 'r') as claims_file:\n",
-    "        for line in tqdm(claims_file,total=98482585):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                continue\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            curr_pair = (line[1], line[3])\n",
-    "            if curr_pair in all_pairs:\n",
-    "                removed_pairs.add(curr_pair)\n",
-    "    newDF = []\n",
-    "    for pair in all_pairs - removed_pairs - mainset_pairs:\n",
-    "        try:\n",
-    "            if pair[0] is None or pair[1] is None:\n",
-    "                continue\n",
-    "            newDF.append(pair[0], pair[1], 'U', node_label_mappings[pair[0]], node_label_mappings[pair[1]])\n",
-    "        except:\n",
-    "            print(pair)\n",
-    "    return pd.DataFrame(newDF, columns=subset.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "immune-jesus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3df524d9d4fd47e6864fe57299e1f8e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/98482585 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q1070669\n",
-      "Q20755059\n",
-      "CPU times: user 13min 20s, sys: 1min 24s, total: 14min 44s\n",
-      "Wall time: 14min 40s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "U_sampled_set = find_relation_mapping(wikiDF, wikiDF[wikiDF.category == 'M'], I_set_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "transparent-weather",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(U_sampled_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "graduate-guarantee",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "amended-support",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-knowing",
-   "metadata": {},
-   "source": [
-    "# Check Diff in versions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "under-simon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "275927\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "def get_all_nodes():\n",
-    "    p279ChildPar = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_baremetal.csv')\n",
-    "    wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "    concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "    p279QnodesList = set(p279ChildPar.node1.to_list() \n",
-    "                        + p279ChildPar.node2.to_list()\n",
-    "                        + wordsim_df['word1_kg_id'].to_list() \n",
-    "                        + wordsim_df['word2_kg_id'].to_list()\n",
-    "                        + wiki_cs_df['word1_kg_id'].to_list() \n",
-    "                        + wiki_cs_df['word2_kg_id'].to_list()\n",
-    "                        + concept_net_df['word1_kg_id'].to_list()\n",
-    "                        + concept_net_df['word2_kg_id'].to_list())\n",
-    "    print(len(p279QnodesList))\n",
-    "    return p279QnodesList\n",
-    "allNodes = get_all_nodes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "excess-killer",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "files_set_1 = ['../data/wikidata-20210215-dwd/descriptions.en.tsv.gz',\n",
-    "              '../data/wikidata-20210215-dwd/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "files_set_2 = [DESCRIPTIONS_FILE,\n",
-    "              '../../wd-correctness/gdrive-kgtk-dump-2020-12-07/sitelinks.en.tsv.gz']\n",
-    "\n",
-    "\n",
-    "all_nodes_set = set(allNodes)\n",
-    "for file1, file2 in zip(files_set_1, files_set_2):\n",
-    "    file1_dict, file2_dict = {}, {}\n",
-    "    with gzip.open(file1) as file1:\n",
-    "        for line in tqdm(file1):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file1_dict[line[1]] = line[3][1:-4]\n",
-    "    with gzip.open(file2) as file2:\n",
-    "        for line in tqdm(file2):\n",
-    "            line = line.decode(\"utf-8\").strip().split('\\t')\n",
-    "            if line[1] in all_nodes_set:\n",
-    "                file2_dict[line[1]] = line[3][1:-4]\n",
-    "    for elem in file1_dict.keys():\n",
-    "        try:\n",
-    "            if file1_dict[elem] != file2_dict[elem]:\n",
-    "                print(elem, file1_dict[elem], file2_dict[elem])\n",
-    "        except:\n",
-    "            continue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "integrated-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "romantic-rebecca",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "needed-convergence",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "communist-thumbnail",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "278px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/P279 50 Samples Sim Annotated File Analysis.ipynb b/P279 50 Samples Sim Annotated File Analysis.ipynb
deleted file mode 100644
index ca00c9f..0000000
--- a/P279 50 Samples Sim Annotated File Analysis.ipynb	
+++ /dev/null
@@ -1,3566 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "portuguese-pollution",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "undefined-genesis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv('../data/P279_dataset/P279_Sim_Dataset_all_annotated.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "quiet-antibody",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF['node1_label'] = p279DF.node1_label.apply(lambda p: p.replace('\\\\',''))\n",
-    "p279DF['node2_label'] = p279DF.node2_label.apply(lambda p: p.replace('\\\\',''))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "respective-tanzania",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation_type</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>S_Sim</th>\n",
-       "      <th>S_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>J_Dim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q48842375</td>\n",
-       "      <td>Asian Women's Softball Championship</td>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q8001434</td>\n",
-       "      <td>Wildlife of South Africa</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>fauna</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.285714</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q40189366</td>\n",
-       "      <td>interstellar radiation field</td>\n",
-       "      <td>Q11379</td>\n",
-       "      <td>energy</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.714286</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q2162453</td>\n",
-       "      <td>traditional political party</td>\n",
-       "      <td>Q7278</td>\n",
-       "      <td>political party</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.714286</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q6786579</td>\n",
-       "      <td>materials physics</td>\n",
-       "      <td>Q413</td>\n",
-       "      <td>physics</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID      node1                          node1_label     node2  \\\n",
-       "0   1  Q48842375  Asian Women's Softball Championship  Q1079023   \n",
-       "1   2   Q8001434             Wildlife of South Africa   Q104021   \n",
-       "2   3  Q40189366         interstellar radiation field    Q11379   \n",
-       "3   4   Q2162453          traditional political party     Q7278   \n",
-       "4   5   Q6786579                    materials physics      Q413   \n",
-       "\n",
-       "       node2_label relation_type  P_Sim P_Dim  H_Sim H_Dim  ...  S_Sim S_Dim  \\\n",
-       "0     championship  parent/child      2     i      2     I  ...      2     i   \n",
-       "1            fauna  parent/child      2     i      2     I  ...      2     i   \n",
-       "2           energy  parent/child      3     i      3     I  ...      2     i   \n",
-       "3  political party  parent/child      1   NaN      2     I  ...      2     i   \n",
-       "4          physics  parent/child      2     i      2     I  ...      2     i   \n",
-       "\n",
-       "   N_Sim N_Dim  F_Sim F_Dim  J_Sim J_Dim   Average    StdDev  \n",
-       "0      2     I      2     I      3     I  2.142857  0.377964  \n",
-       "1      3     H      2     I      3     I  2.285714  0.487950  \n",
-       "2      3     H      3     D      3     I  2.714286  0.487950  \n",
-       "3      2     I      2     I      2     I  1.714286  0.487950  \n",
-       "4      3     H      2     I      2     H  2.142857  0.377964  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279DF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "illegal-afternoon",
-   "metadata": {},
-   "source": [
-    "# Split to ChildPar, Siblings Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "simplified-journalist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_ChildParDF = p279DF[p279DF.relation_type == 'parent/child']\n",
-    "p279_SiblingsDF = p279DF[p279DF.relation_type == 'sibling']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "other-lingerie",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation_type</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>S_Sim</th>\n",
-       "      <th>S_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>J_Dim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>26</td>\n",
-       "      <td>Q4165229</td>\n",
-       "      <td>gain</td>\n",
-       "      <td>Q570938</td>\n",
-       "      <td>net income</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>27</td>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>president</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>disaster</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>29</td>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>software</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.571429</td>\n",
-       "      <td>0.786796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>30</td>\n",
-       "      <td>Q1400881</td>\n",
-       "      <td>property</td>\n",
-       "      <td>Q2574811</td>\n",
-       "      <td>cause</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.714286</td>\n",
-       "      <td>0.755929</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    ID     node1 node1_label     node2    node2_label relation_type  P_Sim  \\\n",
-       "25  26  Q4165229        gain   Q570938     net income       sibling      2   \n",
-       "26  27    Q30461   president    Q48352  head of state       sibling      1   \n",
-       "27  28   Q381072      crisis  Q3839081       disaster       sibling      2   \n",
-       "28  29    Q39546        tool     Q7397       software       sibling      3   \n",
-       "29  30  Q1400881    property  Q2574811          cause       sibling      4   \n",
-       "\n",
-       "   P_Dim  H_Sim H_Dim  ...  S_Sim S_Dim  N_Sim N_Dim  F_Sim F_Dim  J_Sim  \\\n",
-       "25     i      2     I  ...      2     i      3     D      2     S      2   \n",
-       "26   NaN      2     I  ...      2     i      2     I      1   NaN      2   \n",
-       "27     i      2     I  ...      2     i      1   NaN      1   NaN      2   \n",
-       "28     i      2     I  ...      2     i      3     I      2     I      4   \n",
-       "29   NaN      4   NaN  ...      4   NaN      4   NaN      2     S      4   \n",
-       "\n",
-       "   J_Dim   Average    StdDev  \n",
-       "25     I  2.142857  0.377964  \n",
-       "26     I  1.571429  0.534522  \n",
-       "27     I  1.571429  0.534522  \n",
-       "28   NaN  2.571429  0.786796  \n",
-       "29   NaN  3.714286  0.755929  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_SiblingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unauthorized-knife",
-   "metadata": {},
-   "source": [
-    "# Fetch parents for siblings dataset and create triplets for sentence construction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "competent-flush",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279SibWordSet = set(p279_SiblingsDF.node1.to_list() + p279_SiblingsDF.node2.to_list())\n",
-    "p279SibWordPairSet = list(zip(p279_SiblingsDF.node1.to_list(),p279_SiblingsDF.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "liked-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_Parents_DF = pd.read_csv('../data/P279_dataset/wikidata-P279.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "conventional-binary",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100000030-P279-Q14748-30394205-0</td>\n",
-       "      <td>Q100000030</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q14748</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100000058-P279-Q1622444-bd182663-0</td>\n",
-       "      <td>Q100000058</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1622444</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1000032-P279-Q1813494-0aa0f1dc-0</td>\n",
-       "      <td>Q1000032</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1813494</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1000032-P279-Q83602-482a1943-0</td>\n",
-       "      <td>Q1000032</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q83602</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1000039-P279-Q11555767-2dddfd86-0</td>\n",
-       "      <td>Q1000039</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11555767</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    id       node1 label      node2    rank  \\\n",
-       "0    Q100000030-P279-Q14748-30394205-0  Q100000030  P279     Q14748  normal   \n",
-       "1  Q100000058-P279-Q1622444-bd182663-0  Q100000058  P279   Q1622444  normal   \n",
-       "2    Q1000032-P279-Q1813494-0aa0f1dc-0    Q1000032  P279   Q1813494  normal   \n",
-       "3      Q1000032-P279-Q83602-482a1943-0    Q1000032  P279     Q83602  normal   \n",
-       "4   Q1000039-P279-Q11555767-2dddfd86-0    Q1000039  P279  Q11555767  normal   \n",
-       "\n",
-       "  node2;wikidatatype  \n",
-       "0      wikibase-item  \n",
-       "1      wikibase-item  \n",
-       "2      wikibase-item  \n",
-       "3      wikibase-item  \n",
-       "4      wikibase-item  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_Parents_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "pursuant-lecture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_Parents_DF1 = p279_Parents_DF[p279_Parents_DF.apply(lambda p: p.node1 in p279SibWordSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "smaller-youth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "noted-drawing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = p279_Parents_DF1.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "female-tower",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = temp1[['id','label','node2','node2_label']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "engaged-constant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "appropriate-andrew",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "appreciated-setting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "ruled-screening",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp3[~temp3['node1_label'].isna()]\n",
-    "temp3 = temp3[~temp3['node2_label'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "transsexual-queensland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3['node2_label'].isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "offensive-replica",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "suspended-proof",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "completed-immune",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q523-P279-Q1027098-3ce5cd2f-0</td>\n",
-       "      <td>Q523</td>\n",
-       "      <td>star</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1027098</td>\n",
-       "      <td>fusor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q381072-P279-Q104493-9eab93d0-0</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q104493</td>\n",
-       "      <td>risk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q176763-P279-Q11028-28192af9-0</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>information</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q3695082-P279-Q11028-fb23389c-0</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>information</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q551800-P279-Q11105360-c1431a2e-0</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11105360</td>\n",
-       "      <td>medium of exchange</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>Q951964-P279-Q8195619-98e34f68-0</td>\n",
-       "      <td>Q951964</td>\n",
-       "      <td>food product</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q8195619</td>\n",
-       "      <td>human food</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q30461-P279-Q82955-2d27e026-0</td>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>president</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>politician</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q951964-P279-Q868404-10500678-0</td>\n",
-       "      <td>Q951964</td>\n",
-       "      <td>food product</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q868404</td>\n",
-       "      <td>final good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>Q2006324-P279-Q930933-06286d46-0</td>\n",
-       "      <td>Q2006324</td>\n",
-       "      <td>agreement</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q930933</td>\n",
-       "      <td>relation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>Q551800-P279-Q986008-b5d5fa1c-0</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q986008</td>\n",
-       "      <td>payment system</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    id     node1   node1_label label  \\\n",
-       "0        Q523-P279-Q1027098-3ce5cd2f-0      Q523          star  P279   \n",
-       "1      Q381072-P279-Q104493-9eab93d0-0   Q381072        crisis  P279   \n",
-       "2       Q176763-P279-Q11028-28192af9-0   Q176763      evidence  P279   \n",
-       "3      Q3695082-P279-Q11028-fb23389c-0  Q3695082          sign  P279   \n",
-       "4    Q551800-P279-Q11105360-c1431a2e-0   Q551800        ticket  P279   \n",
-       "..                                 ...       ...           ...   ...   \n",
-       "96    Q951964-P279-Q8195619-98e34f68-0   Q951964  food product  P279   \n",
-       "97       Q30461-P279-Q82955-2d27e026-0    Q30461     president  P279   \n",
-       "98     Q951964-P279-Q868404-10500678-0   Q951964  food product  P279   \n",
-       "99    Q2006324-P279-Q930933-06286d46-0  Q2006324     agreement  P279   \n",
-       "100    Q551800-P279-Q986008-b5d5fa1c-0   Q551800        ticket  P279   \n",
-       "\n",
-       "         node2         node2_label  \n",
-       "0     Q1027098               fusor  \n",
-       "1      Q104493                risk  \n",
-       "2       Q11028         information  \n",
-       "3       Q11028         information  \n",
-       "4    Q11105360  medium of exchange  \n",
-       "..         ...                 ...  \n",
-       "96    Q8195619          human food  \n",
-       "97      Q82955          politician  \n",
-       "98     Q868404          final good  \n",
-       "99     Q930933            relation  \n",
-       "100    Q986008      payment system  \n",
-       "\n",
-       "[100 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "spectacular-blackjack",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairLeftDF = temp3.copy()\n",
-    "pairRightDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "metric-persian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF = pairLeftDF.set_index('node2').join(pairRightDF.set_index('node2'),rsuffix='_right').reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "pleasant-apparel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF['set_nodes'] = pairsDF.apply(lambda p: str(sorted([p.node1, p.node1_right])), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "national-pleasure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF1 = pairsDF[~pairsDF['set_nodes'].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "roman-voluntary",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "196 statements get reduced to 84 statements after checking for duplicate pairs\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{len(pairsDF)} statements get reduced to {len(pairsDF1)} statements after checking for duplicate pairs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "thick-special",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q1027098</td>\n",
-       "      <td>Q523-P279-Q1027098-3ce5cd2f-0</td>\n",
-       "      <td>Q523</td>\n",
-       "      <td>star</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fusor</td>\n",
-       "      <td>Q523-P279-Q1027098-3ce5cd2f-0</td>\n",
-       "      <td>Q523</td>\n",
-       "      <td>star</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fusor</td>\n",
-       "      <td>['Q523', 'Q523']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q104493</td>\n",
-       "      <td>Q381072-P279-Q104493-9eab93d0-0</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>risk</td>\n",
-       "      <td>Q381072-P279-Q104493-9eab93d0-0</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>risk</td>\n",
-       "      <td>['Q381072', 'Q381072']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q176763-P279-Q11028-28192af9-0</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>Q176763-P279-Q11028-28192af9-0</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>['Q176763', 'Q176763']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q176763-P279-Q11028-28192af9-0</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>Q3695082-P279-Q11028-fb23389c-0</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>['Q176763', 'Q3695082']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082-P279-Q11028-fb23389c-0</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>Q3695082-P279-Q11028-fb23389c-0</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>information</td>\n",
-       "      <td>['Q3695082', 'Q3695082']</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node2                               id     node1 node1_label label  \\\n",
-       "0  Q1027098    Q523-P279-Q1027098-3ce5cd2f-0      Q523        star  P279   \n",
-       "1   Q104493  Q381072-P279-Q104493-9eab93d0-0   Q381072      crisis  P279   \n",
-       "2    Q11028   Q176763-P279-Q11028-28192af9-0   Q176763    evidence  P279   \n",
-       "3    Q11028   Q176763-P279-Q11028-28192af9-0   Q176763    evidence  P279   \n",
-       "5    Q11028  Q3695082-P279-Q11028-fb23389c-0  Q3695082        sign  P279   \n",
-       "\n",
-       "   node2_label                         id_right node1_right node1_label_right  \\\n",
-       "0        fusor    Q523-P279-Q1027098-3ce5cd2f-0        Q523              star   \n",
-       "1         risk  Q381072-P279-Q104493-9eab93d0-0     Q381072            crisis   \n",
-       "2  information   Q176763-P279-Q11028-28192af9-0     Q176763          evidence   \n",
-       "3  information  Q3695082-P279-Q11028-fb23389c-0    Q3695082              sign   \n",
-       "5  information  Q3695082-P279-Q11028-fb23389c-0    Q3695082              sign   \n",
-       "\n",
-       "  label_right node2_label_right                 set_nodes  \n",
-       "0        P279             fusor          ['Q523', 'Q523']  \n",
-       "1        P279              risk    ['Q381072', 'Q381072']  \n",
-       "2        P279       information    ['Q176763', 'Q176763']  \n",
-       "3        P279       information   ['Q176763', 'Q3695082']  \n",
-       "5        P279       information  ['Q3695082', 'Q3695082']  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "designed-video",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF2 = pairsDF1[['node1', 'node2', 'node1_right', 'node1_label', 'node2_label', 'node1_label_right']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "scenic-reaction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3 = pairsDF2.rename(columns={'node1_right': 'node2', 'node2': 'parent', 'node1_label_right': 'node2_label', 'node2_label': 'par_label'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "substantial-frederick",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q523</td>\n",
-       "      <td>Q1027098</td>\n",
-       "      <td>Q523</td>\n",
-       "      <td>star</td>\n",
-       "      <td>fusor</td>\n",
-       "      <td>star</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>Q104493</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>risk</td>\n",
-       "      <td>crisis</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>information</td>\n",
-       "      <td>evidence</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>information</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>information</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1    parent     node2 node1_label    par_label node2_label\n",
-       "0      Q523  Q1027098      Q523        star        fusor        star\n",
-       "1   Q381072   Q104493   Q381072      crisis         risk      crisis\n",
-       "2   Q176763    Q11028   Q176763    evidence  information    evidence\n",
-       "3   Q176763    Q11028  Q3695082    evidence  information        sign\n",
-       "5  Q3695082    Q11028  Q3695082        sign  information        sign"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "female-native",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q523</td>\n",
-       "      <td>Q1027098</td>\n",
-       "      <td>Q523</td>\n",
-       "      <td>star</td>\n",
-       "      <td>fusor</td>\n",
-       "      <td>star</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>Q104493</td>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>risk</td>\n",
-       "      <td>crisis</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>information</td>\n",
-       "      <td>evidence</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>information</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>information</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>Q8928</td>\n",
-       "      <td>Q6999</td>\n",
-       "      <td>Q8928</td>\n",
-       "      <td>constellation</td>\n",
-       "      <td>astronomical object</td>\n",
-       "      <td>constellation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q7184903</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>information</td>\n",
-       "      <td>abstract object</td>\n",
-       "      <td>information</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q7184903</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>information</td>\n",
-       "      <td>abstract object</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q7184903</td>\n",
-       "      <td>Q450</td>\n",
-       "      <td>information</td>\n",
-       "      <td>abstract object</td>\n",
-       "      <td>mind</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>Q7184903</td>\n",
-       "      <td>Q450</td>\n",
-       "      <td>sign</td>\n",
-       "      <td>abstract object</td>\n",
-       "      <td>mind</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>84 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1    parent     node2    node1_label            par_label  \\\n",
-       "0        Q523  Q1027098      Q523           star                fusor   \n",
-       "1     Q381072   Q104493   Q381072         crisis                 risk   \n",
-       "2     Q176763    Q11028   Q176763       evidence          information   \n",
-       "3     Q176763    Q11028  Q3695082       evidence          information   \n",
-       "5    Q3695082    Q11028  Q3695082           sign          information   \n",
-       "..        ...       ...       ...            ...                  ...   \n",
-       "176     Q8928     Q6999     Q8928  constellation  astronomical object   \n",
-       "177    Q11028  Q7184903    Q11028    information      abstract object   \n",
-       "178    Q11028  Q7184903  Q3695082    information      abstract object   \n",
-       "179    Q11028  Q7184903      Q450    information      abstract object   \n",
-       "182  Q3695082  Q7184903      Q450           sign      abstract object   \n",
-       "\n",
-       "       node2_label  \n",
-       "0             star  \n",
-       "1           crisis  \n",
-       "2         evidence  \n",
-       "3             sign  \n",
-       "5             sign  \n",
-       "..             ...  \n",
-       "176  constellation  \n",
-       "177    information  \n",
-       "178           sign  \n",
-       "179           mind  \n",
-       "182           mind  \n",
-       "\n",
-       "[84 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "modified-effects",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF4 = pairsDF3[pairsDF3.apply(lambda p: (p.node1, p.node2) in p279SibWordPairSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "tribal-consumer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(pairsDF4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "integrated-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q176763</td>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q3695082</td>\n",
-       "      <td>evidence</td>\n",
-       "      <td>information</td>\n",
-       "      <td>sign</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q1308677</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>Q21171241</td>\n",
-       "      <td>deployment</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>departure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q1308677</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>deployment</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>disaster</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>Q198</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>war</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>disaster</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>disaster</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>Q186360</td>\n",
-       "      <td>Q11974939</td>\n",
-       "      <td>Q39631</td>\n",
-       "      <td>nurse</td>\n",
-       "      <td>health professional</td>\n",
-       "      <td>physician</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Q4165229</td>\n",
-       "      <td>Q1527264</td>\n",
-       "      <td>Q570938</td>\n",
-       "      <td>gain</td>\n",
-       "      <td>income</td>\n",
-       "      <td>net income</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>Q192935</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q3314483</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>food</td>\n",
-       "      <td>fruit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>Q3314483</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q951964</td>\n",
-       "      <td>fruit</td>\n",
-       "      <td>food</td>\n",
-       "      <td>food product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Q191067</td>\n",
-       "      <td>Q234460</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>article</td>\n",
-       "      <td>text</td>\n",
-       "      <td>document</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Q42470</td>\n",
-       "      <td>Q234460</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>motto</td>\n",
-       "      <td>text</td>\n",
-       "      <td>document</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>Q24060765</td>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>animal organ</td>\n",
-       "      <td>mouth</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>Q2424752</td>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>product</td>\n",
-       "      <td>software</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>Q2424752</td>\n",
-       "      <td>Q951964</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>product</td>\n",
-       "      <td>food product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>Q2424752</td>\n",
-       "      <td>Q951964</td>\n",
-       "      <td>software</td>\n",
-       "      <td>product</td>\n",
-       "      <td>food product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>114</th>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>Q294414</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>president</td>\n",
-       "      <td>public office</td>\n",
-       "      <td>head of state</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>Q11499267</td>\n",
-       "      <td>Q2995644</td>\n",
-       "      <td>Q50000</td>\n",
-       "      <td>defeat</td>\n",
-       "      <td>result</td>\n",
-       "      <td>victory</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Q1400881</td>\n",
-       "      <td>Q35120</td>\n",
-       "      <td>Q2574811</td>\n",
-       "      <td>property</td>\n",
-       "      <td>entity</td>\n",
-       "      <td>cause</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>Q154</td>\n",
-       "      <td>Q40050</td>\n",
-       "      <td>Q8486</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink</td>\n",
-       "      <td>coffee</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>Q16917</td>\n",
-       "      <td>Q4260475</td>\n",
-       "      <td>Q1774898</td>\n",
-       "      <td>hospital</td>\n",
-       "      <td>medical facility</td>\n",
-       "      <td>clinic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>Q2006324</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>agreement</td>\n",
-       "      <td>document</td>\n",
-       "      <td>ticket</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>159</th>\n",
-       "      <td>Q2006324</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>agreement</td>\n",
-       "      <td>document</td>\n",
-       "      <td>video</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>Q15921555</td>\n",
-       "      <td>Q61509</td>\n",
-       "      <td>Q17990955</td>\n",
-       "      <td>flight</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>sea voyage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>Q523</td>\n",
-       "      <td>Q6999</td>\n",
-       "      <td>Q8928</td>\n",
-       "      <td>star</td>\n",
-       "      <td>astronomical object</td>\n",
-       "      <td>constellation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>Q11028</td>\n",
-       "      <td>Q7184903</td>\n",
-       "      <td>Q450</td>\n",
-       "      <td>information</td>\n",
-       "      <td>abstract object</td>\n",
-       "      <td>mind</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         node1     parent      node2         node1_label            par_label  \\\n",
-       "3      Q176763     Q11028   Q3695082            evidence          information   \n",
-       "12    Q1308677   Q1190554  Q21171241          deployment           occurrence   \n",
-       "14    Q1308677   Q1190554   Q3839081          deployment           occurrence   \n",
-       "20        Q198   Q1190554   Q3839081                 war           occurrence   \n",
-       "38     Q381072   Q1190554   Q3839081              crisis           occurrence   \n",
-       "46     Q186360  Q11974939     Q39631               nurse  health professional   \n",
-       "52    Q4165229   Q1527264    Q570938                gain               income   \n",
-       "71     Q192935      Q2095   Q3314483             seafood                 food   \n",
-       "75    Q3314483      Q2095    Q951964               fruit                 food   \n",
-       "85     Q191067    Q234460     Q49848             article                 text   \n",
-       "88      Q42470    Q234460     Q49848               motto                 text   \n",
-       "93       Q7364  Q24060765      Q9635                 eye         animal organ   \n",
-       "97      Q39546   Q2424752      Q7397                tool              product   \n",
-       "98      Q39546   Q2424752    Q951964                tool              product   \n",
-       "101      Q7397   Q2424752    Q951964            software              product   \n",
-       "114     Q30461    Q294414     Q48352           president        public office   \n",
-       "118  Q11499267   Q2995644     Q50000              defeat               result   \n",
-       "129   Q1400881     Q35120   Q2574811            property               entity   \n",
-       "139       Q154     Q40050      Q8486  alcoholic beverage                drink   \n",
-       "143     Q16917   Q4260475   Q1774898            hospital     medical facility   \n",
-       "158   Q2006324     Q49848    Q551800           agreement             document   \n",
-       "159   Q2006324     Q49848  Q98069877           agreement             document   \n",
-       "168  Q15921555     Q61509  Q17990955              flight               travel   \n",
-       "174       Q523      Q6999      Q8928                star  astronomical object   \n",
-       "179     Q11028   Q7184903       Q450         information      abstract object   \n",
-       "\n",
-       "       node2_label  \n",
-       "3             sign  \n",
-       "12       departure  \n",
-       "14        disaster  \n",
-       "20        disaster  \n",
-       "38        disaster  \n",
-       "46       physician  \n",
-       "52      net income  \n",
-       "71           fruit  \n",
-       "75    food product  \n",
-       "85        document  \n",
-       "88        document  \n",
-       "93           mouth  \n",
-       "97        software  \n",
-       "98    food product  \n",
-       "101   food product  \n",
-       "114  head of state  \n",
-       "118        victory  \n",
-       "129          cause  \n",
-       "139         coffee  \n",
-       "143         clinic  \n",
-       "158         ticket  \n",
-       "159          video  \n",
-       "168     sea voyage  \n",
-       "174  constellation  \n",
-       "179           mind  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "beginning-ethnic",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-35-6a1335c5de17>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  pairsDF4['comb_sent'] = pairsDF4.apply(lambda p: p.par_label + ' such as ' + p.node1_label + ' and ' + p.node2_label, axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "pairsDF4['comb_sent'] = pairsDF4.apply(lambda p: p.par_label + ' such as ' + p.node1_label + ' and ' + p.node2_label, axis=1)\n",
-    "# pairsDF4['comb_sent'] = pairsDF4.apply(lambda p: p.par_label + ' is typically ' + p.node1_label + ' or ' + p.node2_label, axis=1)\n",
-    "# pairsDF4['comb_sent'] = pairsDF4.apply(lambda p: p.node1_label + ' and ' + p.node2_label + ' are examples of ' + p.par_label, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "subtle-owner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_SiblingsDF1 = p279_SiblingsDF.set_index(['node1', 'node2']).join(pairsDF4.set_index(['node1', 'node2'])[['parent', 'par_label']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "generous-russian",
-   "metadata": {},
-   "source": [
-    "# Language Models Utility Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "indie-costs",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getBERTSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "secondary-temperature",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
-    "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
-    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
-    "\n",
-    "def getGPT2Score(sent1):\n",
-    "    # input sentence tensor\n",
-    "    # output score\n",
-    "    tokens_tensor = tokenizer.encode(sent1, add_special_tokens=False, return_tensors=\"pt\")\n",
-    "    loss = model(tokens_tensor, labels=tokens_tensor)[0]\n",
-    "#     print(tokens_tensor.size(1))\n",
-    "    return np.exp(loss.cpu().detach().numpy() / tokens_tensor.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "religious-warrant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pytorch_pretrained_bert import BertTokenizer,BertForMaskedLM\n",
-    "import torch\n",
-    "import pandas as pd\n",
-    "import math\n",
-    "\n",
-    "bertMaskedLM = BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
-    "berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
-    "\n",
-    "def getBERTScore(sentence):\n",
-    "    tokenize_input = berttokenizer.tokenize(sentence)\n",
-    "    tensor_input = torch.tensor([berttokenizer.convert_tokens_to_ids(tokenize_input)])\n",
-    "    predictions=bertMaskedLM(tensor_input)\n",
-    "    loss_fct = torch.nn.CrossEntropyLoss()\n",
-    "    loss = loss_fct(predictions.squeeze(),tensor_input.squeeze()).data \n",
-    "    return math.exp(loss / tensor_input.size(1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exact-mirror",
-   "metadata": {},
-   "source": [
-    "# Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "boxed-electric",
-   "metadata": {},
-   "source": [
-    "## ChildPar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "aware-netherlands",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation_type</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>S_Sim</th>\n",
-       "      <th>S_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>J_Dim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q48842375</td>\n",
-       "      <td>Asian Women's Softball Championship</td>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q8001434</td>\n",
-       "      <td>Wildlife of South Africa</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>fauna</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.285714</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q40189366</td>\n",
-       "      <td>interstellar radiation field</td>\n",
-       "      <td>Q11379</td>\n",
-       "      <td>energy</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.714286</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q2162453</td>\n",
-       "      <td>traditional political party</td>\n",
-       "      <td>Q7278</td>\n",
-       "      <td>political party</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.714286</td>\n",
-       "      <td>0.487950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q6786579</td>\n",
-       "      <td>materials physics</td>\n",
-       "      <td>Q413</td>\n",
-       "      <td>physics</td>\n",
-       "      <td>parent/child</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>H</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID      node1                          node1_label     node2  \\\n",
-       "0   1  Q48842375  Asian Women's Softball Championship  Q1079023   \n",
-       "1   2   Q8001434             Wildlife of South Africa   Q104021   \n",
-       "2   3  Q40189366         interstellar radiation field    Q11379   \n",
-       "3   4   Q2162453          traditional political party     Q7278   \n",
-       "4   5   Q6786579                    materials physics      Q413   \n",
-       "\n",
-       "       node2_label relation_type  P_Sim P_Dim  H_Sim H_Dim  ...  S_Sim S_Dim  \\\n",
-       "0     championship  parent/child      2     i      2     I  ...      2     i   \n",
-       "1            fauna  parent/child      2     i      2     I  ...      2     i   \n",
-       "2           energy  parent/child      3     i      3     I  ...      2     i   \n",
-       "3  political party  parent/child      1   NaN      2     I  ...      2     i   \n",
-       "4          physics  parent/child      2     i      2     I  ...      2     i   \n",
-       "\n",
-       "   N_Sim N_Dim  F_Sim F_Dim  J_Sim J_Dim   Average    StdDev  \n",
-       "0      2     I      2     I      3     I  2.142857  0.377964  \n",
-       "1      3     H      2     I      3     I  2.285714  0.487950  \n",
-       "2      3     H      3     D      3     I  2.714286  0.487950  \n",
-       "3      2     I      2     I      2     I  1.714286  0.487950  \n",
-       "4      3     H      2     I      2     H  2.142857  0.377964  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_ChildParDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "coral-proportion",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-41-40cb45bbcd3a>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['sent'] = p279_ChildParDF.apply(lambda p: p['node1_label'] + ' is ' + p['node2_label'], axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279_ChildParDF['sent'] = p279_ChildParDF.apply(lambda p: p['node1_label'] + ' is ' + p['node2_label'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "inappropriate-disposal",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.1961464881896973 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-42-e0247d33ebc1>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['node1_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_ChildParDF.node1_label, 'bert-base-nli-mean-tokens')))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0921180248260498 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-42-e0247d33ebc1>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['node2_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_ChildParDF.node2_label, 'bert-base-nli-mean-tokens')))\n",
-      "<ipython-input-42-e0247d33ebc1>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['bert2SentSim'] = p279_ChildParDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-42-e0247d33ebc1>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['bert2SentSim_normed'] = p279_ChildParDF['bert2SentSim'].apply(lambda p: 4 - 3 * p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279_ChildParDF['node1_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_ChildParDF.node1_label, 'bert-base-nli-mean-tokens')))\n",
-    "p279_ChildParDF['node2_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_ChildParDF.node2_label, 'bert-base-nli-mean-tokens')))\n",
-    "p279_ChildParDF['bert2SentSim'] = p279_ChildParDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)\n",
-    "p279_ChildParDF['bert2SentSim_normed'] = p279_ChildParDF['bert2SentSim'].apply(lambda p: 4 - 3 * p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "domestic-wealth",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-43-83ad8e410c3b>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['gpt2Ppl'] = p279_ChildParDF.sent.apply(getGPT2Score)\n",
-      "<ipython-input-43-83ad8e410c3b>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['gpt2Ppl_normed'] = p279_ChildParDF['gpt2Ppl'].apply(lambda p: 4 - 3/(1+p))\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279_ChildParDF['gpt2Ppl'] = p279_ChildParDF.sent.apply(getGPT2Score)\n",
-    "p279_ChildParDF['gpt2Ppl_normed'] = p279_ChildParDF['gpt2Ppl'].apply(lambda p: 4 - 3/(1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "defensive-development",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-44-ee8882497c76>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['bert_ppl'] = p279_ChildParDF.sent.apply(getBERTScore)\n",
-      "<ipython-input-44-ee8882497c76>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  p279_ChildParDF['bert_ppl_normed'] = p279_ChildParDF['bert_ppl'].apply(lambda p: 4 - 3 / (1+p))\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279_ChildParDF['bert_ppl'] = p279_ChildParDF.sent.apply(getBERTScore)\n",
-    "p279_ChildParDF['bert_ppl_normed'] = p279_ChildParDF['bert_ppl'].apply(lambda p: 4 - 3 / (1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "encouraging-export",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>G_Sim</th>\n",
-       "      <th>S_Sim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>bert2SentSim_normed</th>\n",
-       "      <th>gpt2Ppl</th>\n",
-       "      <th>gpt2Ppl_normed</th>\n",
-       "      <th>bert_ppl</th>\n",
-       "      <th>bert_ppl_normed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.00000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>2.320000</td>\n",
-       "      <td>2.24000</td>\n",
-       "      <td>2.360000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.640000</td>\n",
-       "      <td>2.200000</td>\n",
-       "      <td>2.560000</td>\n",
-       "      <td>2.331429</td>\n",
-       "      <td>0.453733</td>\n",
-       "      <td>0.618659</td>\n",
-       "      <td>2.144024</td>\n",
-       "      <td>3.957845</td>\n",
-       "      <td>3.299563</td>\n",
-       "      <td>4.272888</td>\n",
-       "      <td>3.282017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7.359801</td>\n",
-       "      <td>0.556776</td>\n",
-       "      <td>0.43589</td>\n",
-       "      <td>0.757188</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.568624</td>\n",
-       "      <td>0.408248</td>\n",
-       "      <td>0.506623</td>\n",
-       "      <td>0.290788</td>\n",
-       "      <td>0.198787</td>\n",
-       "      <td>0.139203</td>\n",
-       "      <td>0.417609</td>\n",
-       "      <td>2.226331</td>\n",
-       "      <td>0.237021</td>\n",
-       "      <td>3.068290</td>\n",
-       "      <td>0.309286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.714286</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.253893</td>\n",
-       "      <td>1.356893</td>\n",
-       "      <td>1.730686</td>\n",
-       "      <td>2.901375</td>\n",
-       "      <td>1.432772</td>\n",
-       "      <td>2.766839</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "      <td>0.532726</td>\n",
-       "      <td>1.853700</td>\n",
-       "      <td>2.411533</td>\n",
-       "      <td>3.120630</td>\n",
-       "      <td>1.905373</td>\n",
-       "      <td>2.967430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.285714</td>\n",
-       "      <td>0.487950</td>\n",
-       "      <td>0.602635</td>\n",
-       "      <td>2.192096</td>\n",
-       "      <td>3.229335</td>\n",
-       "      <td>3.290669</td>\n",
-       "      <td>3.136767</td>\n",
-       "      <td>3.274796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>19.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "      <td>0.715433</td>\n",
-       "      <td>2.401822</td>\n",
-       "      <td>4.949554</td>\n",
-       "      <td>3.495761</td>\n",
-       "      <td>6.102226</td>\n",
-       "      <td>3.577597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.00000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.857143</td>\n",
-       "      <td>0.786796</td>\n",
-       "      <td>0.881036</td>\n",
-       "      <td>3.238321</td>\n",
-       "      <td>9.925628</td>\n",
-       "      <td>3.725416</td>\n",
-       "      <td>15.121811</td>\n",
-       "      <td>3.813917</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              ID      P_Sim     H_Sim      G_Sim  S_Sim      N_Sim      F_Sim  \\\n",
-       "count  25.000000  25.000000  25.00000  25.000000   25.0  25.000000  25.000000   \n",
-       "mean   13.000000   2.320000   2.24000   2.360000    2.0   2.640000   2.200000   \n",
-       "std     7.359801   0.556776   0.43589   0.757188    0.0   0.568624   0.408248   \n",
-       "min     1.000000   1.000000   2.00000   1.000000    2.0   2.000000   2.000000   \n",
-       "25%     7.000000   2.000000   2.00000   2.000000    2.0   2.000000   2.000000   \n",
-       "50%    13.000000   2.000000   2.00000   2.000000    2.0   3.000000   2.000000   \n",
-       "75%    19.000000   3.000000   2.00000   3.000000    2.0   3.000000   2.000000   \n",
-       "max    25.000000   3.000000   3.00000   4.000000    2.0   4.000000   3.000000   \n",
-       "\n",
-       "           J_Sim    Average     StdDev  bert2SentSim  bert2SentSim_normed  \\\n",
-       "count  25.000000  25.000000  25.000000     25.000000            25.000000   \n",
-       "mean    2.560000   2.331429   0.453733      0.618659             2.144024   \n",
-       "std     0.506623   0.290788   0.198787      0.139203             0.417609   \n",
-       "min     2.000000   1.714286   0.000000      0.253893             1.356893   \n",
-       "25%     2.000000   2.142857   0.377964      0.532726             1.853700   \n",
-       "50%     3.000000   2.285714   0.487950      0.602635             2.192096   \n",
-       "75%     3.000000   2.571429   0.534522      0.715433             2.401822   \n",
-       "max     3.000000   2.857143   0.786796      0.881036             3.238321   \n",
-       "\n",
-       "         gpt2Ppl  gpt2Ppl_normed   bert_ppl  bert_ppl_normed  \n",
-       "count  25.000000       25.000000  25.000000        25.000000  \n",
-       "mean    3.957845        3.299563   4.272888         3.282017  \n",
-       "std     2.226331        0.237021   3.068290         0.309286  \n",
-       "min     1.730686        2.901375   1.432772         2.766839  \n",
-       "25%     2.411533        3.120630   1.905373         2.967430  \n",
-       "50%     3.229335        3.290669   3.136767         3.274796  \n",
-       "75%     4.949554        3.495761   6.102226         3.577597  \n",
-       "max     9.925628        3.725416  15.121811         3.813917  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_ChildParDF.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "norwegian-parks",
-   "metadata": {},
-   "source": [
-    "### Correlation Coefficients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "careful-combining",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "corporate-potato",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.2084662497345841, pvalue=0.1621791786562139)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert2SentSim_normed'\n",
-    "stats.kendalltau(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "sacred-oklahoma",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.29501981151634904, pvalue=0.1522370779127172)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert2SentSim_normed'\n",
-    "stats.spearmanr(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "korean-tamil",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=-0.03886658893356653, pvalue=0.7943986542617295)"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert_ppl_normed'\n",
-    "stats.kendalltau(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "instructional-receptor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=-0.04475894238044873, pvalue=0.8317611751488296)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert_ppl_normed'\n",
-    "stats.spearmanr(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "hairy-workstation",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.017666631333439334, pvalue=0.905706418385764)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'gpt2Ppl_normed'\n",
-    "stats.kendalltau(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "wanted-indian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.05526756363498887, pvalue=0.7930202504178021)"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'gpt2Ppl_normed'\n",
-    "stats.spearmanr(p279_ChildParDF[col1], p279_ChildParDF['Average'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scheduled-queen",
-   "metadata": {},
-   "source": [
-    "## Siblings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "accepted-institution",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>relation_type</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>J_Dim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>par_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q4165229</td>\n",
-       "      <td>Q570938</td>\n",
-       "      <td>26</td>\n",
-       "      <td>gain</td>\n",
-       "      <td>net income</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.142857</td>\n",
-       "      <td>0.377964</td>\n",
-       "      <td>Q1527264</td>\n",
-       "      <td>income</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>27</td>\n",
-       "      <td>president</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "      <td>Q294414</td>\n",
-       "      <td>public office</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>28</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>disaster</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>occurrence</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>29</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>software</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.571429</td>\n",
-       "      <td>0.786796</td>\n",
-       "      <td>Q2424752</td>\n",
-       "      <td>product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1400881</td>\n",
-       "      <td>Q2574811</td>\n",
-       "      <td>30</td>\n",
-       "      <td>property</td>\n",
-       "      <td>cause</td>\n",
-       "      <td>sibling</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.714286</td>\n",
-       "      <td>0.755929</td>\n",
-       "      <td>Q35120</td>\n",
-       "      <td>entity</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1     node2  ID node1_label    node2_label relation_type  P_Sim  \\\n",
-       "0  Q4165229   Q570938  26        gain     net income       sibling      2   \n",
-       "1    Q30461    Q48352  27   president  head of state       sibling      1   \n",
-       "2   Q381072  Q3839081  28      crisis       disaster       sibling      2   \n",
-       "3    Q39546     Q7397  29        tool       software       sibling      3   \n",
-       "4  Q1400881  Q2574811  30    property          cause       sibling      4   \n",
-       "\n",
-       "  P_Dim  H_Sim H_Dim  ...  N_Sim N_Dim  F_Sim F_Dim  J_Sim J_Dim   Average  \\\n",
-       "0     i      2     I  ...      3     D      2     S      2     I  2.142857   \n",
-       "1   NaN      2     I  ...      2     I      1   NaN      2     I  1.571429   \n",
-       "2     i      2     I  ...      1   NaN      1   NaN      2     I  1.571429   \n",
-       "3     i      2     I  ...      3     I      2     I      4   NaN  2.571429   \n",
-       "4   NaN      4   NaN  ...      4   NaN      2     S      4   NaN  3.714286   \n",
-       "\n",
-       "     StdDev    parent      par_label  \n",
-       "0  0.377964  Q1527264         income  \n",
-       "1  0.534522   Q294414  public office  \n",
-       "2  0.534522  Q1190554     occurrence  \n",
-       "3  0.786796  Q2424752        product  \n",
-       "4  0.755929    Q35120         entity  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_SiblingsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "quarterly-conditioning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>par_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q4165229</td>\n",
-       "      <td>Q570938</td>\n",
-       "      <td>gain</td>\n",
-       "      <td>net income</td>\n",
-       "      <td>income</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>president</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>public office</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>disaster</td>\n",
-       "      <td>occurrence</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>software</td>\n",
-       "      <td>product</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1400881</td>\n",
-       "      <td>Q2574811</td>\n",
-       "      <td>property</td>\n",
-       "      <td>cause</td>\n",
-       "      <td>entity</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1     node2 node1_label    node2_label      par_label\n",
-       "0  Q4165229   Q570938        gain     net income         income\n",
-       "1    Q30461    Q48352   president  head of state  public office\n",
-       "2   Q381072  Q3839081      crisis       disaster     occurrence\n",
-       "3    Q39546     Q7397        tool       software        product\n",
-       "4  Q1400881  Q2574811    property          cause         entity"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_SiblingsDF1[['node1', 'node2', 'node1_label', 'node2_label', 'par_label']].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "vocational-trout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279_SiblingsDF1['comb_sent'] = p279_SiblingsDF1.apply(lambda p: p.par_label + ' such as ' + p.node1_label + ' and ' + p.node2_label, axis=1)\n",
-    "p279_SiblingsDF1['comb_sent'] = p279_SiblingsDF1.apply(lambda p: p.par_label + ' is typically ' + p.node1_label + ' or ' + p.node2_label, axis=1)\n",
-    "# p279_SiblingsDF1['comb_sent'] = p279_SiblingsDF1.apply(lambda p: p.node1_label + ' and ' + p.node2_label + ' are examples of ' + p.par_label, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "sapphire-penetration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_SiblingsDF1['node1_sent'] = p279_SiblingsDF1.apply(lambda p: p.node1_label + ' is ' + p.par_label, axis=1)\n",
-    "p279_SiblingsDF1['node2_sent'] = p279_SiblingsDF1.apply(lambda p: p.node2_label + ' is ' + p.par_label, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "collective-parks",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.08741140365600586 s\n",
-      "0.10142040252685547 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279_SiblingsDF1['node1_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_SiblingsDF1.node1_sent, 'bert-base-nli-mean-tokens')))\n",
-    "p279_SiblingsDF1['node2_emb'] = pd.Series(list(getBERTSentEmbeddings(p279_SiblingsDF1.node2_sent, 'bert-base-nli-mean-tokens')))\n",
-    "p279_SiblingsDF1['bert2SentSim'] = p279_SiblingsDF1.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)\n",
-    "p279_SiblingsDF1['bert2SentSim_normed'] = p279_SiblingsDF1['bert2SentSim'].apply(lambda p: 4 - 3 * p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "thrown-basis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_SiblingsDF1['gpt2Ppl'] = p279_SiblingsDF1.comb_sent.apply(getGPT2Score)\n",
-    "p279_SiblingsDF1['gpt2Ppl_normed'] = p279_SiblingsDF1['gpt2Ppl'].apply(lambda p: 4 - 3/(1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "external-genome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_SiblingsDF1['bert_ppl'] = p279_SiblingsDF1.comb_sent.apply(getBERTScore)\n",
-    "p279_SiblingsDF1['bert_ppl_normed'] = p279_SiblingsDF1['bert_ppl'].apply(lambda p: 4 - 3 / (1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "electoral-finger",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>G_Sim</th>\n",
-       "      <th>S_Sim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>J_Sim</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>StdDev</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>bert2SentSim_normed</th>\n",
-       "      <th>gpt2Ppl</th>\n",
-       "      <th>gpt2Ppl_normed</th>\n",
-       "      <th>bert_ppl</th>\n",
-       "      <th>bert_ppl_normed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>38.000000</td>\n",
-       "      <td>2.880000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>2.840000</td>\n",
-       "      <td>2.200000</td>\n",
-       "      <td>2.960000</td>\n",
-       "      <td>2.754286</td>\n",
-       "      <td>0.562032</td>\n",
-       "      <td>0.802449</td>\n",
-       "      <td>1.592652</td>\n",
-       "      <td>2.722357</td>\n",
-       "      <td>3.176962</td>\n",
-       "      <td>2.451830</td>\n",
-       "      <td>3.106608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7.359801</td>\n",
-       "      <td>0.927362</td>\n",
-       "      <td>0.816497</td>\n",
-       "      <td>1.154701</td>\n",
-       "      <td>0.816497</td>\n",
-       "      <td>0.943398</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>0.888819</td>\n",
-       "      <td>0.773190</td>\n",
-       "      <td>0.214379</td>\n",
-       "      <td>0.127288</td>\n",
-       "      <td>0.381863</td>\n",
-       "      <td>0.556725</td>\n",
-       "      <td>0.121081</td>\n",
-       "      <td>0.575310</td>\n",
-       "      <td>0.153490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.571429</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.469092</td>\n",
-       "      <td>1.113144</td>\n",
-       "      <td>1.709140</td>\n",
-       "      <td>2.892638</td>\n",
-       "      <td>1.476361</td>\n",
-       "      <td>2.788545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>32.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.285714</td>\n",
-       "      <td>0.487950</td>\n",
-       "      <td>0.736685</td>\n",
-       "      <td>1.281499</td>\n",
-       "      <td>2.397223</td>\n",
-       "      <td>3.116926</td>\n",
-       "      <td>1.949784</td>\n",
-       "      <td>2.982976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>38.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.571429</td>\n",
-       "      <td>0.534522</td>\n",
-       "      <td>0.822490</td>\n",
-       "      <td>1.532531</td>\n",
-       "      <td>2.619082</td>\n",
-       "      <td>3.171061</td>\n",
-       "      <td>2.590667</td>\n",
-       "      <td>3.164501</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>44.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.571429</td>\n",
-       "      <td>0.755929</td>\n",
-       "      <td>0.906167</td>\n",
-       "      <td>1.789944</td>\n",
-       "      <td>3.015666</td>\n",
-       "      <td>3.252926</td>\n",
-       "      <td>2.947505</td>\n",
-       "      <td>3.240026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>0.786796</td>\n",
-       "      <td>0.962285</td>\n",
-       "      <td>2.592723</td>\n",
-       "      <td>3.846773</td>\n",
-       "      <td>3.381031</td>\n",
-       "      <td>3.553839</td>\n",
-       "      <td>3.341215</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              ID      P_Sim      H_Sim      G_Sim      S_Sim      N_Sim  \\\n",
-       "count  25.000000  25.000000  25.000000  25.000000  25.000000  25.000000   \n",
-       "mean   38.000000   2.880000   3.000000   2.800000   2.600000   2.840000   \n",
-       "std     7.359801   0.927362   0.816497   1.154701   0.816497   0.943398   \n",
-       "min    26.000000   1.000000   2.000000   1.000000   2.000000   1.000000   \n",
-       "25%    32.000000   3.000000   2.000000   2.000000   2.000000   2.000000   \n",
-       "50%    38.000000   3.000000   3.000000   3.000000   2.000000   3.000000   \n",
-       "75%    44.000000   3.000000   4.000000   4.000000   3.000000   4.000000   \n",
-       "max    50.000000   4.000000   4.000000   4.000000   4.000000   4.000000   \n",
-       "\n",
-       "           F_Sim      J_Sim    Average     StdDev  bert2SentSim  \\\n",
-       "count  25.000000  25.000000  25.000000  25.000000     25.000000   \n",
-       "mean    2.200000   2.960000   2.754286   0.562032      0.802449   \n",
-       "std     0.866025   0.888819   0.773190   0.214379      0.127288   \n",
-       "min     1.000000   2.000000   1.571429   0.000000      0.469092   \n",
-       "25%     2.000000   2.000000   2.285714   0.487950      0.736685   \n",
-       "50%     2.000000   3.000000   2.571429   0.534522      0.822490   \n",
-       "75%     2.000000   4.000000   3.571429   0.755929      0.906167   \n",
-       "max     4.000000   4.000000   4.000000   0.786796      0.962285   \n",
-       "\n",
-       "       bert2SentSim_normed    gpt2Ppl  gpt2Ppl_normed   bert_ppl  \\\n",
-       "count            25.000000  25.000000       25.000000  25.000000   \n",
-       "mean              1.592652   2.722357        3.176962   2.451830   \n",
-       "std               0.381863   0.556725        0.121081   0.575310   \n",
-       "min               1.113144   1.709140        2.892638   1.476361   \n",
-       "25%               1.281499   2.397223        3.116926   1.949784   \n",
-       "50%               1.532531   2.619082        3.171061   2.590667   \n",
-       "75%               1.789944   3.015666        3.252926   2.947505   \n",
-       "max               2.592723   3.846773        3.381031   3.553839   \n",
-       "\n",
-       "       bert_ppl_normed  \n",
-       "count        25.000000  \n",
-       "mean          3.106608  \n",
-       "std           0.153490  \n",
-       "min           2.788545  \n",
-       "25%           2.982976  \n",
-       "50%           3.164501  \n",
-       "75%           3.240026  \n",
-       "max           3.341215  "
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_SiblingsDF1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continent-offering",
-   "metadata": {},
-   "source": [
-    "### Correlation Coefficients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "thousand-polish",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "alert-perspective",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3713116486531656, pvalue=0.011086947617304882)"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert2SentSim_normed'\n",
-    "stats.kendalltau(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "typical-rehabilitation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5082269055839773, pvalue=0.009488755079055035)"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert2SentSim_normed'\n",
-    "stats.spearmanr(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "genetic-paper",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.07563755805897819, pvalue=0.6048794180574604)"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert_ppl_normed'\n",
-    "stats.kendalltau(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "respected-capacity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.12222199555900824, pvalue=0.5605582648757985)"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'bert_ppl_normed'\n",
-    "stats.spearmanr(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "uniform-equality",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.309426373877638, pvalue=0.03429194537072205)"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'gpt2Ppl_normed'\n",
-    "stats.kendalltau(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "legitimate-muscle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4246827567208578, pvalue=0.03434090330987831)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col1 = 'gpt2Ppl_normed'\n",
-    "stats.spearmanr(p279_SiblingsDF1[col1], p279_SiblingsDF1['Average'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "intimate-chemical",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "conservative-client",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "raising-elements",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "shaped-citizen",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cardiac-pierce",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "lasting-omega",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/P279 Dataset Metrics Generation - Similarity Work Copy 3.ipynb b/P279 Dataset Metrics Generation - Similarity Work Copy 3.ipynb
deleted file mode 100644
index 33b3fed..0000000
--- a/P279 Dataset Metrics Generation - Similarity Work Copy 3.ipynb	
+++ /dev/null
@@ -1,4143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "protecting-craft",
-   "metadata": {},
-   "source": [
-    "Downloaded dataset from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "indirect-kingdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "import pandas as pd\n",
-    "from time import time\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "phantom-northeast",
-   "metadata": {},
-   "source": [
-    "# KGTK Text Embeddings Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "statistical-asian",
-   "metadata": {},
-   "source": [
-    "## Extracting wikidataos required properties "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "offensive-hacker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF = pd.read_csv('../data/P279_dataset/P279_19k_ChildPar_Dataset.csv')\n",
-    "p279Seeded_SiblingsDF3 = pd.read_csv('../data/P279_dataset/P279_19k_Siblings_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "effective-shepherd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesList = list(set(p279WordSimSeededDF.node1.to_list() + p279WordSimSeededDF.node2.to_list() + p279Seeded_SiblingsDF3.node1.to_list() + p279Seeded_SiblingsDF3.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "extraordinary-dinner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(p279QnodesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "included-wound",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bsize = len(p279QnodesList) // 25\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(p279QnodesList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz --word-separator '|' -p '\" + '|'.join(p279QnodesList[i:i+bsize]) + \";;' -o ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "adaptive-dragon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279QnodesListMissing = ['Q105427573', 'Q10953913', 'Q10998', 'Q111', 'Q11326182', 'Q1218', 'Q12897867', 'Q13371', 'Q1746570', 'Q17515', 'Q181201', 'Q19939', 'Q203', 'Q219060', 'Q23350', 'Q253276', 'Q25365', 'Q25472932', 'Q260460', 'Q30055', 'Q3149193', 'Q3230', 'Q32341', 'Q34211', 'Q35694', 'Q37413', 'Q42602', 'Q49112', 'Q4917', 'Q5113', 'Q729', 'Q7377', 'Q7795', 'Q795', 'Q8146', 'Q828224', 'Q8333', 'Q847337', 'Q85029384', 'Q9215']\n",
-    "# bsize = 25\n",
-    "# cnt = 27\n",
-    "# for i in range(0, len(p279QnodesListMissing), 25):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz --word-separator '|' -p '\" + '|'.join(p279QnodesListMissing[i:i+bsize]) + \";;' -o ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "requested-masters",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(1,11):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "center-brain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(11,21):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "european-population",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(21,27):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "lovely-ordinance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(27, 29):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "rubber-pattern",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "front-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(1,11):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "retired-formula",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(11,21):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "patient-newman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(21,27):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "governmental-mentor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(27,29):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/P279_dataset/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279   \\\n",
-    "#         --save-embedding-sentence > ../data/P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "elder-russian",
-   "metadata": {},
-   "source": [
-    "# Process and format DBPedia dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "characteristic-orientation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cnt = 0\n",
-    "\n",
-    "# p1s = []\n",
-    "# p11s = []\n",
-    "# p2s = []\n",
-    "# lines = []\n",
-    "# with open(\"../data/short-abstracts_lang=en.ttl\", 'r', encoding='utf-8') as f:\n",
-    "#     for line in tqdm(f):\n",
-    "#         p1 = line[:line.find(\" \")]\n",
-    "# #         p11 = p1.split(\"/\")[-1][:-1]\n",
-    "#         p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "#         line = line[line.find(\" \")+1:]\n",
-    "#         p2 = line[:line.find(\" \")]\n",
-    "#         line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "# #         print(p1, p2, line)\n",
-    "#         p1s.append(p1)\n",
-    "#         p11s.append(p11)\n",
-    "#         p2s.append(p2)\n",
-    "#         lines.append(line)\n",
-    "# #         except UnicodeDecodeError:\n",
-    "# #             cnt += 1\n",
-    "# # print(\"Encoding error:\", cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prostate-master",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "# df1.to_csv('../data/short-abstracts_lang=en.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "occupational-sheriff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "environmental-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dirty-sample",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df1.set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "defensive-switch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "specified-emission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "streaming-watch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "clean-brook",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "residential-bunch",
-   "metadata": {},
-   "source": [
-    "There are many duplicate element names with different abstracts. For example, 390 corresponds to year 390, ibm 390 systemss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "whole-retirement",
-   "metadata": {},
-   "source": [
-    "# Wikidata Sitelinks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "usual-albuquerque",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "classical-tamil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "undefined-gospel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "backed-criminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "armed-gardening",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "sitelinksDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "thousand-marriage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "alpha-sport",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "terminal-offer",
-   "metadata": {},
-   "source": [
-    "# Join sitelinks with DBPedia file - File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "particular-nerve",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dimensional-preserve",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "existing-disabled",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "floppy-mayor",
-   "metadata": {},
-   "source": [
-    "# P279 ChildPar, Siblings datasets + DBPedia abstracts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brazilian-isolation",
-   "metadata": {},
-   "source": [
-    "## Extract relevant DBPedia abstracts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "present-meeting",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "print(f\"We have {len(p279QnodesList)} no. of Qnodes extracted from node1, node2 of both ChildPar and Siblings datasets\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "minor-listening",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279QnodesSet = set(p279QnodesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "coral-royal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset = abstractsDF2[abstractsDF2.node1.apply(lambda p: p in p279QnodesSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unexpected-shepherd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset = abstractsDF_subset[~abstractsDF_subset.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "shaped-cambridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documentary-temple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "abstractsDF_subset['abstract_firstSent'] = abstractsDF_subset.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ranging-advertiser",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset.to_csv('../data/P279_dataset/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "absolute-gentleman",
-   "metadata": {},
-   "source": [
-    "## Generate BERT Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "hired-giant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset = pd.read_csv('../data/P279_dataset/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 368,
-   "id": "improved-place",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11745"
-      ]
-     },
-     "execution_count": 368,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 369,
-   "id": "passive-penetration",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have 19126 no. of Qnodes extracted from node1, node2 of both ChildPar and Siblings datasets\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"We have {len(p279QnodesList)} no. of Qnodes extracted from node1, node2 of both ChildPar and Siblings datasets\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "frank-boutique",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getBERTSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "written-arena",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF_subset = abstractsDF_subset.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cutting-improvement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset['BERT_abstract_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "earned-night",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset['BERT_abstract_firstSent_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "productive-diving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "verbal-qualification",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb']:\n",
-    "    abstractsDF_subset[col] = abstractsDF_subset[col].apply(lambda p: p.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "noble-catalog",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset[abstractsDF_subset.node1.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "blind-violence",
-   "metadata": {},
-   "source": [
-    "## Merge abstract embeddings with 19k file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "billion-paradise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs = p279WordSimSeededDF.set_index('node1').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "organized-investing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs = p279Seeded_SiblingsDF3.set_index('node1').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "continuing-synthesis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs1 = p279WordSimSeededDF_wabs.reset_index().set_index('node2').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "private-singles",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs1 = p279Seeded_SiblingsDF3_wabs.reset_index().set_index('node2').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "demanding-detector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs1 = p279WordSimSeededDF_wabs1.reset_index().rename(columns={'level_0':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "exempt-meditation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs1 = p279Seeded_SiblingsDF3_wabs1.reset_index().rename(columns={'level_0':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "local-accident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "allied-comedy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 366,
-   "id": "human-burner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(19454, 10797)"
-      ]
-     },
-     "execution_count": 366,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279WordSimSeededDF), len(p279WordSimSeededDF_wabs1[(~p279WordSimSeededDF_wabs1.abstract.isna()) & (~p279WordSimSeededDF_wabs1.abstract_right.isna())])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "id": "turned-planning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(19454, 12242)"
-      ]
-     },
-     "execution_count": 367,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279WordSimSeededDF_wabs), len(p279Seeded_SiblingsDF3_wabs1[(~p279Seeded_SiblingsDF3_wabs1.abstract.isna()) & (~p279Seeded_SiblingsDF3_wabs1.abstract_right.isna())])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assisted-portugal",
-   "metadata": {},
-   "source": [
-    "# Merge text embeddings with 19k file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "behavioral-punch",
-   "metadata": {},
-   "source": [
-    "## Combining kgtk text embeddings into one file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "boolean-composition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !head ../data/P279_dataset/output/P279-text-embedding-7-props-part-2.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "frozen-testing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DFArr = []\n",
-    "for cnt in range(1,29):\n",
-    "    textEmb_7props_DFArr.append(pd.read_csv(\"../data/P279_dataset/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\", sep='\\t'))\n",
-    "textEmb_7props_DF = pd.concat(textEmb_7props_DFArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "stretch-roads",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF.to_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "vocational-sequence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_2props_DFArr = []\n",
-    "for cnt in range(1,29):\n",
-    "    textEmb_2props_DFArr.append(pd.read_csv(\"../data/P279_dataset/output/P279-text-embedding-2-props-part-\" + str(cnt) + \".tsv\", sep='\\t'))\n",
-    "textEmb_2props_DF = pd.concat(textEmb_2props_DFArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "united-entrepreneur",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_2props_DF.to_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "mysterious-corps",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = textEmb_7props_DF[textEmb_7props_DF.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "significant-thickness",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_2props_DF = textEmb_2props_DF[textEmb_2props_DF.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "id": "actual-arrangement",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q99907279</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.13148594,0.20147827,0.015393771,1.1277922,-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99898510</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.19386396,0.41306746,-0.32783666,0.45018852,0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q99526025</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.89479333,0.27038768,0.15989815,-0.09214429,0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q99365546</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.10686234,0.95048314,0.32389534,0.19600846,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q993308</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.23211154,0.060303785,-1.0003253,0.63782924,0...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node        property  \\\n",
-       "0  Q99907279  text_embedding   \n",
-       "2  Q99898510  text_embedding   \n",
-       "4  Q99526025  text_embedding   \n",
-       "6  Q99365546  text_embedding   \n",
-       "8    Q993308  text_embedding   \n",
-       "\n",
-       "                                               value  \n",
-       "0  -0.13148594,0.20147827,0.015393771,1.1277922,-...  \n",
-       "2  0.19386396,0.41306746,-0.32783666,0.45018852,0...  \n",
-       "4  0.89479333,0.27038768,0.15989815,-0.09214429,0...  \n",
-       "6  0.10686234,0.95048314,0.32389534,0.19600846,0....  \n",
-       "8  0.23211154,0.060303785,-1.0003253,0.63782924,0...  "
-      ]
-     },
-     "execution_count": 273,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "korean-arthritis",
-   "metadata": {},
-   "source": [
-    "## Merge Step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "id": "authentic-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs1.set_index('node1').join(textEmb_7props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "id": "about-extreme",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs1.set_index('node1').join(textEmb_7props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "id": "peaceful-monitoring",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs_text.reset_index().rename(columns={'level_0':'node1'}).set_index('node2').join(textEmb_7props_DF.set_index('node')[['value']], rsuffix='_right')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "id": "confidential-academy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs_text.reset_index().rename(columns={'level_0':'node1'}).set_index('node2').join(textEmb_7props_DF.set_index('node')[['value']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "id": "twenty-feelings",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs_text.reset_index().rename(columns={'level_0':'node2', 'value': 'textEmb_7props_left', 'value_right': 'textEmb_7props_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "id": "phantom-treat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs_text.reset_index().rename(columns={'level_0':'node2', 'value': 'textEmb_7props_left', 'value_right': 'textEmb_7props_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "weighted-wesley",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "id": "adopted-tracker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs_text.set_index('node1').join(textEmb_2props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "id": "golden-tobacco",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs_text.set_index('node1').join(textEmb_2props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "id": "grave-outreach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs_text.reset_index().rename(columns={'level_0':'node1'}).set_index('node2').join(textEmb_2props_DF.set_index('node')[['value']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "id": "nuclear-lafayette",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs_text.reset_index().rename(columns={'level_0':'node1'}).set_index('node2').join(textEmb_2props_DF.set_index('node')[['value']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "id": "inner-bibliography",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = p279WordSimSeededDF_wabs_text.reset_index().rename(columns={'level_0':'node2', 'value': 'textEmb_2props_left', 'value_right': 'textEmb_2props_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "id": "sixth-indianapolis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = p279Seeded_SiblingsDF3_wabs_text.reset_index().rename(columns={'level_0':'node2', 'value': 'textEmb_2props_left', 'value_right': 'textEmb_2props_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "id": "infrared-mystery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>0</td>\n",
-       "      <td>160</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.18359379470348358, 0.4367634654045105, -0.3...</td>\n",
-       "      <td>[0.6000513434410095, 0.261519193649292, -0.064...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.87660795,0.4991357,-0.39552155,0.7046891,1.1...</td>\n",
-       "      <td>0.36156428,-0.015657336,-0.14483449,-0.2533233...</td>\n",
-       "      <td>0.79082793,0.53679967,-0.3825465,0.67784667,1....</td>\n",
-       "      <td>0.36156428,-0.015657336,-0.14483449,-0.2533233...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>1</td>\n",
-       "      <td>211</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Periodical literature (also called a periodica...</td>\n",
-       "      <td>Periodical literature (also called a periodica...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>1.256428,0.3254261,-0.26960722,0.101883665,1.1...</td>\n",
-       "      <td>0.92698413,0.32396156,-0.7647308,0.6973268,0.7...</td>\n",
-       "      <td>1.256428,0.3254261,-0.26960722,0.101883665,1.1...</td>\n",
-       "      <td>1.0344226,0.39984593,-0.76180136,0.8256311,0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3495</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.6039562225341797, 0.8376446962356567, -0.3...</td>\n",
-       "      <td>[0.07287552207708359, -0.12277502566576004, 0....</td>\n",
-       "      <td>A biome  is a community of plants and animals ...</td>\n",
-       "      <td>A biome  is a community of plants and animals ...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>0.8141036,0.14491142,-0.59703183,0.335001,0.94...</td>\n",
-       "      <td>-0.16750671,-0.17960861,-0.58211946,-0.6872659...</td>\n",
-       "      <td>0.91628444,0.104261845,-0.49683735,0.2129627,1...</td>\n",
-       "      <td>-0.13534825,-0.23398273,-0.5165668,-0.72972137...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3827</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.08822021633386612, 0.4630779027938843, -1.0...</td>\n",
-       "      <td>[0.027711834758520126, 0.15031695365905762, -0...</td>\n",
-       "      <td>In chemistry and physics, a nucleon is either ...</td>\n",
-       "      <td>In chemistry and physics, a nucleon is either ...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>0.469377,0.7162001,-0.36740896,0.76742333,1.00...</td>\n",
-       "      <td>-0.11307852,0.65730673,-0.13959256,1.4663302,1...</td>\n",
-       "      <td>0.4080446,0.7498908,-0.2665757,0.74437106,1.11...</td>\n",
-       "      <td>-0.11307852,0.65730673,-0.13959256,1.4663302,1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4267</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.06287963688373566, 0.49383553862571716, 0.2...</td>\n",
-       "      <td>[0.4055871367454529, 0.0892435610294342, 0.801...</td>\n",
-       "      <td>In physics, a fluid is a substance that contin...</td>\n",
-       "      <td>In physics, a fluid is a substance that contin...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>-0.19995248,0.65605164,-0.14234662,0.43931133,...</td>\n",
-       "      <td>0.4293771,1.1976917,0.04422909,0.5975481,0.988...</td>\n",
-       "      <td>-0.45549682,0.73478854,-0.1980287,0.43679318,0...</td>\n",
-       "      <td>0.33631665,1.1661042,0.13744242,0.6776439,0.87...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1  Unnamed: 0  index  \\\n",
-       "0  Q100171002  Q1503443           0    160   \n",
-       "1    Q1002697    Q49850           1    211   \n",
-       "2     Q101998     Q4421           2   3495   \n",
-       "3     Q102165     Q2294           3   3827   \n",
-       "4     Q102205    Q11435           4   4267   \n",
-       "\n",
-       "                                    id node1_label label  node2_label  \\\n",
-       "0  Q1503443-P279-Q100171002-8282ae74-0     secrecy  P279  concealment   \n",
-       "1      Q49850-P279-Q1002697-0c52cf68-0     journal  P279   periodical   \n",
-       "2        Q4421-P279-Q101998-d0983886-0      forest  P279        biome   \n",
-       "3        Q2294-P279-Q102165-0893a180-0      proton  P279      nucleon   \n",
-       "4       Q11435-P279-Q102205-7b448d05-0      liquid  P279        fluid   \n",
-       "\n",
-       "                     sent                                          node1_emb  \\\n",
-       "0  secrecy is concealment  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1   journal is periodical  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2         forest is biome  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3       proton is nucleon  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4         liquid is fluid  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "   ...                                  BERT_abstract_emb  \\\n",
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-       "1  ...                                                NaN   \n",
-       "2  ...  [-0.6039562225341797, 0.8376446962356567, -0.3...   \n",
-       "3  ...  [0.08822021633386612, 0.4630779027938843, -1.0...   \n",
-       "4  ...  [0.06287963688373566, 0.49383553862571716, 0.2...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb  \\\n",
-       "0  [0.6000513434410095, 0.261519193649292, -0.064...   \n",
-       "1                                                NaN   \n",
-       "2  [0.07287552207708359, -0.12277502566576004, 0....   \n",
-       "3  [0.027711834758520126, 0.15031695365905762, -0...   \n",
-       "4  [0.4055871367454529, 0.0892435610294342, 0.801...   \n",
-       "\n",
-       "                                      abstract_right  \\\n",
-       "0                                                NaN   \n",
-       "1  Periodical literature (also called a periodica...   \n",
-       "2  A biome  is a community of plants and animals ...   \n",
-       "3  In chemistry and physics, a nucleon is either ...   \n",
-       "4  In physics, a fluid is a substance that contin...   \n",
-       "\n",
-       "                            abstract_firstSent_right  \\\n",
-       "0                                                NaN   \n",
-       "1  Periodical literature (also called a periodica...   \n",
-       "2  A biome  is a community of plants and animals ...   \n",
-       "3  In chemistry and physics, a nucleon is either ...   \n",
-       "4  In physics, a fluid is a substance that contin...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
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-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
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-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
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-       "2  0.8141036,0.14491142,-0.59703183,0.335001,0.94...   \n",
-       "3  0.469377,0.7162001,-0.36740896,0.76742333,1.00...   \n",
-       "4  -0.19995248,0.65605164,-0.14234662,0.43931133,...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  0.36156428,-0.015657336,-0.14483449,-0.2533233...   \n",
-       "1  0.92698413,0.32396156,-0.7647308,0.6973268,0.7...   \n",
-       "2  -0.16750671,-0.17960861,-0.58211946,-0.6872659...   \n",
-       "3  -0.11307852,0.65730673,-0.13959256,1.4663302,1...   \n",
-       "4  0.4293771,1.1976917,0.04422909,0.5975481,0.988...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  0.79082793,0.53679967,-0.3825465,0.67784667,1....   \n",
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-       "2  0.91628444,0.104261845,-0.49683735,0.2129627,1...   \n",
-       "3  0.4080446,0.7498908,-0.2665757,0.74437106,1.11...   \n",
-       "4  -0.45549682,0.73478854,-0.1980287,0.43679318,0...   \n",
-       "\n",
-       "                                textEmb_2props_right  \n",
-       "0  0.36156428,-0.015657336,-0.14483449,-0.2533233...  \n",
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-       "2  -0.13534825,-0.23398273,-0.5165668,-0.72972137...  \n",
-       "3  -0.11307852,0.65730673,-0.13959256,1.4663302,1...  \n",
-       "4  0.33631665,1.1661042,0.13744242,0.6776439,0.87...  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "id": "included-coverage",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>0</th>\n",
-       "      <td>Q100400481</td>\n",
-       "      <td>Q100355826</td>\n",
-       "      <td>1123</td>\n",
-       "      <td>10344291</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Yukoners</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Detroiters</td>\n",
-       "      <td>Yukoners is people</td>\n",
-       "      <td>Detroiters is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>-0.07126519,0.6527018,-0.3797528,0.19690958,0....</td>\n",
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-       "      <td>-0.07126519,0.6527018,-0.3797528,0.19690958,0....</td>\n",
-       "      <td>0.52179945,0.6418564,0.3750265,0.5378938,-0.77...</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100448818</td>\n",
-       "      <td>Q100196985</td>\n",
-       "      <td>8456</td>\n",
-       "      <td>10339627</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Oregonians</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Kuwaitis</td>\n",
-       "      <td>Oregonians is people</td>\n",
-       "      <td>Kuwaitis is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.082290515,0.7254986,-0.14525111,0.3196717,0...</td>\n",
-       "      <td>-0.42398202,0.17036317,0.35879236,1.1121372,0....</td>\n",
-       "      <td>-0.082290515,0.7254986,-0.14525111,0.3196717,0...</td>\n",
-       "      <td>-0.42398202,0.17036317,0.35879236,1.1121372,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100588072</td>\n",
-       "      <td>Q100568638</td>\n",
-       "      <td>3032</td>\n",
-       "      <td>11887935</td>\n",
-       "      <td>Q846570</td>\n",
-       "      <td>Utahns</td>\n",
-       "      <td>Americans</td>\n",
-       "      <td>Wyomingites</td>\n",
-       "      <td>Utahns is Americans</td>\n",
-       "      <td>Wyomingites is Americans</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.17937128,-0.30326533,-0.14656651,0.26193923...</td>\n",
-       "      <td>0.2421372,0.41370165,-0.017250024,0.8962314,-0...</td>\n",
-       "      <td>-0.17937128,-0.30326533,-0.14656651,0.26193923...</td>\n",
-       "      <td>0.2421372,0.41370165,-0.017250024,0.8962314,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>Q100440056</td>\n",
-       "      <td>13959</td>\n",
-       "      <td>10346647</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Moldovans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>Moldovans is people</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.27328575,0.3186499,-0.13949376,1.0969052,-0....</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "      <td>0.27328575,0.3186499,-0.13949376,1.0969052,-0....</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100606074</td>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>5612</td>\n",
-       "      <td>10351613</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Hoosiers</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>Hoosiers is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>-0.4371684,1.0444875,-0.63107914,0.07898032,-0...</td>\n",
-       "    </tr>\n",
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-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
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-      "text/plain": [
-       "        node2       node1  Unnamed: 0     index    parent node1_label  \\\n",
-       "0  Q100400481  Q100355826        1123  10344291  Q2472587    Yukoners   \n",
-       "1  Q100448818  Q100196985        8456  10339627  Q2472587  Oregonians   \n",
-       "2  Q100588072  Q100568638        3032  11887935   Q846570      Utahns   \n",
-       "3  Q100606028  Q100440056       13959  10346647  Q2472587   Moldovans   \n",
-       "4  Q100606074  Q100606028        5612  10351613  Q2472587      Iowans   \n",
-       "\n",
-       "   par_label  node2_label            node1_sent                node2_sent  \\\n",
-       "0     people   Detroiters    Yukoners is people      Detroiters is people   \n",
-       "1     people     Kuwaitis  Oregonians is people        Kuwaitis is people   \n",
-       "2  Americans  Wyomingites   Utahns is Americans  Wyomingites is Americans   \n",
-       "3     people       Iowans   Moldovans is people          Iowans is people   \n",
-       "4     people     Hoosiers      Iowans is people        Hoosiers is people   \n",
-       "\n",
-       "   ... BERT_abstract_emb BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  ...               NaN                         NaN            NaN   \n",
-       "1  ...               NaN                         NaN            NaN   \n",
-       "2  ...               NaN                         NaN            NaN   \n",
-       "3  ...               NaN                         NaN            NaN   \n",
-       "4  ...               NaN                         NaN            NaN   \n",
-       "\n",
-       "   abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                       NaN                     NaN   \n",
-       "1                       NaN                     NaN   \n",
-       "2                       NaN                     NaN   \n",
-       "3                       NaN                     NaN   \n",
-       "4                       NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  -0.07126519,0.6527018,-0.3797528,0.19690958,0....   \n",
-       "1  -0.082290515,0.7254986,-0.14525111,0.3196717,0...   \n",
-       "2  -0.17937128,-0.30326533,-0.14656651,0.26193923...   \n",
-       "3  0.27328575,0.3186499,-0.13949376,1.0969052,-0....   \n",
-       "4  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  0.52179945,0.6418564,0.3750265,0.5378938,-0.77...   \n",
-       "1  -0.42398202,0.17036317,0.35879236,1.1121372,0....   \n",
-       "2  0.2421372,0.41370165,-0.017250024,0.8962314,-0...   \n",
-       "3  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "4  -0.4371684,1.0444875,-0.63107914,0.07898032,-0...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  -0.07126519,0.6527018,-0.3797528,0.19690958,0....   \n",
-       "1  -0.082290515,0.7254986,-0.14525111,0.3196717,0...   \n",
-       "2  -0.17937128,-0.30326533,-0.14656651,0.26193923...   \n",
-       "3  0.27328575,0.3186499,-0.13949376,1.0969052,-0....   \n",
-       "4  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "\n",
-       "                                textEmb_2props_right  \n",
-       "0  0.52179945,0.6418564,0.3750265,0.5378938,-0.77...  \n",
-       "1  -0.42398202,0.17036317,0.35879236,1.1121372,0....  \n",
-       "2  0.2421372,0.41370165,-0.017250024,0.8962314,-0...  \n",
-       "3  0.025020197,0.4693855,-0.50199574,1.3510414,-0...  \n",
-       "4  -0.4371684,1.0444875,-0.63107914,0.07898032,-0...  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "id": "separated-region",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(19454, 19454)"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279WordSimSeededDF_wabs_text), len(p279WordSimSeededDF_wabs_text[(~p279WordSimSeededDF_wabs_text.textEmb_7props_left.isna()) & (~p279WordSimSeededDF_wabs_text.textEmb_7props_right.isna())])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "id": "piano-cameroon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(19454, 19454)"
-      ]
-     },
-     "execution_count": 289,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279Seeded_SiblingsDF3_wabs_text), len(p279Seeded_SiblingsDF3_wabs_text[(~p279Seeded_SiblingsDF3_wabs_text.textEmb_2props_left.isna()) & (~p279Seeded_SiblingsDF3_wabs_text.textEmb_2props_right.isna())])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "social-somerset",
-   "metadata": {},
-   "source": [
-    "# CHECKPOINT: Store all embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "id": "fitted-frequency",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>0</td>\n",
-       "      <td>160</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.18359379470348358, 0.4367634654045105, -0.3...</td>\n",
-       "      <td>[0.6000513434410095, 0.261519193649292, -0.064...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.87660795,0.4991357,-0.39552155,0.7046891,1.1...</td>\n",
-       "      <td>0.36156428,-0.015657336,-0.14483449,-0.2533233...</td>\n",
-       "      <td>0.79082793,0.53679967,-0.3825465,0.67784667,1....</td>\n",
-       "      <td>0.36156428,-0.015657336,-0.14483449,-0.2533233...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>1</td>\n",
-       "      <td>211</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Periodical literature (also called a periodica...</td>\n",
-       "      <td>Periodical literature (also called a periodica...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>1.256428,0.3254261,-0.26960722,0.101883665,1.1...</td>\n",
-       "      <td>0.92698413,0.32396156,-0.7647308,0.6973268,0.7...</td>\n",
-       "      <td>1.256428,0.3254261,-0.26960722,0.101883665,1.1...</td>\n",
-       "      <td>1.0344226,0.39984593,-0.76180136,0.8256311,0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3495</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.6039562225341797, 0.8376446962356567, -0.3...</td>\n",
-       "      <td>[0.07287552207708359, -0.12277502566576004, 0....</td>\n",
-       "      <td>A biome  is a community of plants and animals ...</td>\n",
-       "      <td>A biome  is a community of plants and animals ...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>0.8141036,0.14491142,-0.59703183,0.335001,0.94...</td>\n",
-       "      <td>-0.16750671,-0.17960861,-0.58211946,-0.6872659...</td>\n",
-       "      <td>0.91628444,0.104261845,-0.49683735,0.2129627,1...</td>\n",
-       "      <td>-0.13534825,-0.23398273,-0.5165668,-0.72972137...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3827</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.08822021633386612, 0.4630779027938843, -1.0...</td>\n",
-       "      <td>[0.027711834758520126, 0.15031695365905762, -0...</td>\n",
-       "      <td>In chemistry and physics, a nucleon is either ...</td>\n",
-       "      <td>In chemistry and physics, a nucleon is either ...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>0.469377,0.7162001,-0.36740896,0.76742333,1.00...</td>\n",
-       "      <td>-0.11307852,0.65730673,-0.13959256,1.4663302,1...</td>\n",
-       "      <td>0.4080446,0.7498908,-0.2665757,0.74437106,1.11...</td>\n",
-       "      <td>-0.11307852,0.65730673,-0.13959256,1.4663302,1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4267</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.06287963688373566, 0.49383553862571716, 0.2...</td>\n",
-       "      <td>[0.4055871367454529, 0.0892435610294342, 0.801...</td>\n",
-       "      <td>In physics, a fluid is a substance that contin...</td>\n",
-       "      <td>In physics, a fluid is a substance that contin...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>-0.19995248,0.65605164,-0.14234662,0.43931133,...</td>\n",
-       "      <td>0.4293771,1.1976917,0.04422909,0.5975481,0.988...</td>\n",
-       "      <td>-0.45549682,0.73478854,-0.1980287,0.43679318,0...</td>\n",
-       "      <td>0.33631665,1.1661042,0.13744242,0.6776439,0.87...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1  Unnamed: 0  index  \\\n",
-       "0  Q100171002  Q1503443           0    160   \n",
-       "1    Q1002697    Q49850           1    211   \n",
-       "2     Q101998     Q4421           2   3495   \n",
-       "3     Q102165     Q2294           3   3827   \n",
-       "4     Q102205    Q11435           4   4267   \n",
-       "\n",
-       "                                    id node1_label label  node2_label  \\\n",
-       "0  Q1503443-P279-Q100171002-8282ae74-0     secrecy  P279  concealment   \n",
-       "1      Q49850-P279-Q1002697-0c52cf68-0     journal  P279   periodical   \n",
-       "2        Q4421-P279-Q101998-d0983886-0      forest  P279        biome   \n",
-       "3        Q2294-P279-Q102165-0893a180-0      proton  P279      nucleon   \n",
-       "4       Q11435-P279-Q102205-7b448d05-0      liquid  P279        fluid   \n",
-       "\n",
-       "                     sent                                          node1_emb  \\\n",
-       "0  secrecy is concealment  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1   journal is periodical  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2         forest is biome  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3       proton is nucleon  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4         liquid is fluid  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "   ...                                  BERT_abstract_emb  \\\n",
-       "0  ...  [0.18359379470348358, 0.4367634654045105, -0.3...   \n",
-       "1  ...                                                NaN   \n",
-       "2  ...  [-0.6039562225341797, 0.8376446962356567, -0.3...   \n",
-       "3  ...  [0.08822021633386612, 0.4630779027938843, -1.0...   \n",
-       "4  ...  [0.06287963688373566, 0.49383553862571716, 0.2...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb  \\\n",
-       "0  [0.6000513434410095, 0.261519193649292, -0.064...   \n",
-       "1                                                NaN   \n",
-       "2  [0.07287552207708359, -0.12277502566576004, 0....   \n",
-       "3  [0.027711834758520126, 0.15031695365905762, -0...   \n",
-       "4  [0.4055871367454529, 0.0892435610294342, 0.801...   \n",
-       "\n",
-       "                                      abstract_right  \\\n",
-       "0                                                NaN   \n",
-       "1  Periodical literature (also called a periodica...   \n",
-       "2  A biome  is a community of plants and animals ...   \n",
-       "3  In chemistry and physics, a nucleon is either ...   \n",
-       "4  In physics, a fluid is a substance that contin...   \n",
-       "\n",
-       "                            abstract_firstSent_right  \\\n",
-       "0                                                NaN   \n",
-       "1  Periodical literature (also called a periodica...   \n",
-       "2  A biome  is a community of plants and animals ...   \n",
-       "3  In chemistry and physics, a nucleon is either ...   \n",
-       "4  In physics, a fluid is a substance that contin...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  0.87660795,0.4991357,-0.39552155,0.7046891,1.1...   \n",
-       "1  1.256428,0.3254261,-0.26960722,0.101883665,1.1...   \n",
-       "2  0.8141036,0.14491142,-0.59703183,0.335001,0.94...   \n",
-       "3  0.469377,0.7162001,-0.36740896,0.76742333,1.00...   \n",
-       "4  -0.19995248,0.65605164,-0.14234662,0.43931133,...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  0.36156428,-0.015657336,-0.14483449,-0.2533233...   \n",
-       "1  0.92698413,0.32396156,-0.7647308,0.6973268,0.7...   \n",
-       "2  -0.16750671,-0.17960861,-0.58211946,-0.6872659...   \n",
-       "3  -0.11307852,0.65730673,-0.13959256,1.4663302,1...   \n",
-       "4  0.4293771,1.1976917,0.04422909,0.5975481,0.988...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  0.79082793,0.53679967,-0.3825465,0.67784667,1....   \n",
-       "1  1.256428,0.3254261,-0.26960722,0.101883665,1.1...   \n",
-       "2  0.91628444,0.104261845,-0.49683735,0.2129627,1...   \n",
-       "3  0.4080446,0.7498908,-0.2665757,0.74437106,1.11...   \n",
-       "4  -0.45549682,0.73478854,-0.1980287,0.43679318,0...   \n",
-       "\n",
-       "                                textEmb_2props_right  \n",
-       "0  0.36156428,-0.015657336,-0.14483449,-0.2533233...  \n",
-       "1  1.0344226,0.39984593,-0.76180136,0.8256311,0.6...  \n",
-       "2  -0.13534825,-0.23398273,-0.5165668,-0.72972137...  \n",
-       "3  -0.11307852,0.65730673,-0.13959256,1.4663302,1...  \n",
-       "4  0.33631665,1.1661042,0.13744242,0.6776439,0.87...  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "id": "inner-solomon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100400481</td>\n",
-       "      <td>Q100355826</td>\n",
-       "      <td>1123</td>\n",
-       "      <td>10344291</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Yukoners</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Detroiters</td>\n",
-       "      <td>Yukoners is people</td>\n",
-       "      <td>Detroiters is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.07126519,0.6527018,-0.3797528,0.19690958,0....</td>\n",
-       "      <td>0.52179945,0.6418564,0.3750265,0.5378938,-0.77...</td>\n",
-       "      <td>-0.07126519,0.6527018,-0.3797528,0.19690958,0....</td>\n",
-       "      <td>0.52179945,0.6418564,0.3750265,0.5378938,-0.77...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100448818</td>\n",
-       "      <td>Q100196985</td>\n",
-       "      <td>8456</td>\n",
-       "      <td>10339627</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Oregonians</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Kuwaitis</td>\n",
-       "      <td>Oregonians is people</td>\n",
-       "      <td>Kuwaitis is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.082290515,0.7254986,-0.14525111,0.3196717,0...</td>\n",
-       "      <td>-0.42398202,0.17036317,0.35879236,1.1121372,0....</td>\n",
-       "      <td>-0.082290515,0.7254986,-0.14525111,0.3196717,0...</td>\n",
-       "      <td>-0.42398202,0.17036317,0.35879236,1.1121372,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100588072</td>\n",
-       "      <td>Q100568638</td>\n",
-       "      <td>3032</td>\n",
-       "      <td>11887935</td>\n",
-       "      <td>Q846570</td>\n",
-       "      <td>Utahns</td>\n",
-       "      <td>Americans</td>\n",
-       "      <td>Wyomingites</td>\n",
-       "      <td>Utahns is Americans</td>\n",
-       "      <td>Wyomingites is Americans</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.17937128,-0.30326533,-0.14656651,0.26193923...</td>\n",
-       "      <td>0.2421372,0.41370165,-0.017250024,0.8962314,-0...</td>\n",
-       "      <td>-0.17937128,-0.30326533,-0.14656651,0.26193923...</td>\n",
-       "      <td>0.2421372,0.41370165,-0.017250024,0.8962314,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>Q100440056</td>\n",
-       "      <td>13959</td>\n",
-       "      <td>10346647</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Moldovans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>Moldovans is people</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.27328575,0.3186499,-0.13949376,1.0969052,-0....</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "      <td>0.27328575,0.3186499,-0.13949376,1.0969052,-0....</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100606074</td>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>5612</td>\n",
-       "      <td>10351613</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Hoosiers</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>Hoosiers is people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "      <td>-0.4371684,1.0444875,-0.63107914,0.07898032,-0...</td>\n",
-       "      <td>0.025020197,0.4693855,-0.50199574,1.3510414,-0...</td>\n",
-       "      <td>-0.4371684,1.0444875,-0.63107914,0.07898032,-0...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2       node1  Unnamed: 0     index    parent node1_label  \\\n",
-       "0  Q100400481  Q100355826        1123  10344291  Q2472587    Yukoners   \n",
-       "1  Q100448818  Q100196985        8456  10339627  Q2472587  Oregonians   \n",
-       "2  Q100588072  Q100568638        3032  11887935   Q846570      Utahns   \n",
-       "3  Q100606028  Q100440056       13959  10346647  Q2472587   Moldovans   \n",
-       "4  Q100606074  Q100606028        5612  10351613  Q2472587      Iowans   \n",
-       "\n",
-       "   par_label  node2_label            node1_sent                node2_sent  \\\n",
-       "0     people   Detroiters    Yukoners is people      Detroiters is people   \n",
-       "1     people     Kuwaitis  Oregonians is people        Kuwaitis is people   \n",
-       "2  Americans  Wyomingites   Utahns is Americans  Wyomingites is Americans   \n",
-       "3     people       Iowans   Moldovans is people          Iowans is people   \n",
-       "4     people     Hoosiers      Iowans is people        Hoosiers is people   \n",
-       "\n",
-       "   ... BERT_abstract_emb BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  ...               NaN                         NaN            NaN   \n",
-       "1  ...               NaN                         NaN            NaN   \n",
-       "2  ...               NaN                         NaN            NaN   \n",
-       "3  ...               NaN                         NaN            NaN   \n",
-       "4  ...               NaN                         NaN            NaN   \n",
-       "\n",
-       "   abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                       NaN                     NaN   \n",
-       "1                       NaN                     NaN   \n",
-       "2                       NaN                     NaN   \n",
-       "3                       NaN                     NaN   \n",
-       "4                       NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  -0.07126519,0.6527018,-0.3797528,0.19690958,0....   \n",
-       "1  -0.082290515,0.7254986,-0.14525111,0.3196717,0...   \n",
-       "2  -0.17937128,-0.30326533,-0.14656651,0.26193923...   \n",
-       "3  0.27328575,0.3186499,-0.13949376,1.0969052,-0....   \n",
-       "4  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  0.52179945,0.6418564,0.3750265,0.5378938,-0.77...   \n",
-       "1  -0.42398202,0.17036317,0.35879236,1.1121372,0....   \n",
-       "2  0.2421372,0.41370165,-0.017250024,0.8962314,-0...   \n",
-       "3  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "4  -0.4371684,1.0444875,-0.63107914,0.07898032,-0...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  -0.07126519,0.6527018,-0.3797528,0.19690958,0....   \n",
-       "1  -0.082290515,0.7254986,-0.14525111,0.3196717,0...   \n",
-       "2  -0.17937128,-0.30326533,-0.14656651,0.26193923...   \n",
-       "3  0.27328575,0.3186499,-0.13949376,1.0969052,-0....   \n",
-       "4  0.025020197,0.4693855,-0.50199574,1.3510414,-0...   \n",
-       "\n",
-       "                                textEmb_2props_right  \n",
-       "0  0.52179945,0.6418564,0.3750265,0.5378938,-0.77...  \n",
-       "1  -0.42398202,0.17036317,0.35879236,1.1121372,0....  \n",
-       "2  0.2421372,0.41370165,-0.017250024,0.8962314,-0...  \n",
-       "3  0.025020197,0.4693855,-0.50199574,1.3510414,-0...  \n",
-       "4  -0.4371684,1.0444875,-0.63107914,0.07898032,-0...  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "id": "sweet-garlic",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.drop(columns=['Unnamed: 0', 'index']).to_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmb.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "id": "imposed-dairy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.drop(columns=['Unnamed: 0', 'index']).to_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmb.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fitting-competition",
-   "metadata": {},
-   "source": [
-    "# Analyze 4 Metrics for Cosine Similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "id": "instrumental-bunny",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "id": "eastern-competition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmb.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmb.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "id": "graduate-nutrition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text['textEmb_7props_left'] = p279WordSimSeededDF_wabs_text['textEmb_7props_left'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279WordSimSeededDF_wabs_text['textEmb_7props_right'] = p279WordSimSeededDF_wabs_text['textEmb_7props_right'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279WordSimSeededDF_wabs_text['textEmb_2props_left'] = p279WordSimSeededDF_wabs_text['textEmb_2props_left'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279WordSimSeededDF_wabs_text['textEmb_2props_right'] = p279WordSimSeededDF_wabs_text['textEmb_2props_right'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "\n",
-    "p279WordSimSeededDF_wabs_text['textEmb_7props_cosSim'] = p279WordSimSeededDF_wabs_text.apply(lambda p: cosine_similarity(np.array(p['textEmb_7props_left']).reshape(1,-1), np.array(p['textEmb_7props_right']).reshape(1,-1)), axis=1)\n",
-    "p279WordSimSeededDF_wabs_text['textEmb_2props_cosSim'] = p279WordSimSeededDF_wabs_text.apply(lambda p: cosine_similarity(np.array(p['textEmb_2props_left']).reshape(1,-1), np.array(p['textEmb_2props_right']).reshape(1,-1)), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "injured-pleasure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_emb'] = p279WordSimSeededDF_wabs_text['BERT_abstract_emb'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_emb_right'] = p279WordSimSeededDF_wabs_text['BERT_abstract_emb_right'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_firstSent_emb'] = p279WordSimSeededDF_wabs_text['BERT_abstract_firstSent_emb'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_firstSent_emb_right'] = p279WordSimSeededDF_wabs_text['BERT_abstract_firstSent_emb_right'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "\n",
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_cosSim'] = p279WordSimSeededDF_wabs_text.apply(lambda p: cosine_similarity(np.array(p['BERT_abstract_emb']).reshape(1,-1), np.array(p['BERT_abstract_emb_right']).reshape(1,-1)) if type(p['BERT_abstract_emb']) == list and type(p['BERT_abstract_emb_right']) == list else -1, axis=1)\n",
-    "p279WordSimSeededDF_wabs_text['BERT_abstract_firstSent_cosSim'] = p279WordSimSeededDF_wabs_text.apply(lambda p: cosine_similarity(np.array(p['BERT_abstract_firstSent_emb']).reshape(1,-1), np.array(p['BERT_abstract_firstSent_emb_right']).reshape(1,-1)) if type(p['BERT_abstract_firstSent_emb']) == list and type(p['BERT_abstract_firstSent_emb_right']) == list else -1, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "binding-residence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['textEmb_7props_cosSim', 'textEmb_2props_cosSim', 'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim']:\n",
-    "    p279WordSimSeededDF_wabs_text[col] = p279WordSimSeededDF_wabs_text[col].apply(lambda p: p[0][0] if p != -1 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "ahead-prince",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 320,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "short-permit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_7props_left'] = p279Seeded_SiblingsDF3_wabs_text['textEmb_7props_left'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_7props_right'] = p279Seeded_SiblingsDF3_wabs_text['textEmb_7props_right'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_2props_left'] = p279Seeded_SiblingsDF3_wabs_text['textEmb_2props_left'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_2props_right'] = p279Seeded_SiblingsDF3_wabs_text['textEmb_2props_right'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "\n",
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_7props_cosSim'] = p279Seeded_SiblingsDF3_wabs_text.apply(lambda p: cosine_similarity(np.array(p['textEmb_7props_left']).reshape(1,-1), np.array(p['textEmb_7props_right']).reshape(1,-1)), axis=1)\n",
-    "p279Seeded_SiblingsDF3_wabs_text['textEmb_2props_cosSim'] = p279Seeded_SiblingsDF3_wabs_text.apply(lambda p: cosine_similarity(np.array(p['textEmb_2props_left']).reshape(1,-1), np.array(p['textEmb_2props_right']).reshape(1,-1)), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "charming-courtesy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_emb'] = p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_emb'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_emb_right'] = p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_emb_right'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_firstSent_emb'] = p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_firstSent_emb'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_firstSent_emb_right'] = p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_firstSent_emb_right'].apply(lambda p: [float(p1) for p1 in p[1:-1].split(',')] if not(pd.isna(p)) else p)\n",
-    "\n",
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_cosSim'] = p279Seeded_SiblingsDF3_wabs_text.apply(lambda p: cosine_similarity(np.array(p['BERT_abstract_emb']).reshape(1,-1), np.array(p['BERT_abstract_emb_right']).reshape(1,-1)) if type(p['BERT_abstract_emb']) == list and type(p['BERT_abstract_emb_right']) == list else -1, axis=1)\n",
-    "p279Seeded_SiblingsDF3_wabs_text['BERT_abstract_firstSent_cosSim'] = p279Seeded_SiblingsDF3_wabs_text.apply(lambda p: cosine_similarity(np.array(p['BERT_abstract_firstSent_emb']).reshape(1,-1), np.array(p['BERT_abstract_firstSent_emb_right']).reshape(1,-1)) if type(p['BERT_abstract_firstSent_emb']) == list and type(p['BERT_abstract_firstSent_emb_right']) == list else -1, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "following-bhutan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['textEmb_7props_cosSim', 'textEmb_2props_cosSim', 'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim']:\n",
-    "    p279Seeded_SiblingsDF3_wabs_text[col] = p279Seeded_SiblingsDF3_wabs_text[col].apply(lambda p: p[0][0] if p != -1 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "offensive-tamil",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100400481</td>\n",
-       "      <td>Q100355826</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Yukoners</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Detroiters</td>\n",
-       "      <td>Yukoners is people</td>\n",
-       "      <td>Detroiters is people</td>\n",
-       "      <td>people is typically Yukoners or Detroiters</td>\n",
-       "      <td>[ 3.88018750e-02  8.60690176e-01  4.90037464e-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.07126519, 0.6527018, -0.3797528, 0.1969095...</td>\n",
-       "      <td>[0.52179945, 0.6418564, 0.3750265, 0.5378938, ...</td>\n",
-       "      <td>[-0.07126519, 0.6527018, -0.3797528, 0.1969095...</td>\n",
-       "      <td>[0.52179945, 0.6418564, 0.3750265, 0.5378938, ...</td>\n",
-       "      <td>0.559523</td>\n",
-       "      <td>0.559523</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100448818</td>\n",
-       "      <td>Q100196985</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Oregonians</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Kuwaitis</td>\n",
-       "      <td>Oregonians is people</td>\n",
-       "      <td>Kuwaitis is people</td>\n",
-       "      <td>people is typically Oregonians or Kuwaitis</td>\n",
-       "      <td>[ 2.83955485e-01  4.97512490e-01  9.54957902e-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.082290515, 0.7254986, -0.14525111, 0.31967...</td>\n",
-       "      <td>[-0.42398202, 0.17036317, 0.35879236, 1.112137...</td>\n",
-       "      <td>[-0.082290515, 0.7254986, -0.14525111, 0.31967...</td>\n",
-       "      <td>[-0.42398202, 0.17036317, 0.35879236, 1.112137...</td>\n",
-       "      <td>0.598038</td>\n",
-       "      <td>0.598038</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100588072</td>\n",
-       "      <td>Q100568638</td>\n",
-       "      <td>Q846570</td>\n",
-       "      <td>Utahns</td>\n",
-       "      <td>Americans</td>\n",
-       "      <td>Wyomingites</td>\n",
-       "      <td>Utahns is Americans</td>\n",
-       "      <td>Wyomingites is Americans</td>\n",
-       "      <td>Americans is typically Utahns or Wyomingites</td>\n",
-       "      <td>[-5.01574516e-01  7.97144234e-01 -5.04231870e-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.17937128, -0.30326533, -0.14656651, 0.2619...</td>\n",
-       "      <td>[0.2421372, 0.41370165, -0.017250024, 0.896231...</td>\n",
-       "      <td>[-0.17937128, -0.30326533, -0.14656651, 0.2619...</td>\n",
-       "      <td>[0.2421372, 0.41370165, -0.017250024, 0.896231...</td>\n",
-       "      <td>0.825117</td>\n",
-       "      <td>0.825117</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>Q100440056</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Moldovans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>Moldovans is people</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>people is typically Moldovans or Iowans</td>\n",
-       "      <td>[ 2.46052667e-01 -3.61022986e-02  2.22438946e-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.27328575, 0.3186499, -0.13949376, 1.0969052...</td>\n",
-       "      <td>[0.025020197, 0.4693855, -0.50199574, 1.351041...</td>\n",
-       "      <td>[0.27328575, 0.3186499, -0.13949376, 1.0969052...</td>\n",
-       "      <td>[0.025020197, 0.4693855, -0.50199574, 1.351041...</td>\n",
-       "      <td>0.578932</td>\n",
-       "      <td>0.578932</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100606074</td>\n",
-       "      <td>Q100606028</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Iowans</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Hoosiers</td>\n",
-       "      <td>Iowans is people</td>\n",
-       "      <td>Hoosiers is people</td>\n",
-       "      <td>people is typically Iowans or Hoosiers</td>\n",
-       "      <td>[-2.31350675e-01  9.12515879e-01  2.31145900e-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.025020197, 0.4693855, -0.50199574, 1.351041...</td>\n",
-       "      <td>[-0.4371684, 1.0444875, -0.63107914, 0.0789803...</td>\n",
-       "      <td>[0.025020197, 0.4693855, -0.50199574, 1.351041...</td>\n",
-       "      <td>[-0.4371684, 1.0444875, -0.63107914, 0.0789803...</td>\n",
-       "      <td>0.767688</td>\n",
-       "      <td>0.767688</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2       node1    parent node1_label  par_label  node2_label  \\\n",
-       "0  Q100400481  Q100355826  Q2472587    Yukoners     people   Detroiters   \n",
-       "1  Q100448818  Q100196985  Q2472587  Oregonians     people     Kuwaitis   \n",
-       "2  Q100588072  Q100568638   Q846570      Utahns  Americans  Wyomingites   \n",
-       "3  Q100606028  Q100440056  Q2472587   Moldovans     people       Iowans   \n",
-       "4  Q100606074  Q100606028  Q2472587      Iowans     people     Hoosiers   \n",
-       "\n",
-       "             node1_sent                node2_sent  \\\n",
-       "0    Yukoners is people      Detroiters is people   \n",
-       "1  Oregonians is people        Kuwaitis is people   \n",
-       "2   Utahns is Americans  Wyomingites is Americans   \n",
-       "3   Moldovans is people          Iowans is people   \n",
-       "4      Iowans is people        Hoosiers is people   \n",
-       "\n",
-       "                                      comb_sent  \\\n",
-       "0    people is typically Yukoners or Detroiters   \n",
-       "1    people is typically Oregonians or Kuwaitis   \n",
-       "2  Americans is typically Utahns or Wyomingites   \n",
-       "3       people is typically Moldovans or Iowans   \n",
-       "4        people is typically Iowans or Hoosiers   \n",
-       "\n",
-       "                                           node1_emb  ...  \\\n",
-       "0  [ 3.88018750e-02  8.60690176e-01  4.90037464e-...  ...   \n",
-       "1  [ 2.83955485e-01  4.97512490e-01  9.54957902e-...  ...   \n",
-       "2  [-5.01574516e-01  7.97144234e-01 -5.04231870e-...  ...   \n",
-       "3  [ 2.46052667e-01 -3.61022986e-02  2.22438946e-...  ...   \n",
-       "4  [-2.31350675e-01  9.12515879e-01  2.31145900e-...  ...   \n",
-       "\n",
-       "  BERT_abstract_emb_right  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                     NaN                                NaN   \n",
-       "1                     NaN                                NaN   \n",
-       "2                     NaN                                NaN   \n",
-       "3                     NaN                                NaN   \n",
-       "4                     NaN                                NaN   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [-0.07126519, 0.6527018, -0.3797528, 0.1969095...   \n",
-       "1  [-0.082290515, 0.7254986, -0.14525111, 0.31967...   \n",
-       "2  [-0.17937128, -0.30326533, -0.14656651, 0.2619...   \n",
-       "3  [0.27328575, 0.3186499, -0.13949376, 1.0969052...   \n",
-       "4  [0.025020197, 0.4693855, -0.50199574, 1.351041...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.52179945, 0.6418564, 0.3750265, 0.5378938, ...   \n",
-       "1  [-0.42398202, 0.17036317, 0.35879236, 1.112137...   \n",
-       "2  [0.2421372, 0.41370165, -0.017250024, 0.896231...   \n",
-       "3  [0.025020197, 0.4693855, -0.50199574, 1.351041...   \n",
-       "4  [-0.4371684, 1.0444875, -0.63107914, 0.0789803...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [-0.07126519, 0.6527018, -0.3797528, 0.1969095...   \n",
-       "1  [-0.082290515, 0.7254986, -0.14525111, 0.31967...   \n",
-       "2  [-0.17937128, -0.30326533, -0.14656651, 0.2619...   \n",
-       "3  [0.27328575, 0.3186499, -0.13949376, 1.0969052...   \n",
-       "4  [0.025020197, 0.4693855, -0.50199574, 1.351041...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.52179945, 0.6418564, 0.3750265, 0.5378938, ...              0.559523   \n",
-       "1  [-0.42398202, 0.17036317, 0.35879236, 1.112137...              0.598038   \n",
-       "2  [0.2421372, 0.41370165, -0.017250024, 0.896231...              0.825117   \n",
-       "3  [0.025020197, 0.4693855, -0.50199574, 1.351041...              0.578932   \n",
-       "4  [-0.4371684, 1.0444875, -0.63107914, 0.0789803...              0.767688   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.559523                 -1.0                           -1.0  \n",
-       "1              0.598038                 -1.0                           -1.0  \n",
-       "2              0.825117                 -1.0                           -1.0  \n",
-       "3              0.578932                 -1.0                           -1.0  \n",
-       "4              0.767688                 -1.0                           -1.0  \n",
-       "\n",
-       "[5 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "boxed-amount",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Checkpoint 2\n",
-    "p279WordSimSeededDF_wabs_text.to_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\", index=False)\n",
-    "p279Seeded_SiblingsDF3_wabs_text.to_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\", index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "handled-wichita",
-   "metadata": {},
-   "source": [
-    "# SVM Training and regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "normal-tomato",
-   "metadata": {},
-   "source": [
-    "## ChildPar Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "id": "necessary-commander",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 370,
-   "id": "under-german",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 370,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 389,
-   "id": "surgical-synthesis",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>19454.000000</td>\n",
-       "      <td>19454.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.686685</td>\n",
-       "      <td>0.678516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.100472</td>\n",
-       "      <td>0.100452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.235755</td>\n",
-       "      <td>0.235755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.623664</td>\n",
-       "      <td>0.615677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.693018</td>\n",
-       "      <td>0.684735</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.755926</td>\n",
-       "      <td>0.748264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>0.965743</td>\n",
-       "      <td>0.964936</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       textEmb_7props_cosSim  textEmb_2props_cosSim\n",
-       "count           19454.000000           19454.000000\n",
-       "mean                0.686685               0.678516\n",
-       "std                 0.100472               0.100452\n",
-       "min                 0.235755               0.235755\n",
-       "25%                 0.623664               0.615677\n",
-       "50%                 0.693018               0.684735\n",
-       "75%                 0.755926               0.748264\n",
-       "max                 0.965743               0.964936"
-      ]
-     },
-     "execution_count": 389,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text[['textEmb_7props_cosSim', 'textEmb_2props_cosSim']].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 391,
-   "id": "incident-nevada",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>19454.000000</td>\n",
-       "      <td>19454.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>-0.145757</td>\n",
-       "      <td>-0.187870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.771780</td>\n",
-       "      <td>0.737520</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.366539</td>\n",
-       "      <td>0.261753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.555416</td>\n",
-       "      <td>0.477809</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>0.915390</td>\n",
-       "      <td>0.934461</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       BERT_abstract_cosSim  BERT_abstract_firstSent_cosSim\n",
-       "count          19454.000000                    19454.000000\n",
-       "mean              -0.145757                       -0.187870\n",
-       "std                0.771780                        0.737520\n",
-       "min               -1.000000                       -1.000000\n",
-       "25%               -1.000000                       -1.000000\n",
-       "50%                0.366539                        0.261753\n",
-       "75%                0.555416                        0.477809\n",
-       "max                0.915390                        0.934461"
-      ]
-     },
-     "execution_count": 391,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text[['BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim']].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 377,
-   "id": "welsh-affairs",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "from sklearn.utils.random import sample_without_replacement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 375,
-   "id": "suspended-auckland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 375,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.lineplot(data=p279WordSimSeededDF_wabs_text.sort_values(by=['bert2SentSim'])[:500][features+['bert2SentSim']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 386,
-   "id": "younger-roller",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 386,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.lineplot(data=p279WordSimSeededDF_wabs_text.sort_values(by=['bert2SentSim'])[features+['bert2SentSim']].iloc[sample_without_replacement(len(p279WordSimSeededDF_wabs_text), 500, random_state=13)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 397,
-   "id": "superb-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp123 = p279WordSimSeededDF_wabs_text[(~p279WordSimSeededDF_wabs_text.BERT_abstract_emb.isna()) & (~p279WordSimSeededDF_wabs_text.BERT_abstract_emb_right.isna())]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "id": "extreme-heath",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 406,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import pyplot\n",
-    "fig, ax = pyplot.subplots(figsize=(20,7))\n",
-    "sns.lineplot(data=temp123.sort_values(by=['bert2SentSim'])[features+['bert2SentSim']].iloc[sample_without_replacement(len(temp123), 500, random_state=13)])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "russian-broadcasting",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "superior-helicopter",
-   "metadata": {},
-   "source": [
-    "### Data Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "id": "specified-arena",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = ['textEmb_7props_cosSim', 'textEmb_2props_cosSim', 'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 392,
-   "id": "afraid-toolbox",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "X = p279WordSimSeededDF_wabs_text[features]\n",
-    "Y = p279WordSimSeededDF_wabs_text['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "id": "exposed-publicity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_SPLITS = 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "id": "valued-blanket",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "for train_index, test_index in skf.split(X, Y):\n",
-    "    X_train_splits.append(X.iloc[train_index])\n",
-    "    X_test_splits.append(X.iloc[test_index])\n",
-    "    Y_train_splits.append(Y.iloc[train_index])\n",
-    "    Y_test_splits.append(Y.iloc[test_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "id": "driving-basin",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.932637</td>\n",
-       "      <td>0.941683</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   textEmb_7props_cosSim  textEmb_2props_cosSim  BERT_abstract_cosSim  \\\n",
-       "0               0.832602               0.823419             -1.000000   \n",
-       "1               0.742638               0.734428             -1.000000   \n",
-       "2               0.647031               0.707611              0.508060   \n",
-       "4               0.765589               0.780342              0.752693   \n",
-       "5               0.932637               0.941683             -1.000000   \n",
-       "\n",
-       "   BERT_abstract_firstSent_cosSim  \n",
-       "0                       -1.000000  \n",
-       "1                       -1.000000  \n",
-       "2                        0.455747  \n",
-       "4                        0.515319  \n",
-       "5                       -1.000000  "
-      ]
-     },
-     "execution_count": 338,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_splits[0].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surgical-defensive",
-   "metadata": {},
-   "source": [
-    "### SVM Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "id": "initial-guest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "from sklearn.metrics import mean_squared_error, explained_variance_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "id": "flexible-steal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = []\n",
-    "for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "    clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "    clf.fit(X_train1, Y_train1)\n",
-    "    preds.append(clf.predict(X_test1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quarterly-season",
-   "metadata": {},
-   "source": [
-    "### Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "id": "expired-publication",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.016201295906940718"
-      ]
-     },
-     "execution_count": 345,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "acc = 0\n",
-    "for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "    acc += mean_squared_error(pred, Y_test1)\n",
-    "acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 351,
-   "id": "ruled-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indNames = ['Text Embedding - 7 props', 'Text Embedding - P279 + P31', 'DBPedia short abstracts', 'DBPedia short abstracts first sentence']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "static-vector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tempVals = []\n",
-    "for col in features:\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf1 = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "        clf1.fit(X_train1[[col]], Y_train1)\n",
-    "        preds.append(clf1.predict(X_test1[[col]]))\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += mean_squared_error(pred, Y_test1)\n",
-    "    tempVals.append(acc/N_SPLITS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "id": "controlled-czech",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSE compared with BERT 2 label emb score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>1.893109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>1.888821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>1.787131</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>1.774453</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        MSE compared with BERT 2 label emb score\n",
-       "Text Embedding - 7 props                                                1.893109\n",
-       "Text Embedding - P279 + P31                                             1.888821\n",
-       "DBPedia short abstracts                                                 1.787131\n",
-       "DBPedia short abstracts first sentence                                  1.774453"
-      ]
-     },
-     "execution_count": 353,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['MSE compared with BERT 2 label emb score']) * 100).sort_values(by=['MSE compared with BERT 2 label emb score'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "guilty-mobile",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "center-times",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "steady-fifth",
-   "metadata": {},
-   "source": [
-    "## Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "id": "molecular-bachelor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "id": "dressed-jordan",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.772184\n",
-       "std          0.122326\n",
-       "min          0.120480\n",
-       "25%          0.684141\n",
-       "50%          0.793718\n",
-       "75%          0.874284\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 371,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "american-dominant",
-   "metadata": {},
-   "source": [
-    "### Data Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "id": "short-seven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = ['textEmb_7props_cosSim', 'textEmb_2props_cosSim', 'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "id": "automotive-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "X = p279Seeded_SiblingsDF3_wabs_text[features]\n",
-    "Y = p279Seeded_SiblingsDF3_wabs_text['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "id": "partial-catalyst",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_SPLITS = 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "id": "popular-shelf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "for train_index, test_index in skf.split(X, Y):\n",
-    "    X_train_splits.append(X.iloc[train_index])\n",
-    "    X_test_splits.append(X.iloc[test_index])\n",
-    "    Y_train_splits.append(Y.iloc[train_index])\n",
-    "    Y_test_splits.append(Y.iloc[test_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "id": "pressed-passport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.559523</td>\n",
-       "      <td>0.559523</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.598038</td>\n",
-       "      <td>0.598038</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.825117</td>\n",
-       "      <td>0.825117</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.767688</td>\n",
-       "      <td>0.767688</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.679326</td>\n",
-       "      <td>0.679326</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   textEmb_7props_cosSim  textEmb_2props_cosSim  BERT_abstract_cosSim  \\\n",
-       "0               0.559523               0.559523                  -1.0   \n",
-       "1               0.598038               0.598038                  -1.0   \n",
-       "2               0.825117               0.825117                  -1.0   \n",
-       "4               0.767688               0.767688                  -1.0   \n",
-       "5               0.679326               0.679326                  -1.0   \n",
-       "\n",
-       "   BERT_abstract_firstSent_cosSim  \n",
-       "0                            -1.0  \n",
-       "1                            -1.0  \n",
-       "2                            -1.0  \n",
-       "4                            -1.0  \n",
-       "5                            -1.0  "
-      ]
-     },
-     "execution_count": 359,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_splits[0].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "above-relevance",
-   "metadata": {},
-   "source": [
-    "### SVM Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "id": "greek-occasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "from sklearn.metrics import mean_squared_error, explained_variance_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "id": "vertical-sweden",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = []\n",
-    "for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "    clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "    clf.fit(X_train1, Y_train1)\n",
-    "    preds.append(clf.predict(X_test1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "present-secretariat",
-   "metadata": {},
-   "source": [
-    "### Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 362,
-   "id": "hundred-authentication",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.012189405746669981"
-      ]
-     },
-     "execution_count": 362,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "acc = 0\n",
-    "for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "    acc += mean_squared_error(pred, Y_test1)\n",
-    "acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "id": "posted-chamber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indNames = ['Text Embedding - 7 props', 'Text Embedding - P279 + P31', 'DBPedia short abstracts', 'DBPedia short abstracts first sentence']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "id": "processed-costs",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tempVals = []\n",
-    "for col in features:\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf1 = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "        clf1.fit(X_train1[[col]], Y_train1)\n",
-    "        preds.append(clf1.predict(X_test1[[col]]))\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += mean_squared_error(pred, Y_test1)\n",
-    "    tempVals.append(acc/N_SPLITS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 365,
-   "id": "african-greek",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSE compared with BERT 2 label emb score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>1.449413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>1.440694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>1.283479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>1.268741</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        MSE compared with BERT 2 label emb score\n",
-       "DBPedia short abstracts first sentence                                  1.449413\n",
-       "DBPedia short abstracts                                                 1.440694\n",
-       "Text Embedding - P279 + P31                                             1.283479\n",
-       "Text Embedding - 7 props                                                1.268741"
-      ]
-     },
-     "execution_count": 365,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['MSE compared with BERT 2 label emb score']) * 100).sort_values(by=['MSE compared with BERT 2 label emb score'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "exact-navigation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
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-   "display_name": "kgtkEnv",
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-   "name": "kgtkenv"
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-    "left": "10px",
-    "top": "150px",
-    "width": "399px"
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-   "toc_window_display": true
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-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/P279-Dataset Generation.ipynb b/P279-Dataset Generation.ipynb
deleted file mode 100644
index 687f0a3..0000000
--- a/P279-Dataset Generation.ipynb	
+++ /dev/null
@@ -1,9377 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "large-steering",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "threatened-pacific",
-   "metadata": {},
-   "source": [
-    "# Extract P279s from the wikidataos file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "associate-mixture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz -p \";P279;\" -o ../data/P279_dataset/wikidata-P279.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "silent-liver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "damaged-driving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv('../data/P279_dataset/wikidata-P279.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "pleasant-wheel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "721983"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "alpine-induction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DFNew = p279DF\n",
-    "# p279DFNew = p279DF[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "liable-australian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100000030-P279-Q14748-30394205-0</td>\n",
-       "      <td>Q100000030</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q14748</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100000058-P279-Q1622444-bd182663-0</td>\n",
-       "      <td>Q100000058</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1622444</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1000032-P279-Q1813494-0aa0f1dc-0</td>\n",
-       "      <td>Q1000032</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1813494</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1000032-P279-Q83602-482a1943-0</td>\n",
-       "      <td>Q1000032</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q83602</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1000039-P279-Q11555767-2dddfd86-0</td>\n",
-       "      <td>Q1000039</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11555767</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721978</th>\n",
-       "      <td>Q999981-P279-Q2990593-dbee3bf9-0</td>\n",
-       "      <td>Q999981</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q2990593</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721979</th>\n",
-       "      <td>Q99998337-P279-Q203920-0c6fcf31-0</td>\n",
-       "      <td>Q99998337</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q203920</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721980</th>\n",
-       "      <td>Q99998385-P279-Q203920-7ed228b8-0</td>\n",
-       "      <td>Q99998385</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q203920</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721981</th>\n",
-       "      <td>Q99998517-P279-Q4918149-39e16ef0-0</td>\n",
-       "      <td>Q99998517</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q4918149</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721982</th>\n",
-       "      <td>Q99999421-P279-Q622679-e690cdab-0</td>\n",
-       "      <td>Q99999421</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q622679</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>721983 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         id       node1 label      node2  \\\n",
-       "0         Q100000030-P279-Q14748-30394205-0  Q100000030  P279     Q14748   \n",
-       "1       Q100000058-P279-Q1622444-bd182663-0  Q100000058  P279   Q1622444   \n",
-       "2         Q1000032-P279-Q1813494-0aa0f1dc-0    Q1000032  P279   Q1813494   \n",
-       "3           Q1000032-P279-Q83602-482a1943-0    Q1000032  P279     Q83602   \n",
-       "4        Q1000039-P279-Q11555767-2dddfd86-0    Q1000039  P279  Q11555767   \n",
-       "...                                     ...         ...   ...        ...   \n",
-       "721978     Q999981-P279-Q2990593-dbee3bf9-0     Q999981  P279   Q2990593   \n",
-       "721979    Q99998337-P279-Q203920-0c6fcf31-0   Q99998337  P279    Q203920   \n",
-       "721980    Q99998385-P279-Q203920-7ed228b8-0   Q99998385  P279    Q203920   \n",
-       "721981   Q99998517-P279-Q4918149-39e16ef0-0   Q99998517  P279   Q4918149   \n",
-       "721982    Q99999421-P279-Q622679-e690cdab-0   Q99999421  P279    Q622679   \n",
-       "\n",
-       "          rank node2;wikidatatype  \n",
-       "0       normal      wikibase-item  \n",
-       "1       normal      wikibase-item  \n",
-       "2       normal      wikibase-item  \n",
-       "3       normal      wikibase-item  \n",
-       "4       normal      wikibase-item  \n",
-       "...        ...                ...  \n",
-       "721978  normal      wikibase-item  \n",
-       "721979  normal      wikibase-item  \n",
-       "721980  normal      wikibase-item  \n",
-       "721981  normal      wikibase-item  \n",
-       "721982  normal      wikibase-item  \n",
-       "\n",
-       "[721983 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279DFNew"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "convinced-chicago",
-   "metadata": {},
-   "source": [
-    "## Join with labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "prompt-attendance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "finite-shape",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = p279DFNew.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fitted-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "      <th>id_label</th>\n",
-       "      <th>label_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q100000030</th>\n",
-       "      <td>Q100000030-P279-Q14748-30394205-0</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q14748</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q100000058</th>\n",
-       "      <td>Q100000058-P279-Q1622444-bd182663-0</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1622444</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "      <td>Q100000058-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'tsunami stone'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1000032</th>\n",
-       "      <td>Q1000032-P279-Q1813494-0aa0f1dc-0</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1813494</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "      <td>Q1000032-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'unschooling'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1000032</th>\n",
-       "      <td>Q1000032-P279-Q83602-482a1943-0</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q83602</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "      <td>Q1000032-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'unschooling'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1000039</th>\n",
-       "      <td>Q1000039-P279-Q11555767-2dddfd86-0</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q11555767</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "      <td>Q1000039-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'Akita ranga'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                             id label      node2    rank  \\\n",
-       "node1                                                                      \n",
-       "Q100000030    Q100000030-P279-Q14748-30394205-0  P279     Q14748  normal   \n",
-       "Q100000058  Q100000058-P279-Q1622444-bd182663-0  P279   Q1622444  normal   \n",
-       "Q1000032      Q1000032-P279-Q1813494-0aa0f1dc-0  P279   Q1813494  normal   \n",
-       "Q1000032        Q1000032-P279-Q83602-482a1943-0  P279     Q83602  normal   \n",
-       "Q1000039     Q1000039-P279-Q11555767-2dddfd86-0  P279  Q11555767  normal   \n",
-       "\n",
-       "           node2;wikidatatype             id_label label_label  \\\n",
-       "node1                                                            \n",
-       "Q100000030      wikibase-item                  NaN         NaN   \n",
-       "Q100000058      wikibase-item  Q100000058-label-en       label   \n",
-       "Q1000032        wikibase-item    Q1000032-label-en       label   \n",
-       "Q1000032        wikibase-item    Q1000032-label-en       label   \n",
-       "Q1000039        wikibase-item    Q1000039-label-en       label   \n",
-       "\n",
-       "                   node2_label  \n",
-       "node1                           \n",
-       "Q100000030                 NaN  \n",
-       "Q100000058  'tsunami stone'@en  \n",
-       "Q1000032      'unschooling'@en  \n",
-       "Q1000032      'unschooling'@en  \n",
-       "Q1000039      'Akita ranga'@en  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "imposed-request",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = temp1[['id','label','node2','node2_label']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "valuable-canal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "thermal-citation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "competitive-symposium",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "surface-concept",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3 = temp3[~temp3['node1_label'].isna()]\n",
-    "temp3 = temp3[~temp3['node2_label'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "muslim-creator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3['node2_label'].isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "organizational-disaster",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "medium-thunder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "disturbed-adaptation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721978</th>\n",
-       "      <td>Q2259388-P279-Q999897-01a551b4-0</td>\n",
-       "      <td>Q2259388</td>\n",
-       "      <td>Ergon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q999897</td>\n",
-       "      <td>middle management</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721979</th>\n",
-       "      <td>Q1294114-P279-Q999981-55b2910f-0</td>\n",
-       "      <td>Q1294114</td>\n",
-       "      <td>skin care</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q999981</td>\n",
-       "      <td>personal grooming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721980</th>\n",
-       "      <td>Q48996240-P279-Q999981-d9e5c4dd-0</td>\n",
-       "      <td>Q48996240</td>\n",
-       "      <td>preening in birds</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q999981</td>\n",
-       "      <td>personal grooming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721981</th>\n",
-       "      <td>Q56400028-P279-Q999981-7b184d82-0</td>\n",
-       "      <td>Q56400028</td>\n",
-       "      <td>haircut</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q999981</td>\n",
-       "      <td>personal grooming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>721982</th>\n",
-       "      <td>Q4918149-P279-Q99998517-04f0b672-0</td>\n",
-       "      <td>Q4918149</td>\n",
-       "      <td>Bisht</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99998517</td>\n",
-       "      <td>Ahsa\\'i Bisht</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>620830 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        id      node1  \\\n",
-       "1        Q17372279-P279-Q100026-beba8cd1-0  Q17372279   \n",
-       "2        Q17372377-P279-Q100026-fd42bd71-0  Q17372377   \n",
-       "3        Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444   \n",
-       "4        Q17372455-P279-Q100026-c2b1bf36-0  Q17372455   \n",
-       "5         Q2029940-P279-Q100026-ceba4380-0   Q2029940   \n",
-       "...                                    ...        ...   \n",
-       "721978    Q2259388-P279-Q999897-01a551b4-0   Q2259388   \n",
-       "721979    Q1294114-P279-Q999981-55b2910f-0   Q1294114   \n",
-       "721980   Q48996240-P279-Q999981-d9e5c4dd-0  Q48996240   \n",
-       "721981   Q56400028-P279-Q999981-7b184d82-0  Q56400028   \n",
-       "721982  Q4918149-P279-Q99998517-04f0b672-0   Q4918149   \n",
-       "\n",
-       "                    node1_label label      node2           node2_label  \n",
-       "1                         YF-16  P279    Q100026  F-16 Fighting Falcon  \n",
-       "2       F-16A/B Fighting Falcon  P279    Q100026  F-16 Fighting Falcon  \n",
-       "3       F-16C/D Fighting Falcon  P279    Q100026  F-16 Fighting Falcon  \n",
-       "4         F-16E/F Desert Falcon  P279    Q100026  F-16 Fighting Falcon  \n",
-       "5                    F-16 VISTA  P279    Q100026  F-16 Fighting Falcon  \n",
-       "...                         ...   ...        ...                   ...  \n",
-       "721978                    Ergon  P279    Q999897     middle management  \n",
-       "721979                skin care  P279    Q999981     personal grooming  \n",
-       "721980        preening in birds  P279    Q999981     personal grooming  \n",
-       "721981                  haircut  P279    Q999981     personal grooming  \n",
-       "721982                    Bisht  P279  Q99998517         Ahsa\\'i Bisht  \n",
-       "\n",
-       "[620830 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "color-breathing",
-   "metadata": {},
-   "source": [
-    "## Create child-parent sentences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "minus-biology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3['sent'] = temp3.apply(lambda p: p['node1_label'] + ' is ' + p['node2_label'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "homeless-combination",
-   "metadata": {},
-   "source": [
-    "## Form pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "authorized-eleven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "determined-option",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF.to_csv('../data/P279_dataset/hierDF.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "latin-mills",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100052791-P279-Q100052938-71a0f2ca-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052938</td>\n",
-       "      <td>fictional jaguar</td>\n",
-       "      <td>nthropomorphic ja is fictional jaguar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100004761-P279-Q100095237-3971e1cd-0</td>\n",
-       "      <td>Q100004761</td>\n",
-       "      <td>helf l</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100095237</td>\n",
-       "      <td>shop shelve</td>\n",
-       "      <td>helf l is shop shelve</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>992</th>\n",
-       "      <td>Q100165468-P279-Q99839432-a6842485-0</td>\n",
-       "      <td>Q100165468</td>\n",
-       "      <td>nthropomorphic axo</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99839432</td>\n",
-       "      <td>fictional axolotl</td>\n",
-       "      <td>nthropomorphic axo is fictional axolotl</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>993</th>\n",
-       "      <td>Q100269055-P279-Q99913543-6a466631-0</td>\n",
-       "      <td>Q100269055</td>\n",
-       "      <td>oe</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99913543</td>\n",
-       "      <td>toe cap</td>\n",
-       "      <td>oe  is toe cap</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>994</th>\n",
-       "      <td>Q100145976-P279-Q99948390-4f0644a4-0</td>\n",
-       "      <td>Q100145976</td>\n",
-       "      <td>pen</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99948390</td>\n",
-       "      <td>toe</td>\n",
-       "      <td>pen is toe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>Q100145990-P279-Q99948390-fe46cfdd-0</td>\n",
-       "      <td>Q100145990</td>\n",
-       "      <td>eep</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99948390</td>\n",
-       "      <td>toe</td>\n",
-       "      <td>eep is toe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>Q100148318-P279-Q99971015-9011d906-0</td>\n",
-       "      <td>Q100148318</td>\n",
-       "      <td>nthropomorphic</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q99971015</td>\n",
-       "      <td>anthropomorphic cow or other cattle</td>\n",
-       "      <td>nthropomorphic  is anthropomorphic cow or othe...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>879 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        id       node1         node1_label  \\\n",
-       "0    Q100052562-P279-Q100052008-1a4fa432-0  Q100052562  nthropomorphic pan   \n",
-       "1    Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "2    Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "3    Q100052791-P279-Q100052938-71a0f2ca-0  Q100052791   nthropomorphic ja   \n",
-       "4    Q100004761-P279-Q100095237-3971e1cd-0  Q100004761              helf l   \n",
-       "..                                     ...         ...                 ...   \n",
-       "992   Q100165468-P279-Q99839432-a6842485-0  Q100165468  nthropomorphic axo   \n",
-       "993   Q100269055-P279-Q99913543-6a466631-0  Q100269055                 oe    \n",
-       "994   Q100145976-P279-Q99948390-4f0644a4-0  Q100145976                 pen   \n",
-       "995   Q100145990-P279-Q99948390-fe46cfdd-0  Q100145990                 eep   \n",
-       "996   Q100148318-P279-Q99971015-9011d906-0  Q100148318     nthropomorphic    \n",
-       "\n",
-       "    label       node2                          node2_label  \\\n",
-       "0    P279  Q100052008          anthropomorphic Pantherinae   \n",
-       "1    P279  Q100052008          anthropomorphic Pantherinae   \n",
-       "2    P279  Q100052008          anthropomorphic Pantherinae   \n",
-       "3    P279  Q100052938                     fictional jaguar   \n",
-       "4    P279  Q100095237                          shop shelve   \n",
-       "..    ...         ...                                  ...   \n",
-       "992  P279   Q99839432                    fictional axolotl   \n",
-       "993  P279   Q99913543                              toe cap   \n",
-       "994  P279   Q99948390                                  toe   \n",
-       "995  P279   Q99948390                                  toe   \n",
-       "996  P279   Q99971015  anthropomorphic cow or other cattle   \n",
-       "\n",
-       "                                                  sent  \n",
-       "0    nthropomorphic pan is anthropomorphic Pantherinae  \n",
-       "1     nthropomorphic ja is anthropomorphic Pantherinae  \n",
-       "2      nthropomorphic t is anthropomorphic Pantherinae  \n",
-       "3                nthropomorphic ja is fictional jaguar  \n",
-       "4                                helf l is shop shelve  \n",
-       "..                                                 ...  \n",
-       "992            nthropomorphic axo is fictional axolotl  \n",
-       "993                                     oe  is toe cap  \n",
-       "994                                         pen is toe  \n",
-       "995                                         eep is toe  \n",
-       "996  nthropomorphic  is anthropomorphic cow or othe...  \n",
-       "\n",
-       "[879 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "specific-atlantic",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairLeftDF = temp3.copy()\n",
-    "pairRightDF = temp3.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "upper-majority",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF = pairLeftDF.set_index('node2').join(pairRightDF.set_index('node2'),rsuffix='_right').reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "owned-liberal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
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-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
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-       "      <th>node2_label_right</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                     id       node1  \\\n",
-       "0  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "2  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "3  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "4  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "\n",
-       "          node1_label label                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                                sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                id_right node1_right   node1_label_right  \\\n",
-       "0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "3  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562  nthropomorphic pan   \n",
-       "4  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "\n",
-       "  label_right            node2_label_right  \\\n",
-       "0        P279  anthropomorphic Pantherinae   \n",
-       "1        P279  anthropomorphic Pantherinae   \n",
-       "2        P279  anthropomorphic Pantherinae   \n",
-       "3        P279  anthropomorphic Pantherinae   \n",
-       "4        P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          sent_right  \n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae  \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae  \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae  \n",
-       "3  nthropomorphic pan is anthropomorphic Pantherinae  \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae  "
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "coral-haven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF['set_nodes'] = pairsDF.apply(lambda p: str(sorted([p.node1, p.node1_right]) + [p.node2]), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "descending-industry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF1 = pairsDF[~pairsDF['set_nodes'].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "spectacular-treasurer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5979 statements get reduced to 3429 statements after checking for duplicate pairs\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{len(pairsDF)} statements get reduced to {len(pairsDF1)} statements after checking for duplicate pairs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "several-microphone",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100052562', 'Q100052008']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100052791', 'Q100052008']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100119502', 'Q100052008']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052791', 'Q100052791', 'Q100052008']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052791', 'Q100119502', 'Q100052008']</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                     id       node1  \\\n",
-       "0  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "2  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "4  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "5  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "\n",
-       "          node1_label label                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "5   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                                sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                id_right node1_right   node1_label_right  \\\n",
-       "0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "4  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "5  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "\n",
-       "  label_right            node2_label_right  \\\n",
-       "0        P279  anthropomorphic Pantherinae   \n",
-       "1        P279  anthropomorphic Pantherinae   \n",
-       "2        P279  anthropomorphic Pantherinae   \n",
-       "4        P279  anthropomorphic Pantherinae   \n",
-       "5        P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          sent_right  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                    set_nodes  \n",
-       "0  ['Q100052562', 'Q100052562', 'Q100052008']  \n",
-       "1  ['Q100052562', 'Q100052791', 'Q100052008']  \n",
-       "2  ['Q100052562', 'Q100119502', 'Q100052008']  \n",
-       "4  ['Q100052791', 'Q100052791', 'Q100052008']  \n",
-       "5  ['Q100052791', 'Q100119502', 'Q100052008']  "
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "hollywood-transmission",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-98-1646efbabaaf>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  pairsDF1['comb_sent'] = pairsDF1.apply(lambda p: p.node2_label + ' such as ' + p.node1_label + ' and ' + p.node1_label_right, axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "pairsDF1['comb_sent'] = pairsDF1.apply(lambda p: p.node2_label + ' such as ' + p.node1_label + ' and ' + p.node1_label_right, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "leading-official",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100052562', 'Q100052008']</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100052791', 'Q100052008']</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052562', 'Q100119502', 'Q100052008']</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052791', 'Q100052791', 'Q100052008']</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>['Q100052791', 'Q100119502', 'Q100052008']</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                     id       node1  \\\n",
-       "0  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "2  Q100052008  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "4  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "5  Q100052008  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "\n",
-       "          node1_label label                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan  P279  anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "5   nthropomorphic ja  P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                                sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                id_right node1_right   node1_label_right  \\\n",
-       "0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "4  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   nthropomorphic ja   \n",
-       "5  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502    nthropomorphic t   \n",
-       "\n",
-       "  label_right            node2_label_right  \\\n",
-       "0        P279  anthropomorphic Pantherinae   \n",
-       "1        P279  anthropomorphic Pantherinae   \n",
-       "2        P279  anthropomorphic Pantherinae   \n",
-       "4        P279  anthropomorphic Pantherinae   \n",
-       "5        P279  anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          sent_right  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                    set_nodes  \\\n",
-       "0  ['Q100052562', 'Q100052562', 'Q100052008']   \n",
-       "1  ['Q100052562', 'Q100052791', 'Q100052008']   \n",
-       "2  ['Q100052562', 'Q100119502', 'Q100052008']   \n",
-       "4  ['Q100052791', 'Q100052791', 'Q100052008']   \n",
-       "5  ['Q100052791', 'Q100119502', 'Q100052008']   \n",
-       "\n",
-       "                                           comb_sent  \n",
-       "0  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "1  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "2  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "4  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "5  anthropomorphic Pantherinae such as nthropomor...  "
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "stretch-jordan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF2 = pairsDF1[['node1', 'node2', 'node1_right', 'node1_label', 'node2_label', 'node1_label_right', 'sent', 'sent_right', 'comb_sent']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "southwest-police",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3 = pairsDF2.rename(columns={'node1_right': 'node2', 'node2': 'parent', 'node1_label_right': 'node2_label', 'node2_label': 'par_label', 'sent': 'node1_sent', 'sent_right': 'node2_sent'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "extra-navigator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1      parent       node2         node1_label  \\\n",
-       "0  Q100052562  Q100052008  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052562  Q100052008  Q100052791  nthropomorphic pan   \n",
-       "2  Q100052562  Q100052008  Q100119502  nthropomorphic pan   \n",
-       "4  Q100052791  Q100052008  Q100052791   nthropomorphic ja   \n",
-       "5  Q100052791  Q100052008  Q100119502   nthropomorphic ja   \n",
-       "\n",
-       "                     par_label         node2_label  \\\n",
-       "0  anthropomorphic Pantherinae  nthropomorphic pan   \n",
-       "1  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "2  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "4  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "5  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "\n",
-       "                                          node1_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          node2_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "5    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                           comb_sent  \n",
-       "0  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "1  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "2  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "4  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "5  anthropomorphic Pantherinae such as nthropomor...  "
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "quick-culture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF.to_csv(\"../data/P279_dataset/pairsDF.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "independent-latin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3.to_csv(\"../data/P279_dataset/pairsDF3.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mounted-meter",
-   "metadata": {},
-   "source": [
-    "# Similarity Scores Construction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "classical-power",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "basic-tolerance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efficient-albania",
-   "metadata": {},
-   "source": [
-    "## BERT Child Parent Embeddings - Labels based"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "severe-tulsa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF = pd.read_csv('../data/P279_dataset/hierDF.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "daily-shaft",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1 = hierDF[~hierDF.node1_label.isna()].reset_index().drop(columns=['index'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "surgical-ebony",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 0)"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1.node1_label.isna().sum(), hierDF1.node2_label.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "reported-moore",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.47850561141967773 s\n",
-      "0.40022730827331543 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "hierDF1['node1_emb'] = pd.Series(list(getSentEmbeddings(hierDF1.node1_label, 'bert-base-nli-mean-tokens')))\n",
-    "hierDF1['node2_emb'] = pd.Series(list(getSentEmbeddings(hierDF1.node2_label, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "enabling-stephen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1['bertChildParSim'] = hierDF1.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "essential-munich",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>gptChildParPpl</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.172506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.318752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.184794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100052791-P279-Q100052938-71a0f2ca-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052938</td>\n",
-       "      <td>fictional jaguar</td>\n",
-       "      <td>nthropomorphic ja is fictional jaguar</td>\n",
-       "      <td>2.516336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100004761-P279-Q100095237-3971e1cd-0</td>\n",
-       "      <td>Q100004761</td>\n",
-       "      <td>helf l</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100095237</td>\n",
-       "      <td>shop shelve</td>\n",
-       "      <td>helf l is shop shelve</td>\n",
-       "      <td>3.221734</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                                     id       node1  \\\n",
-       "0           0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1           1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "2           2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502   \n",
-       "3           3  Q100052791-P279-Q100052938-71a0f2ca-0  Q100052791   \n",
-       "4           4  Q100004761-P279-Q100095237-3971e1cd-0  Q100004761   \n",
-       "\n",
-       "          node1_label label       node2                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja  P279  Q100052938             fictional jaguar   \n",
-       "4              helf l  P279  Q100095237                  shop shelve   \n",
-       "\n",
-       "                                                sent  gptChildParPpl  \n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae        2.172506  \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae        2.318752  \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae        2.184794  \n",
-       "3              nthropomorphic ja is fictional jaguar        2.516336  \n",
-       "4                              helf l is shop shelve        3.221734  "
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "sacred-democracy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "839"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(hierDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "surface-vermont",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    839.000000\n",
-       "mean       0.586407\n",
-       "std        0.147204\n",
-       "min        0.060429\n",
-       "25%        0.496083\n",
-       "50%        0.607622\n",
-       "75%        0.688008\n",
-       "max        0.952739\n",
-       "Name: bertChildParSim, dtype: float64"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1['bertChildParSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "assumed-australian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1.to_csv('../data/P279_dataset/hierDF1_simScores.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "through-watch",
-   "metadata": {},
-   "source": [
-    "## BERT 2 Sent Embeddings (Siblings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "sharing-russell",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3 = pd.read_csv(\"../data/P279_dataset/pairsDF3.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "considered-producer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1      parent       node2         node1_label  \\\n",
-       "0  Q100052562  Q100052008  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052562  Q100052008  Q100052791  nthropomorphic pan   \n",
-       "2  Q100052562  Q100052008  Q100119502  nthropomorphic pan   \n",
-       "3  Q100052791  Q100052008  Q100052791   nthropomorphic ja   \n",
-       "4  Q100052791  Q100052008  Q100119502   nthropomorphic ja   \n",
-       "\n",
-       "                     par_label         node2_label  \\\n",
-       "0  anthropomorphic Pantherinae  nthropomorphic pan   \n",
-       "1  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "2  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "3  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "4  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "\n",
-       "                                          node1_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          node2_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "4    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                           comb_sent  \n",
-       "0  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "1  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "2  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "3  anthropomorphic Pantherinae such as nthropomor...  \n",
-       "4  anthropomorphic Pantherinae such as nthropomor...  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "laden-houston",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.6303539276123047 s\n",
-      "1.4965293407440186 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "pairsDF3['node1_emb'] = pd.Series(list(getSentEmbeddings(pairsDF3.node1_sent, 'bert-base-nli-mean-tokens')))\n",
-    "pairsDF3['node2_emb'] = pd.Series(list(getSentEmbeddings(pairsDF3.node2_sent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "major-summit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3['bert2SentSim'] = pairsDF3.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "informed-queensland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3.to_csv(\"../data/P279_dataset/pairsDF3_simScores.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "published-smile",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.9541891813278198, 0.9541892)"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.iloc[86]['bert2SentSim'], cosine_similarity(pairsDF3.iloc[86].node1_emb.reshape(1,-1), pairsDF3.iloc[86].node2_emb.reshape(1,-1))[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "stylish-grammar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3429"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(pairsDF3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "strong-appointment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3429.000000\n",
-       "mean        0.944740\n",
-       "std         0.063054\n",
-       "min         0.384220\n",
-       "25%         0.912423\n",
-       "50%         0.964450\n",
-       "75%         1.000000\n",
-       "max         1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "charming-reverse",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sustained-sally",
-   "metadata": {},
-   "source": [
-    "## GPT2 Child Parent perplexity computation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "marked-hometown",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "genuine-missile",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# build gpt model\n",
-    "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
-    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
-    "\n",
-    "def score(sent1):\n",
-    "    # input sentence tensor\n",
-    "    # output score\n",
-    "    tokens_tensor = tokenizer.encode(sent1, add_special_tokens=False, return_tensors=\"pt\")\n",
-    "    loss = model(tokens_tensor, labels=tokens_tensor)[0]\n",
-    "#     print(tokens_tensor.size(1))\n",
-    "    return np.exp(loss.cpu().detach().numpy() / tokens_tensor.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "composite-conference",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1 = hierDF[~hierDF.node1_label.isna()].reset_index().drop(columns=['index'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "unsigned-stress",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100052791-P279-Q100052938-71a0f2ca-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052938</td>\n",
-       "      <td>fictional jaguar</td>\n",
-       "      <td>nthropomorphic ja is fictional jaguar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100004761-P279-Q100095237-3971e1cd-0</td>\n",
-       "      <td>Q100004761</td>\n",
-       "      <td>helf l</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100095237</td>\n",
-       "      <td>shop shelve</td>\n",
-       "      <td>helf l is shop shelve</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                                     id       node1  \\\n",
-       "0           0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1           1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "2           2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502   \n",
-       "3           3  Q100052791-P279-Q100052938-71a0f2ca-0  Q100052791   \n",
-       "4           4  Q100004761-P279-Q100095237-3971e1cd-0  Q100004761   \n",
-       "\n",
-       "          node1_label label       node2                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja  P279  Q100052938             fictional jaguar   \n",
-       "4              helf l  P279  Q100095237                  shop shelve   \n",
-       "\n",
-       "                                                sent  \n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae  \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae  \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae  \n",
-       "3              nthropomorphic ja is fictional jaguar  \n",
-       "4                              helf l is shop shelve  "
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "magnetic-collect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1['gptChildParPpl'] = hierDF1.sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "running-cable",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>gptChildParPpl</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Q100052562-P279-Q100052008-1a4fa432-0</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.172506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q100052791-P279-Q100052008-1d15b1ae-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.318752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q100119502-P279-Q100052008-d2c330a4-0</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>2.184794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q100052791-P279-Q100052938-71a0f2ca-0</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100052938</td>\n",
-       "      <td>fictional jaguar</td>\n",
-       "      <td>nthropomorphic ja is fictional jaguar</td>\n",
-       "      <td>2.516336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q100004761-P279-Q100095237-3971e1cd-0</td>\n",
-       "      <td>Q100004761</td>\n",
-       "      <td>helf l</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100095237</td>\n",
-       "      <td>shop shelve</td>\n",
-       "      <td>helf l is shop shelve</td>\n",
-       "      <td>3.221734</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                                     id       node1  \\\n",
-       "0           0  Q100052562-P279-Q100052008-1a4fa432-0  Q100052562   \n",
-       "1           1  Q100052791-P279-Q100052008-1d15b1ae-0  Q100052791   \n",
-       "2           2  Q100119502-P279-Q100052008-d2c330a4-0  Q100119502   \n",
-       "3           3  Q100052791-P279-Q100052938-71a0f2ca-0  Q100052791   \n",
-       "4           4  Q100004761-P279-Q100095237-3971e1cd-0  Q100004761   \n",
-       "\n",
-       "          node1_label label       node2                  node2_label  \\\n",
-       "0  nthropomorphic pan  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t  P279  Q100052008  anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja  P279  Q100052938             fictional jaguar   \n",
-       "4              helf l  P279  Q100095237                  shop shelve   \n",
-       "\n",
-       "                                                sent  gptChildParPpl  \n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae        2.172506  \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae        2.318752  \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae        2.184794  \n",
-       "3              nthropomorphic ja is fictional jaguar        2.516336  \n",
-       "4                              helf l is shop shelve        3.221734  "
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "familiar-marks",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    839.000000\n",
-       "mean       4.615804\n",
-       "std        3.825699\n",
-       "min        1.111450\n",
-       "25%        2.006814\n",
-       "50%        3.344577\n",
-       "75%        5.765571\n",
-       "max       29.560613\n",
-       "Name: gptChildParPpl, dtype: float64"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierDF1['gptChildParPpl'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "loaded-archive",
-   "metadata": {},
-   "source": [
-    "## GPT2 2 Sent Perplexity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "split-johnson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "      <td>[-0.38264108, 0.3698052, -0.452593, -0.0875713...</td>\n",
-       "      <td>[-0.38264108, 0.3698052, -0.452593, -0.0875713...</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "      <td>[-0.38264108, 0.3698052, -0.452593, -0.0875713...</td>\n",
-       "      <td>[-0.38223487, 0.35599083, -0.44246477, -0.1346...</td>\n",
-       "      <td>0.980127</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q100052562</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic pan</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic pan is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "      <td>[-0.38264108, 0.3698052, -0.452593, -0.0875713...</td>\n",
-       "      <td>[-0.31828552, 0.2023329, -0.23166429, 0.037530...</td>\n",
-       "      <td>0.980426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "      <td>[-0.382235, 0.35599056, -0.44246483, -0.134666...</td>\n",
-       "      <td>[-0.38223487, 0.35599083, -0.44246477, -0.1346...</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q100052791</td>\n",
-       "      <td>Q100052008</td>\n",
-       "      <td>Q100119502</td>\n",
-       "      <td>nthropomorphic ja</td>\n",
-       "      <td>anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t</td>\n",
-       "      <td>nthropomorphic ja is anthropomorphic Pantherinae</td>\n",
-       "      <td>nthropomorphic t is anthropomorphic Pantherinae</td>\n",
-       "      <td>anthropomorphic Pantherinae such as nthropomor...</td>\n",
-       "      <td>[-0.38223487, 0.35599083, -0.44246477, -0.1346...</td>\n",
-       "      <td>[-0.31828552, 0.2023329, -0.23166429, 0.037530...</td>\n",
-       "      <td>0.990308</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node1      parent       node2         node1_label  \\\n",
-       "0  Q100052562  Q100052008  Q100052562  nthropomorphic pan   \n",
-       "1  Q100052562  Q100052008  Q100052791  nthropomorphic pan   \n",
-       "2  Q100052562  Q100052008  Q100119502  nthropomorphic pan   \n",
-       "3  Q100052791  Q100052008  Q100052791   nthropomorphic ja   \n",
-       "4  Q100052791  Q100052008  Q100119502   nthropomorphic ja   \n",
-       "\n",
-       "                     par_label         node2_label  \\\n",
-       "0  anthropomorphic Pantherinae  nthropomorphic pan   \n",
-       "1  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "2  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "3  anthropomorphic Pantherinae   nthropomorphic ja   \n",
-       "4  anthropomorphic Pantherinae    nthropomorphic t   \n",
-       "\n",
-       "                                          node1_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "2  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "4   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                          node2_sent  \\\n",
-       "0  nthropomorphic pan is anthropomorphic Pantherinae   \n",
-       "1   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "2    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "3   nthropomorphic ja is anthropomorphic Pantherinae   \n",
-       "4    nthropomorphic t is anthropomorphic Pantherinae   \n",
-       "\n",
-       "                                           comb_sent  \\\n",
-       "0  anthropomorphic Pantherinae such as nthropomor...   \n",
-       "1  anthropomorphic Pantherinae such as nthropomor...   \n",
-       "2  anthropomorphic Pantherinae such as nthropomor...   \n",
-       "3  anthropomorphic Pantherinae such as nthropomor...   \n",
-       "4  anthropomorphic Pantherinae such as nthropomor...   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-0.38264108, 0.3698052, -0.452593, -0.0875713...   \n",
-       "1  [-0.38264108, 0.3698052, -0.452593, -0.0875713...   \n",
-       "2  [-0.38264108, 0.3698052, -0.452593, -0.0875713...   \n",
-       "3  [-0.382235, 0.35599056, -0.44246483, -0.134666...   \n",
-       "4  [-0.38223487, 0.35599083, -0.44246477, -0.1346...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  \n",
-       "0  [-0.38264108, 0.3698052, -0.452593, -0.0875713...      1.000000  \n",
-       "1  [-0.38223487, 0.35599083, -0.44246477, -0.1346...      0.980127  \n",
-       "2  [-0.31828552, 0.2023329, -0.23166429, 0.037530...      0.980426  \n",
-       "3  [-0.38223487, 0.35599083, -0.44246477, -0.1346...      1.000000  \n",
-       "4  [-0.31828552, 0.2023329, -0.23166429, 0.037530...      0.990308  "
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "sorted-phone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3['gpt2SentPpl'] = pairsDF3.comb_sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "working-istanbul",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pairsDF3.to_csv(\"../data/P279_dataset/pairsDF3_simScores.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "incorporate-england",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3429.000000\n",
-       "mean        1.564343\n",
-       "std         0.443964\n",
-       "min         1.058665\n",
-       "25%         1.226182\n",
-       "50%         1.289757\n",
-       "75%         1.894221\n",
-       "max         3.545120\n",
-       "Name: gpt2SentPpl, dtype: float64"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairsDF3['gpt2SentPpl'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "fifth-constitution",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierDF1.to_csv('../data/P279_dataset/hierDF1_simScores.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "synthetic-toddler",
-   "metadata": {},
-   "source": [
-    "# Wordsim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "exposed-absence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "reported-tribe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279DF = pd.read_csv('../data/P279_dataset/wikidata-P279.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "sharp-brake",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "721983"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "obvious-distinction",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-68-2225f7afafbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtemp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp279DFNew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelsDF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_label'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtemp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node2_label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtemp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelsDF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_label2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mtemp3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'node2_label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'node2_label'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'node1_label'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'node2'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'node2_label'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'index'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtemp3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node1_label'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'node2_label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m   8105\u001b[0m         \u001b[0;36m5\u001b[0m  \u001b[0mK5\u001b[0m  \u001b[0mA5\u001b[0m  \u001b[0mNaN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8106\u001b[0m         \"\"\"\n\u001b[0;32m-> 8107\u001b[0;31m         return self._join_compat(\n\u001b[0m\u001b[1;32m   8108\u001b[0m             \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8109\u001b[0m         )\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_join_compat\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m   8130\u001b[0m                     \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8131\u001b[0m                 )\n\u001b[0;32m-> 8132\u001b[0;31m             return merge(\n\u001b[0m\u001b[1;32m   8133\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8134\u001b[0m                 \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     )\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    682\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_indicator_pre_merge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 684\u001b[0;31m         \u001b[0mjoin_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_join_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         llabels, rlabels = _items_overlap_with_suffix(\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_get_join_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft_index\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_index\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"asof\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m             join_index, left_indexer, right_indexer = left_ax.join(\n\u001b[0m\u001b[1;32m    897\u001b[0m                 \u001b[0mright_ax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_indexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m             )\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, how, level, return_indexers, sort)\u001b[0m\n\u001b[1;32m   3681\u001b[0m                 )\n\u001b[1;32m   3682\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3683\u001b[0;31m                 return self._join_non_unique(\n\u001b[0m\u001b[1;32m   3684\u001b[0m                     \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_indexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3685\u001b[0m                 )\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_join_non_unique\u001b[0;34m(self, other, how, return_indexers)\u001b[0m\n\u001b[1;32m   3811\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_engine_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3813\u001b[0;31m         left_idx, right_idx = get_join_indexers(\n\u001b[0m\u001b[1;32m   3814\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3815\u001b[0m         )\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mget_join_indexers\u001b[0;34m(left_keys, right_keys, sort, how, **kwargs)\u001b[0m\n\u001b[1;32m   1425\u001b[0m     \u001b[0;31m# set(lkey) | set(rkey) == range(count)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1427\u001b[0;31m     \u001b[0mlkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_factorize_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1428\u001b[0m     \u001b[0;31m# preserve left frame order if how == 'left' and sort == False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m     \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_factorize_keys\u001b[0;34m(lk, rk, sort, how)\u001b[0m\n\u001b[1;32m   2062\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2063\u001b[0m         \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2064\u001b[0;31m         \u001b[0mllab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrlab\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sort_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrlab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2066\u001b[0m     \u001b[0;31m# NA group\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_sort_labels\u001b[0;34m(uniques, left, right)\u001b[0m\n\u001b[1;32m   2087\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2088\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2089\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msafe_sort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2090\u001b[0m     \u001b[0mnew_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_int64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2091\u001b[0m     \u001b[0mnew_left\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_right\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mllength\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mllength\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36msafe_sort\u001b[0;34m(values, codes, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0;31m# take_1d is faster, but only works for na_sentinels of -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2158\u001b[0m         \u001b[0morder2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2159\u001b[0;31m         \u001b[0mnew_codes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtake_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2160\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2161\u001b[0m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mtake_nd\u001b[0;34m(arr, indexer, axis, out, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m   1757\u001b[0m         \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1758\u001b[0m     )\n\u001b[0;32m-> 1759\u001b[0;31m     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mflip_order\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "p279DFNew = p279DF\n",
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')\n",
-    "temp1 = p279DFNew.set_index('node1').join(labelsDF.set_index('node1'), rsuffix='_label')\n",
-    "temp1 = temp1[['id','label','node2','node2_label']]\n",
-    "temp2 = temp1.reset_index().set_index('node2').join(labelsDF.set_index('node1'), rsuffix='_label2')\n",
-    "temp3 = temp2[['id','node1','label','node2', 'node2_label']].rename(columns={'node2_label': 'node1_label', 'node2':'node2_label'}).reset_index().rename(columns={'index':'node2'})\n",
-    "temp3 = temp3[['id','node1','node1_label','label','node2','node2_label']]\n",
-    "temp3 = temp3[~temp3['node1_label'].isna()]\n",
-    "temp3 = temp3[~temp3['node2_label'].isna()]\n",
-    "temp3['node2_label'].isna().sum()\n",
-    "temp3['node1_label'] = temp3['node1_label'].apply(lambda p: p[1:-4])\n",
-    "temp3['node2_label'] = temp3['node2_label'].apply(lambda p: p[1:-4])\n",
-    "temp3['sent'] = temp3.apply(lambda p: p['node1_label'] + ' is ' + p['node2_label'], axis=1)\n",
-    "temp3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "convinced-longitude",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget http://alfonseca.org/pubs/ws353simrel.tar.gz -o ../data/ws353simrel.tar.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "royal-wedding",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wordsim353_agreed.txt\t   wordsim_relatedness_goldstandard.txt\r\n",
-      "wordsim353_annotator1.txt  wordsim_similarity_goldstandard.txt\r\n",
-      "wordsim353_annotator2.txt\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls ../data/wordsim353_sim_rel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "acknowledged-travel",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tiger\tcat\t7.35\r\n",
-      "tiger\ttiger\t10.00\r\n",
-      "plane\tcar\t5.77\r\n",
-      "train\tcar\t6.31\r\n",
-      "television\tradio\t6.77\r\n",
-      "media\tradio\t7.42\r\n",
-      "bread\tbutter\t6.19\r\n",
-      "cucumber\tpotato\t5.92\r\n",
-      "doctor\tnurse\t7.00\r\n",
-      "professor\tdoctor\t6.62\r\n",
-      "student\tprofessor\t6.81\r\n",
-      "smart\tstupid\t5.81\r\n",
-      "wood\tforest\t7.73\r\n",
-      "money\tcash\t9.15\r\n",
-      "king\tqueen\t8.58\r\n",
-      "king\trook\t5.92\r\n",
-      "bishop\trabbi\t6.69\r\n",
-      "fuck\tsex\t9.44\r\n",
-      "football\tsoccer\t9.03\r\n",
-      "football\tbasketball\t6.81\r\n",
-      "football\ttennis\t6.63\r\n",
-      "Arafat\tJackson\t2.50\r\n",
-      "physics\tchemistry\t7.35\r\n",
-      "vodka\tgin\t8.46\r\n",
-      "vodka\tbrandy\t8.13\r\n",
-      "drink\teat\t6.87\r\n",
-      "car\tautomobile\t8.94\r\n",
-      "gem\tjewel\t8.96\r\n",
-      "journey\tvoyage\t9.29\r\n",
-      "boy\tlad\t8.83\r\n",
-      "coast\tshore\t9.10\r\n",
-      "asylum\tmadhouse\t8.87\r\n",
-      "magician\twizard\t9.02\r\n",
-      "midday\tnoon\t9.29\r\n",
-      "furnace\tstove\t8.79\r\n",
-      "food\tfruit\t7.52\r\n",
-      "bird\tcock\t7.10\r\n",
-      "bird\tcrane\t7.38\r\n",
-      "food\trooster\t4.42\r\n",
-      "money\tdollar\t8.42\r\n",
-      "money\tcurrency\t9.04\r\n",
-      "tiger\tjaguar\t8.00\r\n",
-      "tiger\tfeline\t8.00\r\n",
-      "tiger\tcarnivore\t7.08\r\n",
-      "tiger\tmammal\t6.85\r\n",
-      "tiger\tanimal\t7.00\r\n",
-      "tiger\torganism\t4.77\r\n",
-      "tiger\tfauna\t5.62\r\n",
-      "psychology\tpsychiatry\t8.08\r\n",
-      "psychology\tscience\t6.71\r\n",
-      "psychology\tdiscipline\t5.58\r\n",
-      "planet\tstar\t8.45\r\n",
-      "planet\tmoon\t8.08\r\n",
-      "planet\tsun\t8.02\r\n",
-      "precedent\texample\t5.85\r\n",
-      "precedent\tantecedent\t6.04\r\n",
-      "cup\ttableware\t6.85\r\n",
-      "cup\tartifact\t2.92\r\n",
-      "cup\tobject\t3.69\r\n",
-      "cup\tentity\t2.15\r\n",
-      "jaguar\tcat\t7.42\r\n",
-      "jaguar\tcar\t7.27\r\n",
-      "mile\tkilometer\t8.66\r\n",
-      "skin\teye\t6.22\r\n",
-      "Japanese\tAmerican\t6.50\r\n",
-      "century\tyear\t7.59\r\n",
-      "announcement\tnews\t7.56\r\n",
-      "doctor\tpersonnel\t5.00\r\n",
-      "Harvard\tYale\t8.13\r\n",
-      "hospital\tinfrastructure\t4.63\r\n",
-      "life\tdeath\t7.88\r\n",
-      "travel\tactivity\t5.00\r\n",
-      "type\tkind\t8.97\r\n",
-      "street\tplace\t6.44\r\n",
-      "street\tavenue\t8.88\r\n",
-      "street\tblock\t6.88\r\n",
-      "cell\tphone\t7.81\r\n",
-      "dividend\tpayment\t7.63\r\n",
-      "calculation\tcomputation\t8.44\r\n",
-      "profit\tloss\t7.63\r\n",
-      "dollar\tyen\t7.78\r\n",
-      "dollar\tbuck\t9.22\r\n",
-      "phone\tequipment\t7.13\r\n",
-      "liquid\twater\t7.89\r\n",
-      "marathon\tsprint\t7.47\r\n",
-      "seafood\tfood\t8.34\r\n",
-      "seafood\tlobster\t8.70\r\n",
-      "lobster\tfood\t7.81\r\n",
-      "lobster\twine\t5.70\r\n",
-      "championship\ttournament\t8.36\r\n",
-      "man\twoman\t8.30\r\n",
-      "man\tgovernor\t5.25\r\n",
-      "murder\tmanslaughter\t8.53\r\n",
-      "opera\tperformance\t6.88\r\n",
-      "Mexico\tBrazil\t7.44\r\n",
-      "glass\tmetal\t5.56\r\n",
-      "aluminum\tmetal\t7.83\r\n",
-      "rock\tjazz\t7.59\r\n",
-      "museum\ttheater\t7.19\r\n",
-      "shower\tthunderstorm\t6.31\r\n",
-      "monk\toracle\t5.00\r\n",
-      "cup\tfood\t5.00\r\n",
-      "journal\tassociation\t4.97\r\n",
-      "street\tchildren\t4.94\r\n",
-      "car\tflight\t4.94\r\n",
-      "space\tchemistry\t4.88\r\n",
-      "situation\tconclusion\t4.81\r\n",
-      "word\tsimilarity\t4.75\r\n",
-      "peace\tplan\t4.75\r\n",
-      "consumer\tenergy\t4.75\r\n",
-      "ministry\tculture\t4.69\r\n",
-      "smart\tstudent\t4.62\r\n",
-      "investigation\teffort\t4.59\r\n",
-      "image\tsurface\t4.56\r\n",
-      "life\tterm\t4.50\r\n",
-      "start\tmatch\t4.47\r\n",
-      "computer\tnews\t4.47\r\n",
-      "board\trecommendation\t4.47\r\n",
-      "lad\tbrother\t4.46\r\n",
-      "observation\tarchitecture\t4.38\r\n",
-      "coast\thill\t4.38\r\n",
-      "deployment\tdeparture\t4.25\r\n",
-      "benchmark\tindex\t4.25\r\n",
-      "attempt\tpeace\t4.25\r\n",
-      "consumer\tconfidence\t4.13\r\n",
-      "start\tyear\t4.06\r\n",
-      "focus\tlife\t4.06\r\n",
-      "development\tissue\t3.97\r\n",
-      "theater\thistory\t3.91\r\n",
-      "situation\tisolation\t3.88\r\n",
-      "profit\twarning\t3.88\r\n",
-      "media\ttrading\t3.88\r\n",
-      "chance\tcredibility\t3.88\r\n",
-      "precedent\tinformation\t3.85\r\n",
-      "architecture\tcentury\t3.78\r\n",
-      "population\tdevelopment\t3.75\r\n",
-      "stock\tlive\t3.73\r\n",
-      "peace\tatmosphere\t3.69\r\n",
-      "morality\tmarriage\t3.69\r\n",
-      "minority\tpeace\t3.69\r\n",
-      "atmosphere\tlandscape\t3.69\r\n",
-      "report\tgain\t3.63\r\n",
-      "music\tproject\t3.63\r\n",
-      "seven\tseries\t3.56\r\n",
-      "experience\tmusic\t3.47\r\n",
-      "school\tcenter\t3.44\r\n",
-      "five\tmonth\t3.38\r\n",
-      "announcement\tproduction\t3.38\r\n",
-      "morality\timportance\t3.31\r\n",
-      "money\toperation\t3.31\r\n",
-      "delay\tnews\t3.31\r\n",
-      "governor\tinterview\t3.25\r\n",
-      "practice\tinstitution\t3.19\r\n",
-      "century\tnation\t3.16\r\n",
-      "coast\tforest\t3.15\r\n",
-      "shore\twoodland\t3.08\r\n",
-      "drink\tcar\t3.04\r\n",
-      "president\tmedal\t3.00\r\n",
-      "prejudice\trecognition\t3.00\r\n",
-      "viewer\tserial\t2.97\r\n",
-      "peace\tinsurance\t2.94\r\n",
-      "Mars\twater\t2.94\r\n",
-      "media\tgain\t2.88\r\n",
-      "precedent\tcognition\t2.81\r\n",
-      "announcement\teffort\t2.75\r\n",
-      "line\tinsurance\t2.69\r\n",
-      "crane\timplement\t2.69\r\n",
-      "drink\tmother\t2.65\r\n",
-      "opera\tindustry\t2.63\r\n",
-      "volunteer\tmotto\t2.56\r\n",
-      "listing\tproximity\t2.56\r\n",
-      "precedent\tcollection\t2.50\r\n",
-      "cup\tarticle\t2.40\r\n",
-      "sign\trecess\t2.38\r\n",
-      "problem\tairport\t2.38\r\n",
-      "reason\thypertension\t2.31\r\n",
-      "direction\tcombination\t2.25\r\n",
-      "Wednesday\tnews\t2.22\r\n",
-      "glass\tmagician\t2.08\r\n",
-      "cemetery\twoodland\t2.08\r\n",
-      "possibility\tgirl\t1.94\r\n",
-      "cup\tsubstance\t1.92\r\n",
-      "forest\tgraveyard\t1.85\r\n",
-      "stock\tegg\t1.81\r\n",
-      "month\thotel\t1.81\r\n",
-      "energy\tsecretary\t1.81\r\n",
-      "precedent\tgroup\t1.77\r\n",
-      "production\thike\t1.75\r\n",
-      "stock\tphone\t1.62\r\n",
-      "holy\tsex\t1.62\r\n",
-      "stock\tCD\t1.31\r\n",
-      "drink\tear\t1.31\r\n",
-      "delay\tracism\t1.19\r\n",
-      "stock\tlife\t0.92\r\n",
-      "stock\tjaguar\t0.92\r\n",
-      "monk\tslave\t0.92\r\n",
-      "lad\twizard\t0.92\r\n",
-      "sugar\tapproach\t0.88\r\n",
-      "rooster\tvoyage\t0.62\r\n",
-      "noon\tstring\t0.54\r\n",
-      "chord\tsmile\t0.54\r\n",
-      "professor\tcucumber\t0.31\r\n",
-      "king\tcabbage\t0.23\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "cat ../data/wordsim353_sim_rel/wordsim_similarity_goldstandard.txt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "treated-memory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF = pd.read_csv('../data/wordsim353_sim_rel/wordsim_similarity_goldstandard.txt', sep='\\t', header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "intimate-despite",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF.columns=['word1', 'word2', 'sim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "genuine-shareware",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1</th>\n",
-       "      <th>word2</th>\n",
-       "      <th>sim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tiger</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>7.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>tiger</td>\n",
-       "      <td>tiger</td>\n",
-       "      <td>10.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>plane</td>\n",
-       "      <td>car</td>\n",
-       "      <td>5.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>train</td>\n",
-       "      <td>car</td>\n",
-       "      <td>6.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>television</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>6.77</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        word1  word2    sim\n",
-       "0       tiger    cat   7.35\n",
-       "1       tiger  tiger  10.00\n",
-       "2       plane    car   5.77\n",
-       "3       train    car   6.31\n",
-       "4  television  radio   6.77"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "addressed-consideration",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "indie-madonna",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>YF-16 is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16A/B Fighting Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16C/D Fighting Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16E/F Desert Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16 VISTA is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  id      node1              node1_label  \\\n",
-       "1  Q17372279-P279-Q100026-beba8cd1-0  Q17372279                    YF-16   \n",
-       "2  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  F-16A/B Fighting Falcon   \n",
-       "3  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  F-16C/D Fighting Falcon   \n",
-       "4  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455    F-16E/F Desert Falcon   \n",
-       "5   Q2029940-P279-Q100026-ceba4380-0   Q2029940               F-16 VISTA   \n",
-       "\n",
-       "  label    node2           node2_label  \\\n",
-       "1  P279  Q100026  F-16 Fighting Falcon   \n",
-       "2  P279  Q100026  F-16 Fighting Falcon   \n",
-       "3  P279  Q100026  F-16 Fighting Falcon   \n",
-       "4  P279  Q100026  F-16 Fighting Falcon   \n",
-       "5  P279  Q100026  F-16 Fighting Falcon   \n",
-       "\n",
-       "                                              sent  \n",
-       "1                    YF-16 is F-16 Fighting Falcon  \n",
-       "2  F-16A/B Fighting Falcon is F-16 Fighting Falcon  \n",
-       "3  F-16C/D Fighting Falcon is F-16 Fighting Falcon  \n",
-       "4    F-16E/F Desert Falcon is F-16 Fighting Falcon  \n",
-       "5               F-16 VISTA is F-16 Fighting Falcon  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "threatened-invasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp3[temp3.node1_label.apply(lambda p: 'tiger' in p)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "affected-protest",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "corrected-harbor",
-   "metadata": {},
-   "source": [
-    "## Extracting wordsim words from wikidata P279 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "wrong-generation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimWordPairsSet = set(wordSimDF.apply(lambda p: (p.word1, p.word2), axis=1).tolist())\n",
-    "wordSimWordsSet = set(wordSimDF.word1.tolist() + wordSimDF.word2.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "responsible-couple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimed = temp3[temp3.apply(lambda p: (p.node1_label, p.node2_label) in wordSimWordPairsSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "productive-contractor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>225669</th>\n",
-       "      <td>Q61509-P279-Q1914636-522de7b1-0</td>\n",
-       "      <td>Q61509</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1914636</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>travel is activity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253542</th>\n",
-       "      <td>Q192935-P279-Q2095-c9aba5ef-0</td>\n",
-       "      <td>Q192935</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>food</td>\n",
-       "      <td>seafood is food</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480116</th>\n",
-       "      <td>Q93352-P279-Q468756-18da9376-0</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>coast is shore</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     id    node1 node1_label label     node2  \\\n",
-       "225669  Q61509-P279-Q1914636-522de7b1-0   Q61509      travel  P279  Q1914636   \n",
-       "253542    Q192935-P279-Q2095-c9aba5ef-0  Q192935     seafood  P279     Q2095   \n",
-       "480116   Q93352-P279-Q468756-18da9376-0   Q93352       coast  P279   Q468756   \n",
-       "\n",
-       "       node2_label                sent  \n",
-       "225669    activity  travel is activity  \n",
-       "253542        food     seafood is food  \n",
-       "480116       shore      coast is shore  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3wordsimed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "manual-logistics",
-   "metadata": {},
-   "source": [
-    "## Extracting wordsim words from wikidata P279 siblings dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "polyphonic-receipt",
-   "metadata": {},
-   "source": [
-    "### Generate new P279 siblings dataset from wordsim words subset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "atmospheric-pacific",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279wordsimed = temp3[temp3.node1_label.apply(lambda p: p in wordSimWordsSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "optimum-palestine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "699"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(P279wordsimed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "mediterranean-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairLeftDF = P279wordsimed.copy()\n",
-    "newPairRightDF = P279wordsimed.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "amazing-resource",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF = newPairLeftDF.set_index('node2').join(newPairRightDF.set_index('node2'),rsuffix='_right').reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "nominated-senate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100257358</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                    id      node1 node1_label  \\\n",
-       "0  Q100257358  Q15209696-P279-Q100257358-c4b60f40-0  Q15209696      profit   \n",
-       "1    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "2    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "3    Q1002697       Q49850-P279-Q1002697-0c52cf68-0     Q49850     journal   \n",
-       "4    Q1002697       Q49850-P279-Q1002697-0c52cf68-0     Q49850     journal   \n",
-       "\n",
-       "  label node2_label                   sent  \\\n",
-       "0  P279       lucre        profit is lucre   \n",
-       "1  P279  periodical    issue is periodical   \n",
-       "2  P279  periodical    issue is periodical   \n",
-       "3  P279  periodical  journal is periodical   \n",
-       "4  P279  periodical  journal is periodical   \n",
-       "\n",
-       "                               id_right node1_right node1_label_right  \\\n",
-       "0  Q15209696-P279-Q100257358-c4b60f40-0   Q15209696            profit   \n",
-       "1    Q28869365-P279-Q1002697-ef7c4062-0   Q28869365             issue   \n",
-       "2       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "3    Q28869365-P279-Q1002697-ef7c4062-0   Q28869365             issue   \n",
-       "4       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "\n",
-       "  label_right node2_label_right             sent_right  \n",
-       "0        P279             lucre        profit is lucre  \n",
-       "1        P279        periodical    issue is periodical  \n",
-       "2        P279        periodical  journal is periodical  \n",
-       "3        P279        periodical    issue is periodical  \n",
-       "4        P279        periodical  journal is periodical  "
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "german-falls",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF['set_nodes'] = newPairsDF.apply(lambda p: str(sorted([p.node1, p.node1_right]) + [p.node2]), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "obvious-compatibility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF1 = newPairsDF[~newPairsDF['set_nodes'].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "logical-techno",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1143 statements get reduced to 916 statements after checking for duplicate pairs\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{len(newPairsDF)} statements get reduced to {len(newPairsDF1)} statements after checking for duplicate pairs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "bound-consumer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
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-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100257358</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>['Q15209696', 'Q15209696', 'Q100257358']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>['Q28869365', 'Q28869365', 'Q1002697']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q28869365', 'Q49850', 'Q1002697']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q49850', 'Q49850', 'Q1002697']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q101604684</td>\n",
-       "      <td>Q12562111-P279-Q101604684-bde28046-0</td>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>series</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lithodemic unit</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>Q12562111-P279-Q101604684-bde28046-0</td>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>series</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lithodemic unit</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>['Q12562111', 'Q12562111', 'Q101604684']</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                    id      node1 node1_label  \\\n",
-       "0  Q100257358  Q15209696-P279-Q100257358-c4b60f40-0  Q15209696      profit   \n",
-       "1    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "2    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "4    Q1002697       Q49850-P279-Q1002697-0c52cf68-0     Q49850     journal   \n",
-       "5  Q101604684  Q12562111-P279-Q101604684-bde28046-0  Q12562111      series   \n",
-       "\n",
-       "  label      node2_label                       sent  \\\n",
-       "0  P279            lucre            profit is lucre   \n",
-       "1  P279       periodical        issue is periodical   \n",
-       "2  P279       periodical        issue is periodical   \n",
-       "4  P279       periodical      journal is periodical   \n",
-       "5  P279  lithodemic unit  series is lithodemic unit   \n",
-       "\n",
-       "                               id_right node1_right node1_label_right  \\\n",
-       "0  Q15209696-P279-Q100257358-c4b60f40-0   Q15209696            profit   \n",
-       "1    Q28869365-P279-Q1002697-ef7c4062-0   Q28869365             issue   \n",
-       "2       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "4       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "5  Q12562111-P279-Q101604684-bde28046-0   Q12562111            series   \n",
-       "\n",
-       "  label_right node2_label_right                 sent_right  \\\n",
-       "0        P279             lucre            profit is lucre   \n",
-       "1        P279        periodical        issue is periodical   \n",
-       "2        P279        periodical      journal is periodical   \n",
-       "4        P279        periodical      journal is periodical   \n",
-       "5        P279   lithodemic unit  series is lithodemic unit   \n",
-       "\n",
-       "                                  set_nodes  \n",
-       "0  ['Q15209696', 'Q15209696', 'Q100257358']  \n",
-       "1    ['Q28869365', 'Q28869365', 'Q1002697']  \n",
-       "2       ['Q28869365', 'Q49850', 'Q1002697']  \n",
-       "4          ['Q49850', 'Q49850', 'Q1002697']  \n",
-       "5  ['Q12562111', 'Q12562111', 'Q101604684']  "
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "together-cause",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-62-727272aca744>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node2_label + ' such as ' + p.node1_label + ' and ' + p.node1_label_right, axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node2_label + ' such as ' + p.node1_label + ' and ' + p.node1_label_right, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "incident-amber",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100257358</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>Q15209696-P279-Q100257358-c4b60f40-0</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>['Q15209696', 'Q15209696', 'Q100257358']</td>\n",
-       "      <td>lucre such as profit and profit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>['Q28869365', 'Q28869365', 'Q1002697']</td>\n",
-       "      <td>periodical such as issue and issue</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365-P279-Q1002697-ef7c4062-0</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q28869365', 'Q49850', 'Q1002697']</td>\n",
-       "      <td>periodical such as issue and journal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q49850', 'Q49850', 'Q1002697']</td>\n",
-       "      <td>periodical such as journal and journal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q101604684</td>\n",
-       "      <td>Q12562111-P279-Q101604684-bde28046-0</td>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>series</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lithodemic unit</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>Q12562111-P279-Q101604684-bde28046-0</td>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>series</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>lithodemic unit</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>['Q12562111', 'Q12562111', 'Q101604684']</td>\n",
-       "      <td>lithodemic unit such as series and series</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                    id      node1 node1_label  \\\n",
-       "0  Q100257358  Q15209696-P279-Q100257358-c4b60f40-0  Q15209696      profit   \n",
-       "1    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "2    Q1002697    Q28869365-P279-Q1002697-ef7c4062-0  Q28869365       issue   \n",
-       "4    Q1002697       Q49850-P279-Q1002697-0c52cf68-0     Q49850     journal   \n",
-       "5  Q101604684  Q12562111-P279-Q101604684-bde28046-0  Q12562111      series   \n",
-       "\n",
-       "  label      node2_label                       sent  \\\n",
-       "0  P279            lucre            profit is lucre   \n",
-       "1  P279       periodical        issue is periodical   \n",
-       "2  P279       periodical        issue is periodical   \n",
-       "4  P279       periodical      journal is periodical   \n",
-       "5  P279  lithodemic unit  series is lithodemic unit   \n",
-       "\n",
-       "                               id_right node1_right node1_label_right  \\\n",
-       "0  Q15209696-P279-Q100257358-c4b60f40-0   Q15209696            profit   \n",
-       "1    Q28869365-P279-Q1002697-ef7c4062-0   Q28869365             issue   \n",
-       "2       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "4       Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "5  Q12562111-P279-Q101604684-bde28046-0   Q12562111            series   \n",
-       "\n",
-       "  label_right node2_label_right                 sent_right  \\\n",
-       "0        P279             lucre            profit is lucre   \n",
-       "1        P279        periodical        issue is periodical   \n",
-       "2        P279        periodical      journal is periodical   \n",
-       "4        P279        periodical      journal is periodical   \n",
-       "5        P279   lithodemic unit  series is lithodemic unit   \n",
-       "\n",
-       "                                  set_nodes  \\\n",
-       "0  ['Q15209696', 'Q15209696', 'Q100257358']   \n",
-       "1    ['Q28869365', 'Q28869365', 'Q1002697']   \n",
-       "2       ['Q28869365', 'Q49850', 'Q1002697']   \n",
-       "4          ['Q49850', 'Q49850', 'Q1002697']   \n",
-       "5  ['Q12562111', 'Q12562111', 'Q101604684']   \n",
-       "\n",
-       "                                   comb_sent  \n",
-       "0            lucre such as profit and profit  \n",
-       "1         periodical such as issue and issue  \n",
-       "2       periodical such as issue and journal  \n",
-       "4     periodical such as journal and journal  \n",
-       "5  lithodemic unit such as series and series  "
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "young-reception",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF2 = newPairsDF1[['node1', 'node2', 'node1_right', 'node1_label', 'node2_label', 'node1_label_right', 'sent', 'sent_right', 'comb_sent']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "israeli-daniel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3 = newPairsDF2.rename(columns={'node1_right': 'node2', 'node2': 'parent', 'node1_label_right': 'node2_label', 'node2_label': 'par_label', 'sent': 'node1_sent', 'sent_right': 'node2_sent'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "appointed-distributor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>Q100257358</td>\n",
-       "      <td>Q15209696</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>lucre</td>\n",
-       "      <td>profit</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>profit is lucre</td>\n",
-       "      <td>lucre such as profit and profit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>periodical such as issue and issue</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q28869365</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>issue</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>issue is periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>periodical such as issue and journal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>periodical such as journal and journal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>Q101604684</td>\n",
-       "      <td>Q12562111</td>\n",
-       "      <td>series</td>\n",
-       "      <td>lithodemic unit</td>\n",
-       "      <td>series</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>series is lithodemic unit</td>\n",
-       "      <td>lithodemic unit such as series and series</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1      parent      node2 node1_label        par_label node2_label  \\\n",
-       "0  Q15209696  Q100257358  Q15209696      profit            lucre      profit   \n",
-       "1  Q28869365    Q1002697  Q28869365       issue       periodical       issue   \n",
-       "2  Q28869365    Q1002697     Q49850       issue       periodical     journal   \n",
-       "4     Q49850    Q1002697     Q49850     journal       periodical     journal   \n",
-       "5  Q12562111  Q101604684  Q12562111      series  lithodemic unit      series   \n",
-       "\n",
-       "                  node1_sent                 node2_sent  \\\n",
-       "0            profit is lucre            profit is lucre   \n",
-       "1        issue is periodical        issue is periodical   \n",
-       "2        issue is periodical      journal is periodical   \n",
-       "4      journal is periodical      journal is periodical   \n",
-       "5  series is lithodemic unit  series is lithodemic unit   \n",
-       "\n",
-       "                                   comb_sent  \n",
-       "0            lucre such as profit and profit  \n",
-       "1         periodical such as issue and issue  \n",
-       "2       periodical such as issue and journal  \n",
-       "4     periodical such as journal and journal  \n",
-       "5  lithodemic unit such as series and series  "
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "adapted-particular",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'wordSimWordSet' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-63-de98d97421e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewPairsDF3wordsimed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewPairsDF3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnewPairsDF3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode2_label\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwordSimWordSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7763\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7764\u001b[0m         )\n\u001b[0;32m-> 7765\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-63-de98d97421e7>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewPairsDF3wordsimed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewPairsDF3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnewPairsDF3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode1_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode2_label\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwordSimWordSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'wordSimWordSet' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3[newPairsDF3.apply(lambda p: (p.node1_label, p.node2_label) in wordSimWordSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "dying-volleyball",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'newPairsDF3wordsimed' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-64-8cb9196bfc89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewPairsDF3wordsimed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'newPairsDF3wordsimed' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "newPairsDF3wordsimed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "fewer-there",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'newPairsDF3wordsimed' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-62-430ef6ae3413>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewPairsDF3wordsimed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'newPairsDF3wordsimed' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "len(newPairsDF3wordsimed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "naval-compound",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "piano-bernard",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "correct-validation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expressed-graphics",
-   "metadata": {},
-   "source": [
-    "# Amandeep's Sim File Computation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "distinct-teacher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF = pd.read_csv(\"../data/wordsim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interim-exhibition",
-   "metadata": {},
-   "source": [
-    "## Extracting wordsim word pairs from wikidata P279 dataset\n",
-    "\n",
-    "Basis: Find child-parent samples which are in wordsim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "registered-waters",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimWordPairsSet = set(wordSimDF.apply(lambda p: (p.word1_kg_id, p.word2_kg_id), axis=1).tolist())\n",
-    "wordSimWordsSet = set(wordSimDF.word1_kg_id.tolist() + wordSimDF.word2_kg_id.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "separated-jenny",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimed = temp3[temp3.apply(lambda p: (p.node1, p.node2) in wordSimWordPairsSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "suffering-belize",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>225669</th>\n",
-       "      <td>Q61509-P279-Q1914636-522de7b1-0</td>\n",
-       "      <td>Q61509</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1914636</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>travel is activity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253542</th>\n",
-       "      <td>Q192935-P279-Q2095-c9aba5ef-0</td>\n",
-       "      <td>Q192935</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>food</td>\n",
-       "      <td>seafood is food</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480116</th>\n",
-       "      <td>Q93352-P279-Q468756-18da9376-0</td>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>coast is shore</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495543</th>\n",
-       "      <td>Q98069877-P279-Q49848-07219034-0</td>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>video</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>document</td>\n",
-       "      <td>video is document</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      id      node1 node1_label label  \\\n",
-       "225669   Q61509-P279-Q1914636-522de7b1-0     Q61509      travel  P279   \n",
-       "253542     Q192935-P279-Q2095-c9aba5ef-0    Q192935     seafood  P279   \n",
-       "480116    Q93352-P279-Q468756-18da9376-0     Q93352       coast  P279   \n",
-       "495543  Q98069877-P279-Q49848-07219034-0  Q98069877       video  P279   \n",
-       "\n",
-       "           node2 node2_label                sent  \n",
-       "225669  Q1914636    activity  travel is activity  \n",
-       "253542     Q2095        food     seafood is food  \n",
-       "480116   Q468756       shore      coast is shore  \n",
-       "495543    Q49848    document   video is document  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3wordsimed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "billion-highlight",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "refined-mattress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimedMergedDF = temp3wordsimed.set_index(['node1', 'node2']).join(wordSimDF.rename(columns={'word1_kg_id': 'node1', 'word2_kg_id': 'node2'}).set_index(['node1', 'node2']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "prerequisite-jewelry",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q192935</th>\n",
-       "      <th>Q2095</th>\n",
-       "      <td>Q192935-P279-Q2095-c9aba5ef-0</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>food</td>\n",
-       "      <td>seafood is food</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>food from the sea, e.g. fish, shrimp, crab, mu...</td>\n",
-       "      <td>food</td>\n",
-       "      <td>food</td>\n",
-       "      <td>any substance consumed to provide nutritional ...</td>\n",
-       "      <td>6.8750</td>\n",
-       "      <td>0.531331</td>\n",
-       "      <td>0.467495</td>\n",
-       "      <td>0.664127</td>\n",
-       "      <td>0.565204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q61509</th>\n",
-       "      <th>Q1914636</th>\n",
-       "      <td>Q61509-P279-Q1914636-522de7b1-0</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>travel is activity</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>movement of people between relatively distant ...</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>event; actions that result in changes of state</td>\n",
-       "      <td>4.1875</td>\n",
-       "      <td>0.400234</td>\n",
-       "      <td>0.638347</td>\n",
-       "      <td>0.682683</td>\n",
-       "      <td>0.136251</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q93352</th>\n",
-       "      <th>Q468756</th>\n",
-       "      <td>Q93352-P279-Q468756-18da9376-0</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>coast is shore</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>area where land meets the sea or ocean</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>fringe of land at the edge of a large body of ...</td>\n",
-       "      <td>8.2755</td>\n",
-       "      <td>0.604508</td>\n",
-       "      <td>0.628945</td>\n",
-       "      <td>0.859171</td>\n",
-       "      <td>0.539750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q98069877</th>\n",
-       "      <th>Q49848</th>\n",
-       "      <td>Q98069877-P279-Q49848-07219034-0</td>\n",
-       "      <td>video</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>document</td>\n",
-       "      <td>video is document</td>\n",
-       "      <td>video</td>\n",
-       "      <td>video</td>\n",
-       "      <td>motion picture format</td>\n",
-       "      <td>archive</td>\n",
-       "      <td>document</td>\n",
-       "      <td>preserved information</td>\n",
-       "      <td>3.4375</td>\n",
-       "      <td>0.530936</td>\n",
-       "      <td>0.260283</td>\n",
-       "      <td>0.679086</td>\n",
-       "      <td>0.649235</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  id node1_label label  \\\n",
-       "node1     node2                                                          \n",
-       "Q192935   Q2095        Q192935-P279-Q2095-c9aba5ef-0     seafood  P279   \n",
-       "Q61509    Q1914636   Q61509-P279-Q1914636-522de7b1-0      travel  P279   \n",
-       "Q93352    Q468756     Q93352-P279-Q468756-18da9376-0       coast  P279   \n",
-       "Q98069877 Q49848    Q98069877-P279-Q49848-07219034-0       video  P279   \n",
-       "\n",
-       "                   node2_label                sent   Word 1 word1_kg_label  \\\n",
-       "node1     node2                                                              \n",
-       "Q192935   Q2095           food     seafood is food  seafood        seafood   \n",
-       "Q61509    Q1914636    activity  travel is activity   travel         travel   \n",
-       "Q93352    Q468756        shore      coast is shore    coast          coast   \n",
-       "Q98069877 Q49848      document   video is document    video          video   \n",
-       "\n",
-       "                                                 word1_kg_description  \\\n",
-       "node1     node2                                                         \n",
-       "Q192935   Q2095     food from the sea, e.g. fish, shrimp, crab, mu...   \n",
-       "Q61509    Q1914636  movement of people between relatively distant ...   \n",
-       "Q93352    Q468756              area where land meets the sea or ocean   \n",
-       "Q98069877 Q49848                                motion picture format   \n",
-       "\n",
-       "                      Word 2 word2_kg_label  \\\n",
-       "node1     node2                               \n",
-       "Q192935   Q2095         food           food   \n",
-       "Q61509    Q1914636  activity       activity   \n",
-       "Q93352    Q468756      shore          shore   \n",
-       "Q98069877 Q49848     archive       document   \n",
-       "\n",
-       "                                                 word2_kg_description  \\\n",
-       "node1     node2                                                         \n",
-       "Q192935   Q2095     any substance consumed to provide nutritional ...   \n",
-       "Q61509    Q1914636     event; actions that result in changes of state   \n",
-       "Q93352    Q468756   fringe of land at the edge of a large body of ...   \n",
-       "Q98069877 Q49848                                preserved information   \n",
-       "\n",
-       "                    Human (Mean)   complex    transe      text     class  \n",
-       "node1     node2                                                           \n",
-       "Q192935   Q2095           6.8750  0.531331  0.467495  0.664127  0.565204  \n",
-       "Q61509    Q1914636        4.1875  0.400234  0.638347  0.682683  0.136251  \n",
-       "Q93352    Q468756         8.2755  0.604508  0.628945  0.859171  0.539750  \n",
-       "Q98069877 Q49848          3.4375  0.530936  0.260283  0.679086  0.649235  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3wordsimedMergedDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "reduced-treasury",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimedMergedDF = temp3wordsimedMergedDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "alike-killer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.745959043502808 s\n",
-      "0.3390045166015625 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "temp3wordsimedMergedDF['node1_emb'] = pd.Series(list(getSentEmbeddings(temp3wordsimedMergedDF.node1_label, 'bert-base-nli-mean-tokens')))\n",
-    "temp3wordsimedMergedDF['node2_emb'] = pd.Series(list(getSentEmbeddings(temp3wordsimedMergedDF.node2_label, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "surgical-victory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimedMergedDF['bert2SentSim'] = temp3wordsimedMergedDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "valued-disco",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# build gpt model\n",
-    "gpt_model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
-    "gpt_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
-    "\n",
-    "def gpt_score(sent1):\n",
-    "    # input sentence tensor\n",
-    "    # output score\n",
-    "    tokens_tensor = gpt_tokenizer.encode(sent1, add_special_tokens=False, return_tensors=\"pt\")\n",
-    "    loss = gpt_model(tokens_tensor, labels=tokens_tensor)[0]\n",
-    "#     print(tokens_tensor.size(1))\n",
-    "    return np.exp(loss.cpu().detach().numpy() / tokens_tensor.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "id": "accepting-albuquerque",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimedMergedDF['gpt2Ppl'] = temp3wordsimedMergedDF.sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "dedicated-paintball",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp3wordsimedMergedDF['gpt2Ppl_normed'] = temp3wordsimedMergedDF['gpt2Ppl'].apply(lambda p: 1 / (1 + p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "collaborative-resolution",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>Word 1</th>\n",
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-       "      <th>bert2SentSim</th>\n",
-       "      <th>gpt2Ppl</th>\n",
-       "      <th>gpt2Ppl_normed</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q192935</td>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>Q192935-P279-Q2095-c9aba5ef-0</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>food</td>\n",
-       "      <td>seafood is food</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>seafood</td>\n",
-       "      <td>food from the sea, e.g. fish, shrimp, crab, mu...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0.565204</td>\n",
-       "      <td>[0.23942716, -0.5923174, 1.1605071, 0.01221862...</td>\n",
-       "      <td>[-0.143091, -0.18455797, 2.158043, 0.11467595,...</td>\n",
-       "      <td>0.707566</td>\n",
-       "      <td>3.312116</td>\n",
-       "      <td>0.231905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q61509</td>\n",
-       "      <td>Q1914636</td>\n",
-       "      <td>Q61509-P279-Q1914636-522de7b1-0</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>activity</td>\n",
-       "      <td>travel is activity</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>travel</td>\n",
-       "      <td>movement of people between relatively distant ...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.1875</td>\n",
-       "      <td>0.400234</td>\n",
-       "      <td>0.638347</td>\n",
-       "      <td>0.682683</td>\n",
-       "      <td>0.136251</td>\n",
-       "      <td>[-0.056974184, -0.43726933, 2.6971796, 0.40128...</td>\n",
-       "      <td>[0.3885105, -1.4102317, 2.6005147, 0.70024395,...</td>\n",
-       "      <td>0.743532</td>\n",
-       "      <td>15.743042</td>\n",
-       "      <td>0.059726</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>Q93352-P279-Q468756-18da9376-0</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>coast is shore</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>area where land meets the sea or ocean</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8.2755</td>\n",
-       "      <td>0.604508</td>\n",
-       "      <td>0.628945</td>\n",
-       "      <td>0.859171</td>\n",
-       "      <td>0.539750</td>\n",
-       "      <td>[0.18146253, -0.65918523, 2.4364007, -0.160515...</td>\n",
-       "      <td>[-0.22691835, -1.0949634, 2.380236, 0.05142845...</td>\n",
-       "      <td>0.860366</td>\n",
-       "      <td>8.626138</td>\n",
-       "      <td>0.103884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>Q49848</td>\n",
-       "      <td>Q98069877-P279-Q49848-07219034-0</td>\n",
-       "      <td>video</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>document</td>\n",
-       "      <td>video is document</td>\n",
-       "      <td>video</td>\n",
-       "      <td>video</td>\n",
-       "      <td>motion picture format</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.4375</td>\n",
-       "      <td>0.530936</td>\n",
-       "      <td>0.260283</td>\n",
-       "      <td>0.679086</td>\n",
-       "      <td>0.649235</td>\n",
-       "      <td>[0.6363447, -0.42124, 2.4174998, 0.5520306, 0....</td>\n",
-       "      <td>[0.08141766, -0.42179188, 2.2739022, 0.4880786...</td>\n",
-       "      <td>0.683161</td>\n",
-       "      <td>22.084906</td>\n",
-       "      <td>0.043318</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1     node2                                id node1_label label  \\\n",
-       "0    Q192935     Q2095     Q192935-P279-Q2095-c9aba5ef-0     seafood  P279   \n",
-       "1     Q61509  Q1914636   Q61509-P279-Q1914636-522de7b1-0      travel  P279   \n",
-       "2     Q93352   Q468756    Q93352-P279-Q468756-18da9376-0       coast  P279   \n",
-       "3  Q98069877    Q49848  Q98069877-P279-Q49848-07219034-0       video  P279   \n",
-       "\n",
-       "  node2_label                sent   Word 1 word1_kg_label  \\\n",
-       "0        food     seafood is food  seafood        seafood   \n",
-       "1    activity  travel is activity   travel         travel   \n",
-       "2       shore      coast is shore    coast          coast   \n",
-       "3    document   video is document    video          video   \n",
-       "\n",
-       "                                word1_kg_description  ... Human (Mean)  \\\n",
-       "0  food from the sea, e.g. fish, shrimp, crab, mu...  ...       6.8750   \n",
-       "1  movement of people between relatively distant ...  ...       4.1875   \n",
-       "2             area where land meets the sea or ocean  ...       8.2755   \n",
-       "3                              motion picture format  ...       3.4375   \n",
-       "\n",
-       "    complex    transe      text     class  \\\n",
-       "0  0.531331  0.467495  0.664127  0.565204   \n",
-       "1  0.400234  0.638347  0.682683  0.136251   \n",
-       "2  0.604508  0.628945  0.859171  0.539750   \n",
-       "3  0.530936  0.260283  0.679086  0.649235   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [0.23942716, -0.5923174, 1.1605071, 0.01221862...   \n",
-       "1  [-0.056974184, -0.43726933, 2.6971796, 0.40128...   \n",
-       "2  [0.18146253, -0.65918523, 2.4364007, -0.160515...   \n",
-       "3  [0.6363447, -0.42124, 2.4174998, 0.5520306, 0....   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim    gpt2Ppl  \\\n",
-       "0  [-0.143091, -0.18455797, 2.158043, 0.11467595,...      0.707566   3.312116   \n",
-       "1  [0.3885105, -1.4102317, 2.6005147, 0.70024395,...      0.743532  15.743042   \n",
-       "2  [-0.22691835, -1.0949634, 2.380236, 0.05142845...      0.860366   8.626138   \n",
-       "3  [0.08141766, -0.42179188, 2.2739022, 0.4880786...      0.683161  22.084906   \n",
-       "\n",
-       "  gpt2Ppl_normed  \n",
-       "0       0.231905  \n",
-       "1       0.059726  \n",
-       "2       0.103884  \n",
-       "3       0.043318  \n",
-       "\n",
-       "[4 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3wordsimedMergedDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "studied-january",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.6666666666666669, pvalue=0.3333333333333333)"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(temp3wordsimedMergedDF['bert2SentSim'], temp3wordsimedMergedDF['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "spiritual-andrews",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.7999999999999999, pvalue=0.20000000000000007)"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(temp3wordsimedMergedDF['bert2SentSim'], temp3wordsimedMergedDF['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "silent-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.6666666666666669, pvalue=0.3333333333333333)"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(temp3wordsimedMergedDF['gpt2Ppl_normed'], temp3wordsimedMergedDF['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "id": "banner-bleeding",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.7999999999999999, pvalue=0.20000000000000007)"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(temp3wordsimedMergedDF['gpt2Ppl_normed'], temp3wordsimedMergedDF['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "married-alert",
-   "metadata": {},
-   "source": [
-    "## Extracting wordsim word pairs from wikidata P279 siblings dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spoken-watershed",
-   "metadata": {},
-   "source": [
-    "### Generate new P279 siblings dataset from wordsim words subset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "positive-workshop",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pytorch_pretrained_bert import BertTokenizer,BertForMaskedLM\n",
-    "import torch\n",
-    "import pandas as pd\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "id": "impaired-correlation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bertMaskedLM = BertForMaskedLM.from_pretrained('bert-base-uncased')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "id": "modified-outreach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "id": "industrial-victory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bert_score(sentence):\n",
-    "    tokenize_input = berttokenizer.tokenize(sentence)\n",
-    "    tensor_input = torch.tensor([berttokenizer.convert_tokens_to_ids(tokenize_input)])\n",
-    "    predictions=bertMaskedLM(tensor_input)\n",
-    "    loss_fct = torch.nn.CrossEntropyLoss()\n",
-    "    loss = loss_fct(predictions.squeeze(),tensor_input.squeeze()).data \n",
-    "    return math.exp(loss / tensor_input.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "chronic-accused",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279wordsimed = temp3[temp3.node1.apply(lambda p: p in wordSimWordsSet)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "intense-wonder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "690"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(P279wordsimed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "stopped-eligibility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairLeftDF = P279wordsimed.copy()\n",
-    "newPairRightDF = P279wordsimed.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "present-jordan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF = newPairLeftDF.set_index('node2').join(newPairRightDF.set_index('node2'),rsuffix='_right').reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "blank-adelaide",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                   id     node1 node1_label  \\\n",
-       "0  Q100171002  Q1503443-P279-Q100171002-8282ae74-0  Q1503443     secrecy   \n",
-       "1    Q1002697      Q49850-P279-Q1002697-0c52cf68-0    Q49850     journal   \n",
-       "2     Q101998        Q4421-P279-Q101998-d0983886-0     Q4421      forest   \n",
-       "3     Q102165        Q2294-P279-Q102165-0893a180-0     Q2294      proton   \n",
-       "4     Q102205       Q11435-P279-Q102205-7b448d05-0    Q11435      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                              id_right node1_right node1_label_right  \\\n",
-       "0  Q1503443-P279-Q100171002-8282ae74-0    Q1503443           secrecy   \n",
-       "1      Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "2        Q4421-P279-Q101998-d0983886-0       Q4421            forest   \n",
-       "3        Q2294-P279-Q102165-0893a180-0       Q2294            proton   \n",
-       "4       Q11435-P279-Q102205-7b448d05-0      Q11435            liquid   \n",
-       "\n",
-       "  label_right node2_label_right              sent_right  \n",
-       "0        P279       concealment  secrecy is concealment  \n",
-       "1        P279        periodical   journal is periodical  \n",
-       "2        P279             biome         forest is biome  \n",
-       "3        P279           nucleon       proton is nucleon  \n",
-       "4        P279             fluid         liquid is fluid  "
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "living-sigma",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF['set_nodes'] = newPairsDF.apply(lambda p: str(sorted([p.node1, p.node1_right]) + [p.node2]), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "artificial-lawrence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF1 = newPairsDF[~newPairsDF['set_nodes'].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "amended-graduate",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1340 statements get reduced to 1012 statements after checking for duplicate pairs\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{len(newPairsDF)} statements get reduced to {len(newPairsDF1)} statements after checking for duplicate pairs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "id": "worth-albert",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>['Q1503443', 'Q1503443', 'Q100171002']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q49850', 'Q49850', 'Q1002697']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>['Q4421', 'Q4421', 'Q101998']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>['Q2294', 'Q2294', 'Q102165']</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>['Q11435', 'Q11435', 'Q102205']</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                   id     node1 node1_label  \\\n",
-       "0  Q100171002  Q1503443-P279-Q100171002-8282ae74-0  Q1503443     secrecy   \n",
-       "1    Q1002697      Q49850-P279-Q1002697-0c52cf68-0    Q49850     journal   \n",
-       "2     Q101998        Q4421-P279-Q101998-d0983886-0     Q4421      forest   \n",
-       "3     Q102165        Q2294-P279-Q102165-0893a180-0     Q2294      proton   \n",
-       "4     Q102205       Q11435-P279-Q102205-7b448d05-0    Q11435      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                              id_right node1_right node1_label_right  \\\n",
-       "0  Q1503443-P279-Q100171002-8282ae74-0    Q1503443           secrecy   \n",
-       "1      Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "2        Q4421-P279-Q101998-d0983886-0       Q4421            forest   \n",
-       "3        Q2294-P279-Q102165-0893a180-0       Q2294            proton   \n",
-       "4       Q11435-P279-Q102205-7b448d05-0      Q11435            liquid   \n",
-       "\n",
-       "  label_right node2_label_right              sent_right  \\\n",
-       "0        P279       concealment  secrecy is concealment   \n",
-       "1        P279        periodical   journal is periodical   \n",
-       "2        P279             biome         forest is biome   \n",
-       "3        P279           nucleon       proton is nucleon   \n",
-       "4        P279             fluid         liquid is fluid   \n",
-       "\n",
-       "                                set_nodes  \n",
-       "0  ['Q1503443', 'Q1503443', 'Q100171002']  \n",
-       "1        ['Q49850', 'Q49850', 'Q1002697']  \n",
-       "2           ['Q4421', 'Q4421', 'Q101998']  \n",
-       "3           ['Q2294', 'Q2294', 'Q102165']  \n",
-       "4         ['Q11435', 'Q11435', 'Q102205']  "
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "id": "romance-trauma",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-222-f2437d3c9095>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node1_label + ' and ' + p.node1_label_right + ' are examples of ' + p.node2_label, axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node2_label + ' such as ' + p.node1_label + ' and ' + p.node1_label_right, axis=1)\n",
-    "# newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node2_label + ' is typically ' + p.node1_label + ' or ' + p.node1_label_right, axis=1)\n",
-    "newPairsDF1['comb_sent'] = newPairsDF1.apply(lambda p: p.node1_label + ' and ' + p.node1_label_right + ' are examples of ' + p.node2_label, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "id": "endangered-bacteria",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>id_right</th>\n",
-       "      <th>node1_right</th>\n",
-       "      <th>node1_label_right</th>\n",
-       "      <th>label_right</th>\n",
-       "      <th>node2_label_right</th>\n",
-       "      <th>sent_right</th>\n",
-       "      <th>set_nodes</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>['Q1503443', 'Q1503443', 'Q100171002']</td>\n",
-       "      <td>secrecy and secrecy are examples of concealment</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>['Q49850', 'Q49850', 'Q1002697']</td>\n",
-       "      <td>journal and journal are examples of periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>['Q4421', 'Q4421', 'Q101998']</td>\n",
-       "      <td>forest and forest are examples of biome</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>['Q2294', 'Q2294', 'Q102165']</td>\n",
-       "      <td>proton and proton are examples of nucleon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>['Q11435', 'Q11435', 'Q102205']</td>\n",
-       "      <td>liquid and liquid are examples of fluid</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2                                   id     node1 node1_label  \\\n",
-       "0  Q100171002  Q1503443-P279-Q100171002-8282ae74-0  Q1503443     secrecy   \n",
-       "1    Q1002697      Q49850-P279-Q1002697-0c52cf68-0    Q49850     journal   \n",
-       "2     Q101998        Q4421-P279-Q101998-d0983886-0     Q4421      forest   \n",
-       "3     Q102165        Q2294-P279-Q102165-0893a180-0     Q2294      proton   \n",
-       "4     Q102205       Q11435-P279-Q102205-7b448d05-0    Q11435      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                              id_right node1_right node1_label_right  \\\n",
-       "0  Q1503443-P279-Q100171002-8282ae74-0    Q1503443           secrecy   \n",
-       "1      Q49850-P279-Q1002697-0c52cf68-0      Q49850           journal   \n",
-       "2        Q4421-P279-Q101998-d0983886-0       Q4421            forest   \n",
-       "3        Q2294-P279-Q102165-0893a180-0       Q2294            proton   \n",
-       "4       Q11435-P279-Q102205-7b448d05-0      Q11435            liquid   \n",
-       "\n",
-       "  label_right node2_label_right              sent_right  \\\n",
-       "0        P279       concealment  secrecy is concealment   \n",
-       "1        P279        periodical   journal is periodical   \n",
-       "2        P279             biome         forest is biome   \n",
-       "3        P279           nucleon       proton is nucleon   \n",
-       "4        P279             fluid         liquid is fluid   \n",
-       "\n",
-       "                                set_nodes  \\\n",
-       "0  ['Q1503443', 'Q1503443', 'Q100171002']   \n",
-       "1        ['Q49850', 'Q49850', 'Q1002697']   \n",
-       "2           ['Q4421', 'Q4421', 'Q101998']   \n",
-       "3           ['Q2294', 'Q2294', 'Q102165']   \n",
-       "4         ['Q11435', 'Q11435', 'Q102205']   \n",
-       "\n",
-       "                                         comb_sent  \n",
-       "0  secrecy and secrecy are examples of concealment  \n",
-       "1   journal and journal are examples of periodical  \n",
-       "2          forest and forest are examples of biome  \n",
-       "3        proton and proton are examples of nucleon  \n",
-       "4          liquid and liquid are examples of fluid  "
-      ]
-     },
-     "execution_count": 223,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "id": "extra-monitor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF2 = newPairsDF1[['node1', 'node2', 'node1_right', 'node1_label', 'node2_label', 'node1_label_right', 'sent', 'sent_right', 'comb_sent']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "id": "satisfactory-knock",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3 = newPairsDF2.rename(columns={'node1_right': 'node2', 'node2': 'parent', 'node1_label_right': 'node2_label', 'node2_label': 'par_label', 'sent': 'node1_sent', 'sent_right': 'node2_sent'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "id": "divided-reading",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>secrecy and secrecy are examples of concealment</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>journal and journal are examples of periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>forest and forest are examples of biome</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>proton and proton are examples of nucleon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>liquid and liquid are examples of fluid</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1      parent     node2 node1_label    par_label node2_label  \\\n",
-       "0  Q1503443  Q100171002  Q1503443     secrecy  concealment     secrecy   \n",
-       "1    Q49850    Q1002697    Q49850     journal   periodical     journal   \n",
-       "2     Q4421     Q101998     Q4421      forest        biome      forest   \n",
-       "3     Q2294     Q102165     Q2294      proton      nucleon      proton   \n",
-       "4    Q11435     Q102205    Q11435      liquid        fluid      liquid   \n",
-       "\n",
-       "               node1_sent              node2_sent  \\\n",
-       "0  secrecy is concealment  secrecy is concealment   \n",
-       "1   journal is periodical   journal is periodical   \n",
-       "2         forest is biome         forest is biome   \n",
-       "3       proton is nucleon       proton is nucleon   \n",
-       "4         liquid is fluid         liquid is fluid   \n",
-       "\n",
-       "                                         comb_sent  \n",
-       "0  secrecy and secrecy are examples of concealment  \n",
-       "1   journal and journal are examples of periodical  \n",
-       "2          forest and forest are examples of biome  \n",
-       "3        proton and proton are examples of nucleon  \n",
-       "4          liquid and liquid are examples of fluid  "
-      ]
-     },
-     "execution_count": 226,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "id": "demanding-latter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3.set_index(['node1', 'node2']).join(wordSimDF.rename(columns={'word1_kg_id': 'node1', 'word2_kg_id': 'node2'}).set_index(['node1', 'node2']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "id": "blessed-story",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3wordsimed.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "id": "prerequisite-spoke",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3wordsimed[newPairsDF3wordsimed.apply(lambda p: (p.node1, p.node2) in wordSimWordPairsSet, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "id": "upset-multiple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3wordsimed[newPairsDF3wordsimed.apply(lambda p: p.node1 != p.node2, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "id": "associate-digest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed = newPairsDF3wordsimed.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "id": "several-supply",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 232,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(newPairsDF3wordsimed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "id": "textile-thousand",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11620402336120605 s\n",
-      "0.14222192764282227 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "newPairsDF3wordsimed['node1_emb'] = pd.Series(list(getSentEmbeddings(newPairsDF3wordsimed.node1_sent, 'bert-base-nli-mean-tokens')))\n",
-    "newPairsDF3wordsimed['node2_emb'] = pd.Series(list(getSentEmbeddings(newPairsDF3wordsimed.node2_sent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "id": "first-fifteen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>index</th>\n",
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-       "      <th>parent</th>\n",
-       "      <th>node1_label</th>\n",
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-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
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-       "      <th>...</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_label</th>\n",
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-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
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-       "      <th>0</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>Q24060765</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>animal organ</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>skin is animal organ</td>\n",
-       "      <td>eye is animal organ</td>\n",
-       "      <td>skin and eye are examples of animal organ</td>\n",
-       "      <td>...</td>\n",
-       "      <td>eye</td>\n",
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-       "      <td>[0.72735953, -0.5447348, 0.95869535, 0.6319232...</td>\n",
-       "      <td>[0.755933, 0.04024082, 0.9340492, 0.41963616, ...</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12</td>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>Q13406554</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>sports competition</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>championship is sports competition</td>\n",
-       "      <td>tournament is sports competition</td>\n",
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-       "      <td>tournament</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>competition involving a relatively large numbe...</td>\n",
-       "      <td>7.5000</td>\n",
-       "      <td>0.604882</td>\n",
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-       "      <td>0.899429</td>\n",
-       "      <td>[0.083933495, 0.082726024, 1.7943382, 0.518000...</td>\n",
-       "      <td>[0.53537947, -0.30064347, 2.2253025, 0.4019227...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>47</td>\n",
-       "      <td>Q11399</td>\n",
-       "      <td>Q8341</td>\n",
-       "      <td>Q373342</td>\n",
-       "      <td>rock music</td>\n",
-       "      <td>popular music</td>\n",
-       "      <td>jazz</td>\n",
-       "      <td>rock music is popular music</td>\n",
-       "      <td>jazz is popular music</td>\n",
-       "      <td>rock music and jazz are examples of popular music</td>\n",
-       "      <td>...</td>\n",
-       "      <td>jazz</td>\n",
-       "      <td>jazz</td>\n",
-       "      <td>musical style, genre, and theory</td>\n",
-       "      <td>5.5000</td>\n",
-       "      <td>0.778366</td>\n",
-       "      <td>0.424096</td>\n",
-       "      <td>0.566754</td>\n",
-       "      <td>0.935759</td>\n",
-       "      <td>[0.113906465, -0.3389766, 0.10356339, 0.254968...</td>\n",
-       "      <td>[-0.10467594, -0.56408674, 0.6394385, -0.28194...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>127</td>\n",
-       "      <td>Q12097</td>\n",
-       "      <td>Q16511993</td>\n",
-       "      <td>Q116</td>\n",
-       "      <td>king</td>\n",
-       "      <td>monarch</td>\n",
-       "      <td>queen</td>\n",
-       "      <td>king is monarch</td>\n",
-       "      <td>queen is monarch</td>\n",
-       "      <td>king and queen are examples of monarch</td>\n",
-       "      <td>...</td>\n",
-       "      <td>queen</td>\n",
-       "      <td>queen</td>\n",
-       "      <td>class of female monarch</td>\n",
-       "      <td>5.6875</td>\n",
-       "      <td>0.664346</td>\n",
-       "      <td>0.699062</td>\n",
-       "      <td>0.718037</td>\n",
-       "      <td>0.894225</td>\n",
-       "      <td>[0.5253047, 0.57659674, 1.4201468, 0.8925513, ...</td>\n",
-       "      <td>[0.26084498, 0.3714701, 1.3805685, 0.7634336, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>182</td>\n",
-       "      <td>Q1308677</td>\n",
-       "      <td>Q21171241</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>deployment</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>departure</td>\n",
-       "      <td>deployment is occurrence</td>\n",
-       "      <td>departure is occurrence</td>\n",
-       "      <td>deployment and departure are examples of occur...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>departure</td>\n",
-       "      <td>departure</td>\n",
-       "      <td>act of departing or something that has departed</td>\n",
-       "      <td>5.5000</td>\n",
-       "      <td>0.299540</td>\n",
-       "      <td>0.225839</td>\n",
-       "      <td>0.496331</td>\n",
-       "      <td>0.030801</td>\n",
-       "      <td>[0.26534072, -0.9580938, 2.5264893, 0.30084807...</td>\n",
-       "      <td>[-0.32454324, -1.2765993, 2.5993214, 0.3582288...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index     node1      node2     parent   node1_label           par_label  \\\n",
-       "0      5     Q1074      Q7364  Q24060765          skin        animal organ   \n",
-       "1     12  Q1079023    Q500834  Q13406554  championship  sports competition   \n",
-       "2     47    Q11399      Q8341    Q373342    rock music       popular music   \n",
-       "3    127    Q12097  Q16511993       Q116          king             monarch   \n",
-       "4    182  Q1308677  Q21171241   Q1190554    deployment          occurrence   \n",
-       "\n",
-       "  node2_label                          node1_sent  \\\n",
-       "0         eye                skin is animal organ   \n",
-       "1  tournament  championship is sports competition   \n",
-       "2        jazz         rock music is popular music   \n",
-       "3       queen                     king is monarch   \n",
-       "4   departure            deployment is occurrence   \n",
-       "\n",
-       "                         node2_sent  \\\n",
-       "0               eye is animal organ   \n",
-       "1  tournament is sports competition   \n",
-       "2             jazz is popular music   \n",
-       "3                  queen is monarch   \n",
-       "4           departure is occurrence   \n",
-       "\n",
-       "                                           comb_sent  ...      Word 2  \\\n",
-       "0          skin and eye are examples of animal organ  ...         eye   \n",
-       "1  championship and tournament are examples of sp...  ...  tournament   \n",
-       "2  rock music and jazz are examples of popular music  ...        jazz   \n",
-       "3             king and queen are examples of monarch  ...       queen   \n",
-       "4  deployment and departure are examples of occur...  ...   departure   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0            eye  organ that detects light and converts it into ...   \n",
-       "1     tournament  competition involving a relatively large numbe...   \n",
-       "2           jazz                   musical style, genre, and theory   \n",
-       "3          queen                            class of female monarch   \n",
-       "4      departure    act of departing or something that has departed   \n",
-       "\n",
-       "  Human (Mean)   complex    transe      text     class  \\\n",
-       "0       2.7500  0.500769  0.485879  0.611496  0.541116   \n",
-       "1       7.5000  0.604882  0.628038  0.838909  0.899429   \n",
-       "2       5.5000  0.778366  0.424096  0.566754  0.935759   \n",
-       "3       5.6875  0.664346  0.699062  0.718037  0.894225   \n",
-       "4       5.5000  0.299540  0.225839  0.496331  0.030801   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [0.72735953, -0.5447348, 0.95869535, 0.6319232...   \n",
-       "1  [0.083933495, 0.082726024, 1.7943382, 0.518000...   \n",
-       "2  [0.113906465, -0.3389766, 0.10356339, 0.254968...   \n",
-       "3  [0.5253047, 0.57659674, 1.4201468, 0.8925513, ...   \n",
-       "4  [0.26534072, -0.9580938, 2.5264893, 0.30084807...   \n",
-       "\n",
-       "                                           node2_emb  \n",
-       "0  [0.755933, 0.04024082, 0.9340492, 0.41963616, ...  \n",
-       "1  [0.53537947, -0.30064347, 2.2253025, 0.4019227...  \n",
-       "2  [-0.10467594, -0.56408674, 0.6394385, -0.28194...  \n",
-       "3  [0.26084498, 0.3714701, 1.3805685, 0.7634336, ...  \n",
-       "4  [-0.32454324, -1.2765993, 2.5993214, 0.3582288...  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF3wordsimed.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "id": "heard-subscriber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed['bert2SentSim'] = newPairsDF3wordsimed.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "id": "norman-replica",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed['gpt2Ppl'] = newPairsDF3wordsimed.comb_sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "id": "integrated-climate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed['gpt2Ppl_normed'] = newPairsDF3wordsimed['gpt2Ppl'].apply(lambda p: 1 / (1 + p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "id": "modern-irish",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newPairsDF3wordsimed['bert_ppl'] = newPairsDF3wordsimed.comb_sent.apply(bert_score)\n",
-    "newPairsDF3wordsimed['bert_ppl_normed'] = newPairsDF3wordsimed['bert_ppl'].apply(lambda p: 1 / (1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "hispanic-thailand",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "id": "digital-family",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3924956554446995"
-      ]
-     },
-     "execution_count": 239,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 / (1 + bert_score('animal organ such as skin and continent'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "id": "practical-debate",
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>Q24060765</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>animal organ</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>0.855939</td>\n",
-       "      <td>2.035088</td>\n",
-       "      <td>0.329480</td>\n",
-       "      <td>2.064750</td>\n",
-       "      <td>0.326291</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12</td>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>Q13406554</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>sports competition</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>championship is sports competition</td>\n",
-       "      <td>tournament is sports competition</td>\n",
-       "      <td>championship and tournament are examples of sp...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.628038</td>\n",
-       "      <td>0.838909</td>\n",
-       "      <td>0.899429</td>\n",
-       "      <td>[0.083933495, 0.082726024, 1.7943382, 0.518000...</td>\n",
-       "      <td>[0.53537947, -0.30064347, 2.2253025, 0.4019227...</td>\n",
-       "      <td>0.891540</td>\n",
-       "      <td>1.745413</td>\n",
-       "      <td>0.364244</td>\n",
-       "      <td>1.406744</td>\n",
-       "      <td>0.415499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>47</td>\n",
-       "      <td>Q11399</td>\n",
-       "      <td>Q8341</td>\n",
-       "      <td>Q373342</td>\n",
-       "      <td>rock music</td>\n",
-       "      <td>popular music</td>\n",
-       "      <td>jazz</td>\n",
-       "      <td>rock music is popular music</td>\n",
-       "      <td>jazz is popular music</td>\n",
-       "      <td>rock music and jazz are examples of popular music</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.424096</td>\n",
-       "      <td>0.566754</td>\n",
-       "      <td>0.935759</td>\n",
-       "      <td>[0.113906465, -0.3389766, 0.10356339, 0.254968...</td>\n",
-       "      <td>[-0.10467594, -0.56408674, 0.6394385, -0.28194...</td>\n",
-       "      <td>0.648035</td>\n",
-       "      <td>1.696421</td>\n",
-       "      <td>0.370862</td>\n",
-       "      <td>1.465125</td>\n",
-       "      <td>0.405659</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>127</td>\n",
-       "      <td>Q12097</td>\n",
-       "      <td>Q16511993</td>\n",
-       "      <td>Q116</td>\n",
-       "      <td>king</td>\n",
-       "      <td>monarch</td>\n",
-       "      <td>queen</td>\n",
-       "      <td>king is monarch</td>\n",
-       "      <td>queen is monarch</td>\n",
-       "      <td>king and queen are examples of monarch</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.699062</td>\n",
-       "      <td>0.718037</td>\n",
-       "      <td>0.894225</td>\n",
-       "      <td>[0.5253047, 0.57659674, 1.4201468, 0.8925513, ...</td>\n",
-       "      <td>[0.26084498, 0.3714701, 1.3805685, 0.7634336, ...</td>\n",
-       "      <td>0.865548</td>\n",
-       "      <td>2.309254</td>\n",
-       "      <td>0.302183</td>\n",
-       "      <td>1.412110</td>\n",
-       "      <td>0.414575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>182</td>\n",
-       "      <td>Q1308677</td>\n",
-       "      <td>Q21171241</td>\n",
-       "      <td>Q1190554</td>\n",
-       "      <td>deployment</td>\n",
-       "      <td>occurrence</td>\n",
-       "      <td>departure</td>\n",
-       "      <td>deployment is occurrence</td>\n",
-       "      <td>departure is occurrence</td>\n",
-       "      <td>deployment and departure are examples of occur...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.225839</td>\n",
-       "      <td>0.496331</td>\n",
-       "      <td>0.030801</td>\n",
-       "      <td>[0.26534072, -0.9580938, 2.5264893, 0.30084807...</td>\n",
-       "      <td>[-0.32454324, -1.2765993, 2.5993214, 0.3582288...</td>\n",
-       "      <td>0.750943</td>\n",
-       "      <td>1.977621</td>\n",
-       "      <td>0.335839</td>\n",
-       "      <td>1.738580</td>\n",
-       "      <td>0.365153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>580</td>\n",
-       "      <td>Q289</td>\n",
-       "      <td>Q872</td>\n",
-       "      <td>Q15078788</td>\n",
-       "      <td>television</td>\n",
-       "      <td>broadcasting</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>television is broadcasting</td>\n",
-       "      <td>radio is broadcasting</td>\n",
-       "      <td>television and radio are examples of broadcasting</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.502157</td>\n",
-       "      <td>0.758809</td>\n",
-       "      <td>0.475667</td>\n",
-       "      <td>[1.1445429, -0.7258359, 1.4580723, 0.4434381, ...</td>\n",
-       "      <td>[0.56205714, -0.253638, 1.5891607, 0.01657881,...</td>\n",
-       "      <td>0.555305</td>\n",
-       "      <td>1.880143</td>\n",
-       "      <td>0.347205</td>\n",
-       "      <td>2.104690</td>\n",
-       "      <td>0.322093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>666</td>\n",
-       "      <td>Q374</td>\n",
-       "      <td>Q959362</td>\n",
-       "      <td>Q56139</td>\n",
-       "      <td>vodka</td>\n",
-       "      <td>liquor</td>\n",
-       "      <td>gin</td>\n",
-       "      <td>vodka is liquor</td>\n",
-       "      <td>gin is liquor</td>\n",
-       "      <td>vodka and gin are examples of liquor</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.714010</td>\n",
-       "      <td>0.827655</td>\n",
-       "      <td>0.915257</td>\n",
-       "      <td>[-0.06934731, 0.106110536, 0.46463186, 0.45913...</td>\n",
-       "      <td>[-0.019106973, 0.14095607, 0.63778424, 0.74826...</td>\n",
-       "      <td>0.898337</td>\n",
-       "      <td>1.970335</td>\n",
-       "      <td>0.336662</td>\n",
-       "      <td>1.810370</td>\n",
-       "      <td>0.355825</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>848</td>\n",
-       "      <td>Q58893412</td>\n",
-       "      <td>Q846570</td>\n",
-       "      <td>Q215627</td>\n",
-       "      <td>Japanese</td>\n",
-       "      <td>person</td>\n",
-       "      <td>Americans</td>\n",
-       "      <td>Japanese is person</td>\n",
-       "      <td>Americans is person</td>\n",
-       "      <td>Japanese and Americans are examples of person</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.501314</td>\n",
-       "      <td>0.589988</td>\n",
-       "      <td>0.905763</td>\n",
-       "      <td>[-0.30371594, -0.29769143, -0.008753488, 0.090...</td>\n",
-       "      <td>[-0.35450095, -0.107045874, 0.46675515, 0.1212...</td>\n",
-       "      <td>0.664395</td>\n",
-       "      <td>2.207857</td>\n",
-       "      <td>0.311735</td>\n",
-       "      <td>1.799410</td>\n",
-       "      <td>0.357218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>849</td>\n",
-       "      <td>Q58893412</td>\n",
-       "      <td>Q846570</td>\n",
-       "      <td>Q2472587</td>\n",
-       "      <td>Japanese</td>\n",
-       "      <td>people</td>\n",
-       "      <td>Americans</td>\n",
-       "      <td>Japanese is people</td>\n",
-       "      <td>Americans is people</td>\n",
-       "      <td>Japanese and Americans are examples of people</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.501314</td>\n",
-       "      <td>0.589988</td>\n",
-       "      <td>0.905763</td>\n",
-       "      <td>[-0.050824374, -0.25418323, -0.12348578, -0.00...</td>\n",
-       "      <td>[-0.05350799, -0.10230911, 0.53688955, -0.0150...</td>\n",
-       "      <td>0.628451</td>\n",
-       "      <td>1.883196</td>\n",
-       "      <td>0.346837</td>\n",
-       "      <td>1.828021</td>\n",
-       "      <td>0.353604</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index      node1      node2     parent   node1_label           par_label  \\\n",
-       "0      5      Q1074      Q7364  Q24060765          skin        animal organ   \n",
-       "1     12   Q1079023    Q500834  Q13406554  championship  sports competition   \n",
-       "2     47     Q11399      Q8341    Q373342    rock music       popular music   \n",
-       "3    127     Q12097  Q16511993       Q116          king             monarch   \n",
-       "4    182   Q1308677  Q21171241   Q1190554    deployment          occurrence   \n",
-       "5    580       Q289       Q872  Q15078788    television        broadcasting   \n",
-       "6    666       Q374    Q959362     Q56139         vodka              liquor   \n",
-       "7    848  Q58893412    Q846570    Q215627      Japanese              person   \n",
-       "8    849  Q58893412    Q846570   Q2472587      Japanese              people   \n",
-       "\n",
-       "  node2_label                          node1_sent  \\\n",
-       "0         eye                skin is animal organ   \n",
-       "1  tournament  championship is sports competition   \n",
-       "2        jazz         rock music is popular music   \n",
-       "3       queen                     king is monarch   \n",
-       "4   departure            deployment is occurrence   \n",
-       "5       radio          television is broadcasting   \n",
-       "6         gin                     vodka is liquor   \n",
-       "7   Americans                  Japanese is person   \n",
-       "8   Americans                  Japanese is people   \n",
-       "\n",
-       "                         node2_sent  \\\n",
-       "0               eye is animal organ   \n",
-       "1  tournament is sports competition   \n",
-       "2             jazz is popular music   \n",
-       "3                  queen is monarch   \n",
-       "4           departure is occurrence   \n",
-       "5             radio is broadcasting   \n",
-       "6                     gin is liquor   \n",
-       "7               Americans is person   \n",
-       "8               Americans is people   \n",
-       "\n",
-       "                                           comb_sent  ...    transe      text  \\\n",
-       "0          skin and eye are examples of animal organ  ...  0.485879  0.611496   \n",
-       "1  championship and tournament are examples of sp...  ...  0.628038  0.838909   \n",
-       "2  rock music and jazz are examples of popular music  ...  0.424096  0.566754   \n",
-       "3             king and queen are examples of monarch  ...  0.699062  0.718037   \n",
-       "4  deployment and departure are examples of occur...  ...  0.225839  0.496331   \n",
-       "5  television and radio are examples of broadcasting  ...  0.502157  0.758809   \n",
-       "6               vodka and gin are examples of liquor  ...  0.714010  0.827655   \n",
-       "7      Japanese and Americans are examples of person  ...  0.501314  0.589988   \n",
-       "8      Japanese and Americans are examples of people  ...  0.501314  0.589988   \n",
-       "\n",
-       "      class                                          node1_emb  \\\n",
-       "0  0.541116  [0.72735953, -0.5447348, 0.95869535, 0.6319232...   \n",
-       "1  0.899429  [0.083933495, 0.082726024, 1.7943382, 0.518000...   \n",
-       "2  0.935759  [0.113906465, -0.3389766, 0.10356339, 0.254968...   \n",
-       "3  0.894225  [0.5253047, 0.57659674, 1.4201468, 0.8925513, ...   \n",
-       "4  0.030801  [0.26534072, -0.9580938, 2.5264893, 0.30084807...   \n",
-       "5  0.475667  [1.1445429, -0.7258359, 1.4580723, 0.4434381, ...   \n",
-       "6  0.915257  [-0.06934731, 0.106110536, 0.46463186, 0.45913...   \n",
-       "7  0.905763  [-0.30371594, -0.29769143, -0.008753488, 0.090...   \n",
-       "8  0.905763  [-0.050824374, -0.25418323, -0.12348578, -0.00...   \n",
-       "\n",
-       "                                           node2_emb bert2SentSim   gpt2Ppl  \\\n",
-       "0  [0.755933, 0.04024082, 0.9340492, 0.41963616, ...     0.855939  2.035088   \n",
-       "1  [0.53537947, -0.30064347, 2.2253025, 0.4019227...     0.891540  1.745413   \n",
-       "2  [-0.10467594, -0.56408674, 0.6394385, -0.28194...     0.648035  1.696421   \n",
-       "3  [0.26084498, 0.3714701, 1.3805685, 0.7634336, ...     0.865548  2.309254   \n",
-       "4  [-0.32454324, -1.2765993, 2.5993214, 0.3582288...     0.750943  1.977621   \n",
-       "5  [0.56205714, -0.253638, 1.5891607, 0.01657881,...     0.555305  1.880143   \n",
-       "6  [-0.019106973, 0.14095607, 0.63778424, 0.74826...     0.898337  1.970335   \n",
-       "7  [-0.35450095, -0.107045874, 0.46675515, 0.1212...     0.664395  2.207857   \n",
-       "8  [-0.05350799, -0.10230911, 0.53688955, -0.0150...     0.628451  1.883196   \n",
-       "\n",
-       "   gpt2Ppl_normed  bert_ppl  bert_ppl_normed  \n",
-       "0        0.329480  2.064750         0.326291  \n",
-       "1        0.364244  1.406744         0.415499  \n",
-       "2        0.370862  1.465125         0.405659  \n",
-       "3        0.302183  1.412110         0.414575  \n",
-       "4        0.335839  1.738580         0.365153  \n",
-       "5        0.347205  2.104690         0.322093  \n",
-       "6        0.336662  1.810370         0.355825  \n",
-       "7        0.311735  1.799410         0.357218  \n",
-       "8        0.346837  1.828021         0.353604  \n",
-       "\n",
-       "[9 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "newPairsDF3wordsimed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "id": "naked-birth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.45732956038002354, pvalue=0.09169028154942921)"
-      ]
-     },
-     "execution_count": 241,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(newPairsDF3wordsimed['bert2SentSim'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "id": "announced-median",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.6218706970438532, pvalue=0.0737649506248493)"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(newPairsDF3wordsimed['bert2SentSim'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "id": "dominican-botswana",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.5716619504750294, pvalue=0.035014981019662494)"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(newPairsDF3wordsimed['bert_ppl_normed'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "id": "ranking-valentine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.6975036196572949, pvalue=0.036722324744514964)"
-      ]
-     },
-     "execution_count": 244,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(newPairsDF3wordsimed['bert_ppl_normed'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "id": "sealed-motion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.22866478019001177, pvalue=0.3990751965482373)"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(newPairsDF3wordsimed['gpt2Ppl_normed'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "id": "median-palestine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.2773207162492859, pvalue=0.4700126440526218)"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(newPairsDF3wordsimed['gpt2Ppl_normed'], newPairsDF3wordsimed['Human (Mean)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cleared-appreciation",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "magnetic-incentive",
-   "metadata": {},
-   "source": [
-    "## Creating wordsim word pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "silent-march",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279wordsimed.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "infinite-steering",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "environmental-interim",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sentMapper = {node: sent for (node, sent) in zip(P279wordsimed.node1.to_list(), P279wordsimed.sent.to_list())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "horizontal-james",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF['word1_sent'] = wordSimDF.word1_kg_id.apply(lambda p: sentMapper[p] if p in sentMapper else None)\n",
-    "wordSimDF['word2_sent'] = wordSimDF.word2_kg_id.apply(lambda p: sentMapper[p] if p in sentMapper else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "global-cooking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF.word1_sent.isna().sum(), wordSimDF.word2_sent.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "alive-safety",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF1 = wordSimDF[~wordSimDF.word1_sent.isna() & ~wordSimDF.word2_sent.isna()].reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "standing-engineering",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF1['word1_emb'] = pd.Series(list(getSentEmbeddings(wordSimDF1.word1_sent, 'bert-base-nli-mean-tokens')))\n",
-    "wordSimDF1['word2_emb'] = pd.Series(list(getSentEmbeddings(wordSimDF1.word2_sent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "italic-subscriber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF1['bertChildParSim'] = wordSimDF1.apply(lambda p: cosine_similarity(p.word1_emb.reshape(1,-1), p.word2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "linear-mother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(wordSimDF1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "built-ordinance",
-   "metadata": {},
-   "source": [
-    "# Wordsim words seed based extraction from Wordsim 353 - Amandeep's file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "distant-declaration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "confirmed-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimDF = pd.read_csv(\"../data/wordsim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "chubby-table",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "common-making",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "349"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "photographic-doctrine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimWordPairsSet = set(wordSimDF.apply(lambda p: (p.word1_kg_id, p.word2_kg_id), axis=1).tolist())\n",
-    "wordSimWordsSet = set(wordSimDF.word1_kg_id.tolist() + wordSimDF.word2_kg_id.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "suspended-flooring",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "422"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimWordsSet)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "material-sessions",
-   "metadata": {},
-   "source": [
-    "## P279 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "nutritional-brazil",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q17372279-P279-Q100026-beba8cd1-0</td>\n",
-       "      <td>Q17372279</td>\n",
-       "      <td>YF-16</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>YF-16 is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q17372377-P279-Q100026-fd42bd71-0</td>\n",
-       "      <td>Q17372377</td>\n",
-       "      <td>F-16A/B Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16A/B Fighting Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q17372444-P279-Q100026-ca0fc4bd-0</td>\n",
-       "      <td>Q17372444</td>\n",
-       "      <td>F-16C/D Fighting Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16C/D Fighting Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q17372455-P279-Q100026-c2b1bf36-0</td>\n",
-       "      <td>Q17372455</td>\n",
-       "      <td>F-16E/F Desert Falcon</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16E/F Desert Falcon is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q2029940-P279-Q100026-ceba4380-0</td>\n",
-       "      <td>Q2029940</td>\n",
-       "      <td>F-16 VISTA</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100026</td>\n",
-       "      <td>F-16 Fighting Falcon</td>\n",
-       "      <td>F-16 VISTA is F-16 Fighting Falcon</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  id      node1              node1_label  \\\n",
-       "1  Q17372279-P279-Q100026-beba8cd1-0  Q17372279                    YF-16   \n",
-       "2  Q17372377-P279-Q100026-fd42bd71-0  Q17372377  F-16A/B Fighting Falcon   \n",
-       "3  Q17372444-P279-Q100026-ca0fc4bd-0  Q17372444  F-16C/D Fighting Falcon   \n",
-       "4  Q17372455-P279-Q100026-c2b1bf36-0  Q17372455    F-16E/F Desert Falcon   \n",
-       "5   Q2029940-P279-Q100026-ceba4380-0   Q2029940               F-16 VISTA   \n",
-       "\n",
-       "  label    node2           node2_label  \\\n",
-       "1  P279  Q100026  F-16 Fighting Falcon   \n",
-       "2  P279  Q100026  F-16 Fighting Falcon   \n",
-       "3  P279  Q100026  F-16 Fighting Falcon   \n",
-       "4  P279  Q100026  F-16 Fighting Falcon   \n",
-       "5  P279  Q100026  F-16 Fighting Falcon   \n",
-       "\n",
-       "                                              sent  \n",
-       "1                    YF-16 is F-16 Fighting Falcon  \n",
-       "2  F-16A/B Fighting Falcon is F-16 Fighting Falcon  \n",
-       "3  F-16C/D Fighting Falcon is F-16 Fighting Falcon  \n",
-       "4    F-16E/F Desert Falcon is F-16 Fighting Falcon  \n",
-       "5               F-16 VISTA is F-16 Fighting Falcon  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "satisfactory-profile",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "620830"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(temp3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "casual-dublin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter1(row):\n",
-    "    return row.node1 in wordSimWordsSet or row.node2 in wordSimWordsSet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "advised-photography",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF = temp3[temp3.apply(filter1, axis=1)]\n",
-    "# p279WordSimSeededDF = temp3[temp3.apply(filter1, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "alien-davis",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using our wordsim seed of size 422, we are using the dataset of 620830 having P279 relations and both nodes having a label. This dataset gets reduced to a size of 19454\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Using our wordsim seed of size {len(wordSimWordsSet)}, we are using the dataset of {len(temp3)} having P279 relations and both nodes having a label. This dataset gets reduced to a size of {len(p279WordSimSeededDF)}\")\n",
-    "      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "sacred-egyptian",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>211</th>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3495</th>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3827</th>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4267</th>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       id     node1 node1_label label  \\\n",
-       "160   Q1503443-P279-Q100171002-8282ae74-0  Q1503443     secrecy  P279   \n",
-       "211       Q49850-P279-Q1002697-0c52cf68-0    Q49850     journal  P279   \n",
-       "3495        Q4421-P279-Q101998-d0983886-0     Q4421      forest  P279   \n",
-       "3827        Q2294-P279-Q102165-0893a180-0     Q2294      proton  P279   \n",
-       "4267       Q11435-P279-Q102205-7b448d05-0    Q11435      liquid  P279   \n",
-       "\n",
-       "           node2  node2_label                    sent  \n",
-       "160   Q100171002  concealment  secrecy is concealment  \n",
-       "211     Q1002697   periodical   journal is periodical  \n",
-       "3495     Q101998        biome         forest is biome  \n",
-       "3827     Q102165      nucleon       proton is nucleon  \n",
-       "4267     Q102205        fluid         liquid is fluid  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "virgin-decrease",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19454"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279WordSimSeededDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "nasty-ready",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279S_Set = set(p279WordSimSeededDF.node1.to_list() + p279WordSimSeededDF.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "breeding-egyptian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "front-theater",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "296"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimDF.apply(lambda p: p.word1_kg_id in p279S_Set and p.word2_kg_id in p279S_Set, axis=1).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "nearby-oakland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF = p279WordSimSeededDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "coral-auction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "passive-michigan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "elegant-wiring",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# getSentEmbeddings('secrecy', 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "abroad-youth",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15.729505062103271 s\n",
-      "6.890929937362671 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF['node1_emb'] = pd.Series(list(getSentEmbeddings(p279WordSimSeededDF.node1_label, 'bert-base-nli-mean-tokens')))\n",
-    "p279WordSimSeededDF['node2_emb'] = pd.Series(list(getSentEmbeddings(p279WordSimSeededDF.node2_label, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "portuguese-yugoslavia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF['bert2SentSim'] = p279WordSimSeededDF.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "expressed-ancient",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
-    "# # build gpt model\n",
-    "# model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
-    "# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
-    "\n",
-    "# def gpt_score(sent1):\n",
-    "#     # input sentence tensor\n",
-    "#     # output score\n",
-    "#     tokens_tensor = tokenizer.encode(sent1, add_special_tokens=False, return_tensors=\"pt\")\n",
-    "#     loss = model(tokens_tensor, labels=tokens_tensor)[0]\n",
-    "# #     print(tokens_tensor.size(1))\n",
-    "#     return np.exp(loss.cpu().detach().numpy() / tokens_tensor.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "lyric-cooperation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279WordSimSeededDF['gpt2Ppl'] = p279WordSimSeededDF.sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "regular-burton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279WordSimSeededDF['gpt2Ppl_normed'] = p279WordSimSeededDF['gpt2Ppl'].apply(lambda p: 1/(1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "dominican-endorsement",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19454"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279WordSimSeededDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "norwegian-relaxation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF.to_csv('../data/P279_dataset/P279_19k_ChildPar_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "capable-thermal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279WordSimSeededDF.iloc[sample_without_replacement(len(p279WordSimSeededDF), 50, random_state=13)][['node1', 'node1_label', 'node2', 'node2_label']].to_csv('../data/output/P279Sim_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "enhanced-trash",
-   "metadata": {},
-   "source": [
-    "## P279 Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "retained-edwards",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_Siblings_left = p279WordSimSeededDF.copy()\n",
-    "p279WordSimSeededDF_Siblings_right = p279WordSimSeededDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "separate-basics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF = p279WordSimSeededDF_Siblings_left.set_index('node2').join(p279WordSimSeededDF_Siblings_right.set_index('node2'),rsuffix='_right').reset_index()\n",
-    "p279Seeded_SiblingsDF['set_nodes'] = p279Seeded_SiblingsDF.apply(lambda p: str(sorted([p.node1, p.node1_right]) + [p.node2]), axis=1)\n",
-    "p279Seeded_SiblingsDF = p279Seeded_SiblingsDF[~p279Seeded_SiblingsDF['set_nodes'].duplicated()]\n",
-    "p279Seeded_SiblingsDF['comb_sent'] = p279Seeded_SiblingsDF.apply(lambda p: p.node2_label + ' is typically ' + p.node1_label + ' or ' + p.node1_label_right, axis=1)\n",
-    "p279Seeded_SiblingsDF1 = p279Seeded_SiblingsDF[['node1', 'node2', 'node1_right', 'node1_label', 'node2_label', 'node1_label_right', 'sent', 'sent_right', 'comb_sent']]\n",
-    "p279Seeded_SiblingsDF2 = p279Seeded_SiblingsDF1.rename(columns={'node1_right': 'node2', 'node2': 'parent', 'node1_label_right': 'node2_label', 'node2_label': 'par_label', 'sent': 'node1_sent', 'sent_right': 'node2_sent'})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "adjusted-karaoke",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>concealment is typically secrecy or secrecy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>periodical is typically journal or journal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>biome is typically forest or forest</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>nucleon is typically proton or proton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>fluid is typically liquid or liquid</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1      parent     node2 node1_label    par_label node2_label  \\\n",
-       "0  Q1503443  Q100171002  Q1503443     secrecy  concealment     secrecy   \n",
-       "1    Q49850    Q1002697    Q49850     journal   periodical     journal   \n",
-       "2     Q4421     Q101998     Q4421      forest        biome      forest   \n",
-       "3     Q2294     Q102165     Q2294      proton      nucleon      proton   \n",
-       "4    Q11435     Q102205    Q11435      liquid        fluid      liquid   \n",
-       "\n",
-       "               node1_sent              node2_sent  \\\n",
-       "0  secrecy is concealment  secrecy is concealment   \n",
-       "1   journal is periodical   journal is periodical   \n",
-       "2         forest is biome         forest is biome   \n",
-       "3       proton is nucleon       proton is nucleon   \n",
-       "4         liquid is fluid         liquid is fluid   \n",
-       "\n",
-       "                                     comb_sent  \n",
-       "0  concealment is typically secrecy or secrecy  \n",
-       "1   periodical is typically journal or journal  \n",
-       "2          biome is typically forest or forest  \n",
-       "3        nucleon is typically proton or proton  \n",
-       "4          fluid is typically liquid or liquid  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "growing-ethics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF2 = p279Seeded_SiblingsDF2[p279Seeded_SiblingsDF2.node1 != p279Seeded_SiblingsDF2.node2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "selective-consideration",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5948051"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279Seeded_SiblingsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "binding-reason",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279Seeded_SiblingsDF2.iloc[sample_without_replacement(len(p279Seeded_SiblingsDF2), 250, random_state=13)][['node1', 'node1_label', 'node2', 'node2_label']].to_csv('../data/output/P279SiblingsSim_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "sustainable-hughes",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def validateSeeders(row):\n",
-    "    return not(row.node1 in wordSimWordsSet or row.node2 in wordSimWordsSet or row.parent in wordSimWordsSet)\n",
-    "p279Seeded_SiblingsDF2.apply(validateSeeders, axis=1).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "loaded-success",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3 = p279Seeded_SiblingsDF2.iloc[sample_without_replacement(len(p279Seeded_SiblingsDF2), len(p279WordSimSeededDF), random_state=13)]\n",
-    "                                                       "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "tribal-absolute",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3 = p279Seeded_SiblingsDF3.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "democratic-island",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7.479024887084961 s\n",
-      "7.432664394378662 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3['node1_emb'] = pd.Series(list(getSentEmbeddings(p279Seeded_SiblingsDF3.node1_sent, 'bert-base-nli-mean-tokens')))\n",
-    "p279Seeded_SiblingsDF3['node2_emb'] = pd.Series(list(getSentEmbeddings(p279Seeded_SiblingsDF3.node2_sent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "numerical-delicious",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3['bert2SentSim'] = p279Seeded_SiblingsDF3.apply(lambda p: cosine_similarity(p.node1_emb.reshape(1,-1), p.node2_emb.reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "seeing-manner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19454"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(p279Seeded_SiblingsDF3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "collectible-worry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279Seeded_SiblingsDF3.to_csv('../data/P279_dataset/P279_19k_Siblings_Dataset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "special-combination",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p279Seeded_SiblingsDF2['gpt2Ppl'] = p279Seeded_SiblingsDF2.comb_sent.apply(gpt_score)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "several-survey",
-   "metadata": {},
-   "source": [
-    "# Annotated P279s datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "flexible-supplement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "p279_ann_DF = pd.read_csv(\"../data/P279_dataset/P279Sim_annotated.csv\")\n",
-    "p279Sib_ann_DF = pd.read_csv(\"../data/P279_dataset/P279SiblingSim_annotated.csv\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "working-basics",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>Sim</th>\n",
-       "      <th>Dim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q48842375</td>\n",
-       "      <td>Asian Women\\'s Softball Championship</td>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q8001434</td>\n",
-       "      <td>Wildlife of South Africa</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>fauna</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q40189366</td>\n",
-       "      <td>interstellar radiation field</td>\n",
-       "      <td>Q11379</td>\n",
-       "      <td>energy</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q2162453</td>\n",
-       "      <td>traditional political party</td>\n",
-       "      <td>Q7278</td>\n",
-       "      <td>political party</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q6786579</td>\n",
-       "      <td>materials physics</td>\n",
-       "      <td>Q413</td>\n",
-       "      <td>physics</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1                           node1_label     node2      node2_label  \\\n",
-       "0  Q48842375  Asian Women\\'s Softball Championship  Q1079023     championship   \n",
-       "1   Q8001434              Wildlife of South Africa   Q104021            fauna   \n",
-       "2  Q40189366          interstellar radiation field    Q11379           energy   \n",
-       "3   Q2162453           traditional political party     Q7278  political party   \n",
-       "4   Q6786579                     materials physics      Q413          physics   \n",
-       "\n",
-       "   Sim Dim  \n",
-       "0  2.0   I  \n",
-       "1  2.0   I  \n",
-       "2  2.0   I  \n",
-       "3  1.0   I  \n",
-       "4  2.0   I  "
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_ann_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "ahead-indonesia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>Sim</th>\n",
-       "      <th>Dim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q4165229</td>\n",
-       "      <td>gain</td>\n",
-       "      <td>Q570938</td>\n",
-       "      <td>net income</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q30461</td>\n",
-       "      <td>president</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q381072</td>\n",
-       "      <td>crisis</td>\n",
-       "      <td>Q3839081</td>\n",
-       "      <td>disaster</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q39546</td>\n",
-       "      <td>tool</td>\n",
-       "      <td>Q7397</td>\n",
-       "      <td>software</td>\n",
-       "      <td>2</td>\n",
-       "      <td>H</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1400881</td>\n",
-       "      <td>property</td>\n",
-       "      <td>Q2574811</td>\n",
-       "      <td>cause</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      node1 node1_label     node2    node2_label  Sim  Dim\n",
-       "0  Q4165229        gain   Q570938     net income    2    S\n",
-       "1    Q30461   president    Q48352  head of state    1    S\n",
-       "2   Q381072      crisis  Q3839081       disaster    1  NaN\n",
-       "3    Q39546        tool     Q7397       software    2    H\n",
-       "4  Q1400881    property  Q2574811          cause    4  NaN"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Sib_ann_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-interval",
-   "metadata": {},
-   "source": [
-    "## P279 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "ordered-teacher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_ann_DF1 = p279_ann_DF.set_index(['node1', 'node2']).join(p279WordSimSeededDF.set_index(['node1', 'node2']), rsuffix='_og').reset_index().drop(columns=['node2_label_og', 'node1_label_og', 'label'])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "parallel-jerusalem",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_ann_DF1 = p279_ann_DF1[~p279_ann_DF1.Sim.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "defensive-processor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pytorch_pretrained_bert import BertTokenizer,BertForMaskedLM\n",
-    "import torch\n",
-    "import pandas as pd\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "committed-cyprus",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 407873900/407873900 [00:37<00:00, 11005250.37B/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertMaskedLM = BertForMaskedLM.from_pretrained('bert-base-uncased')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "alleged-malaysia",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 231508/231508 [00:00<00:00, 902966.47B/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "annual-lincoln",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bert_score(sentence):\n",
-    "    tokenize_input = tokenizer.tokenize(sentence)\n",
-    "    tensor_input = torch.tensor([tokenizer.convert_tokens_to_ids(tokenize_input)])\n",
-    "    predictions=bertMaskedLM(tensor_input)\n",
-    "    loss_fct = torch.nn.CrossEntropyLoss()\n",
-    "    loss = loss_fct(predictions.squeeze(),tensor_input.squeeze()).data \n",
-    "    return math.exp(loss / tensor_input.size(1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "disturbed-omaha",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_ann_DF1['bert_ppl'] = p279_ann_DF1.sent.apply(get_gpt_score)\n",
-    "p279_ann_DF1['bert_ppl_normed'] = p279_ann_DF1['bert_ppl'].apply(lambda p: 4 - 3 / (1+p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "brief-mounting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279_ann_DF1['bert2SentSim'] = p279_ann_DF1['bert2SentSim'].apply(lambda p: 4 - p*3)\n",
-    "p279_ann_DF1['gpt2Ppl_normed'] = p279_ann_DF1['gpt2Ppl_normed'].apply(lambda p: 4 - p*3)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "renewable-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>Sim</th>\n",
-       "      <th>Dim</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>gpt2Ppl</th>\n",
-       "      <th>gpt2Ppl_normed</th>\n",
-       "      <th>bert_ppl</th>\n",
-       "      <th>bert_ppl_normed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q1125755</td>\n",
-       "      <td>Q11410</td>\n",
-       "      <td>Linux gaming</td>\n",
-       "      <td>game</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>29906.0</td>\n",
-       "      <td>Q1125755-P279-Q11410-c6c8e293-0</td>\n",
-       "      <td>Linux gaming is game</td>\n",
-       "      <td>[-0.38090447, -0.072453715, 1.0361495, -0.0768...</td>\n",
-       "      <td>[0.049388338, -0.96375394, 2.6406975, 0.740393...</td>\n",
-       "      <td>2.802844</td>\n",
-       "      <td>5.629106</td>\n",
-       "      <td>3.547450</td>\n",
-       "      <td>4.126195</td>\n",
-       "      <td>1.393231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q12097</td>\n",
-       "      <td>Q8441</td>\n",
-       "      <td>king</td>\n",
-       "      <td>man</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>689705.0</td>\n",
-       "      <td>Q12097-P279-Q8441-5aee777a-0</td>\n",
-       "      <td>king is man</td>\n",
-       "      <td>[0.39023998, 0.35056773, 1.8369316, 0.78192997...</td>\n",
-       "      <td>[0.30512533, -0.11468178, 2.2824647, 0.7221913...</td>\n",
-       "      <td>1.907925</td>\n",
-       "      <td>9.925628</td>\n",
-       "      <td>3.725416</td>\n",
-       "      <td>3.514388</td>\n",
-       "      <td>1.251559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q1262537</td>\n",
-       "      <td>Q17517</td>\n",
-       "      <td>dual SIM</td>\n",
-       "      <td>mobile phone</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>H</td>\n",
-       "      <td>189153.0</td>\n",
-       "      <td>Q1262537-P279-Q17517-5c20a786-0</td>\n",
-       "      <td>dual SIM is mobile phone</td>\n",
-       "      <td>[-0.2609788, -0.645304, 0.78149086, 0.5846581,...</td>\n",
-       "      <td>[0.18702456, 0.18690406, 1.2201738, -0.1322266...</td>\n",
-       "      <td>2.485325</td>\n",
-       "      <td>3.024970</td>\n",
-       "      <td>3.254653</td>\n",
-       "      <td>3.949115</td>\n",
-       "      <td>1.597018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1362373</td>\n",
-       "      <td>Q16334298</td>\n",
-       "      <td>Cultigen</td>\n",
-       "      <td>group of living things</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>172384.0</td>\n",
-       "      <td>Q1362373-P279-Q16334298-91d03ec5-0</td>\n",
-       "      <td>Cultigen is group of living things</td>\n",
-       "      <td>[-0.2755343, -0.1904294, 1.8262575, 0.18963182...</td>\n",
-       "      <td>[1.1029037, 0.13540803, 1.1756531, 0.5193954, ...</td>\n",
-       "      <td>2.291170</td>\n",
-       "      <td>2.411533</td>\n",
-       "      <td>3.120630</td>\n",
-       "      <td>1.749629</td>\n",
-       "      <td>1.680036</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q14328596</td>\n",
-       "      <td>Q11004</td>\n",
-       "      <td>cabbage</td>\n",
-       "      <td>vegetable</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>20311.0</td>\n",
-       "      <td>Q14328596-P279-Q11004-4dce84cb-0</td>\n",
-       "      <td>cabbage is vegetable</td>\n",
-       "      <td>[-0.2028479, 0.33753565, 0.864861, -0.3964703,...</td>\n",
-       "      <td>[-0.24074318, -0.59905654, 1.2529583, 0.276870...</td>\n",
-       "      <td>1.605159</td>\n",
-       "      <td>8.393172</td>\n",
-       "      <td>3.680619</td>\n",
-       "      <td>16.203755</td>\n",
-       "      <td>1.288651</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node1      node2   node1_label             node2_label  Sim Dim  \\\n",
-       "0   Q1125755     Q11410  Linux gaming                    game  2.0   I   \n",
-       "1     Q12097      Q8441          king                     man  4.0   I   \n",
-       "2   Q1262537     Q17517      dual SIM            mobile phone  3.0   H   \n",
-       "3   Q1362373  Q16334298      Cultigen  group of living things  3.0   I   \n",
-       "4  Q14328596     Q11004       cabbage               vegetable  2.0   I   \n",
-       "\n",
-       "      index                                  id  \\\n",
-       "0   29906.0     Q1125755-P279-Q11410-c6c8e293-0   \n",
-       "1  689705.0        Q12097-P279-Q8441-5aee777a-0   \n",
-       "2  189153.0     Q1262537-P279-Q17517-5c20a786-0   \n",
-       "3  172384.0  Q1362373-P279-Q16334298-91d03ec5-0   \n",
-       "4   20311.0    Q14328596-P279-Q11004-4dce84cb-0   \n",
-       "\n",
-       "                                 sent  \\\n",
-       "0                Linux gaming is game   \n",
-       "1                         king is man   \n",
-       "2            dual SIM is mobile phone   \n",
-       "3  Cultigen is group of living things   \n",
-       "4                cabbage is vegetable   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [-0.38090447, -0.072453715, 1.0361495, -0.0768...   \n",
-       "1  [0.39023998, 0.35056773, 1.8369316, 0.78192997...   \n",
-       "2  [-0.2609788, -0.645304, 0.78149086, 0.5846581,...   \n",
-       "3  [-0.2755343, -0.1904294, 1.8262575, 0.18963182...   \n",
-       "4  [-0.2028479, 0.33753565, 0.864861, -0.3964703,...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim   gpt2Ppl  \\\n",
-       "0  [0.049388338, -0.96375394, 2.6406975, 0.740393...      2.802844  5.629106   \n",
-       "1  [0.30512533, -0.11468178, 2.2824647, 0.7221913...      1.907925  9.925628   \n",
-       "2  [0.18702456, 0.18690406, 1.2201738, -0.1322266...      2.485325  3.024970   \n",
-       "3  [1.1029037, 0.13540803, 1.1756531, 0.5193954, ...      2.291170  2.411533   \n",
-       "4  [-0.24074318, -0.59905654, 1.2529583, 0.276870...      1.605159  8.393172   \n",
-       "\n",
-       "   gpt2Ppl_normed   bert_ppl  bert_ppl_normed  \n",
-       "0        3.547450   4.126195         1.393231  \n",
-       "1        3.725416   3.514388         1.251559  \n",
-       "2        3.254653   3.949115         1.597018  \n",
-       "3        3.120630   1.749629         1.680036  \n",
-       "4        3.680619  16.203755         1.288651  "
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279_ann_DF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "front-dance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "lovely-fraction",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=-0.34042801211515844, pvalue=0.09588091799975365)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(p279_ann_DF1.Sim, p279_ann_DF1.bert_ppl_normed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "pursuant-angel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.04421700535669775, pvalue=0.8337702651869092)"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(p279_ann_DF1.Sim, p279_ann_DF1.bert2SentSim)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "african-standard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.34042801211515844, pvalue=0.09588091799975365)"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(p279_ann_DF1.Sim, p279_ann_DF1.gpt2Ppl_normed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "metric-essex",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "362px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/PyKeen Experiments.ipynb b/PyKeen Experiments.ipynb
deleted file mode 100644
index 83ee2e9..0000000
--- a/PyKeen Experiments.ipynb	
+++ /dev/null
@@ -1,455 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "armed-stewart",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pykeen.pipeline import pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "frozen-compound",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No random seed is specified. Setting to 3177612343.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d77c3efdf45c4a1689aa9931034c9ccf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training epochs on cuda:   0%|          | 0/5 [00:00<?, ?epoch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training batches on cuda:   0%|          | 0/7 [00:00<?, ?batch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training batches on cuda:   0%|          | 0/7 [00:00<?, ?batch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training batches on cuda:   0%|          | 0/7 [00:00<?, ?batch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training batches on cuda:   0%|          | 0/7 [00:00<?, ?batch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training batches on cuda:   0%|          | 0/7 [00:00<?, ?batch/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pykeen.evaluation.evaluator:Starting batch_size search for evaluation now...\n",
-      "INFO:pykeen.evaluation.evaluator:Concluded batch_size search with batch_size=201.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fa69ddc39b248a89d31b7f2195d40b5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Evaluating on cuda:   0%|          | 0.00/201 [00:00<?, ?triple/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pykeen.evaluation.evaluator:Evaluation took 0.23s seconds\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "result = pipeline(\n",
-    "    model='TransE',\n",
-    "    dataset='nations',\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "reserved-creek",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0.19639337, -0.16836089, -0.03114699,  0.02896859, -0.09726831,\n",
-       "        -0.06199516,  0.1575382 , -0.20129088, -0.22236192,  0.13590659,\n",
-       "        -0.0757086 ,  0.20858522,  0.18815908, -0.20766027,  0.10798437,\n",
-       "         0.08954764, -0.09195376,  0.1596149 , -0.11445791, -0.07740876,\n",
-       "         0.06985323, -0.06789938,  0.14529385,  0.02011722,  0.06161602,\n",
-       "         0.02293201, -0.16616736, -0.20045018, -0.15285876, -0.15629375,\n",
-       "         0.14268196, -0.0245172 , -0.09785087, -0.08941651, -0.16856039,\n",
-       "         0.18781452, -0.20977688, -0.22055082, -0.18371245, -0.14164966,\n",
-       "        -0.16435796, -0.07544838,  0.19499303, -0.04047506,  0.1634386 ,\n",
-       "         0.01718387,  0.16803576, -0.18486236, -0.04035482,  0.14574832],\n",
-       "       [ 0.08400718, -0.03522509, -0.13971756,  0.11572263, -0.21427707,\n",
-       "         0.24744639, -0.14123753,  0.21208856,  0.06978457, -0.07410707,\n",
-       "         0.12106603,  0.21160452,  0.04917078, -0.05615771, -0.18857144,\n",
-       "         0.03754288, -0.10723944, -0.0169801 , -0.10961365,  0.09709871,\n",
-       "        -0.21529356,  0.09240917,  0.03186808, -0.1717462 ,  0.03995619,\n",
-       "        -0.07868147, -0.12828583,  0.18932973, -0.13855843, -0.04771087,\n",
-       "         0.13594243, -0.13656731,  0.14042093,  0.07074135,  0.1661649 ,\n",
-       "         0.20597802,  0.07402486, -0.21130997, -0.10915475, -0.07341161,\n",
-       "        -0.20499036,  0.12499193,  0.22410429, -0.0180589 , -0.2099524 ,\n",
-       "        -0.25001502, -0.22337636, -0.01577005,  0.05505931, -0.0830399 ],\n",
-       "       [ 0.17179759, -0.03645484,  0.11370327, -0.14019494, -0.12534976,\n",
-       "         0.15352881,  0.13511488,  0.13912928, -0.0483072 , -0.01592247,\n",
-       "        -0.23853096,  0.09579716,  0.00192258,  0.22907774,  0.11493239,\n",
-       "        -0.176138  ,  0.22415411, -0.21653381, -0.09941845,  0.1492893 ,\n",
-       "         0.14048633,  0.21955404, -0.06212671,  0.05424291,  0.22376709,\n",
-       "         0.00884284,  0.06124731, -0.16212288, -0.08178599,  0.20820902,\n",
-       "         0.06897985,  0.09998728, -0.05633331, -0.06100376,  0.19843301,\n",
-       "         0.17467704, -0.14167382, -0.21307851, -0.12347709, -0.04030309,\n",
-       "         0.14628026, -0.02140245,  0.00356108,  0.07652798, -0.00696065,\n",
-       "         0.19196464,  0.17027777, -0.17200524,  0.15254925,  0.19917835],\n",
-       "       [ 0.09154017, -0.22074386, -0.18088992, -0.09320766, -0.01921322,\n",
-       "         0.09818047,  0.0437395 , -0.00359149, -0.13320586,  0.16235515,\n",
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-       "        -0.03329604,  0.03448562,  0.24132654,  0.24476916,  0.21892832,\n",
-       "         0.07322221,  0.23690651,  0.20367865, -0.25628132, -0.22597446,\n",
-       "        -0.09114146,  0.08833733,  0.1696906 ,  0.05527192,  0.08065356,\n",
-       "        -0.19004323,  0.17325072,  0.17949852, -0.12614197,  0.06957392,\n",
-       "        -0.09307367, -0.03594378, -0.14662026, -0.13562614,  0.1633929 ,\n",
-       "         0.07712308, -0.09067339,  0.11562762,  0.00050979,  0.04710727],\n",
-       "       [-0.08211254,  0.17491034,  0.05896247,  0.02629796,  0.09781988,\n",
-       "        -0.21118644, -0.13064215,  0.11881437, -0.13431005, -0.08001482,\n",
-       "        -0.20614998,  0.11788526, -0.02511169, -0.15362486,  0.13010472,\n",
-       "         0.16089663,  0.17278983, -0.01427156,  0.0324924 ,  0.21914338,\n",
-       "        -0.16901594,  0.2144066 ,  0.18227877, -0.16752782,  0.18044943,\n",
-       "        -0.18088648, -0.08764116,  0.12936604,  0.06761473,  0.0863623 ,\n",
-       "         0.13137005,  0.07520721, -0.07411153,  0.00085493, -0.17404146,\n",
-       "         0.17726275, -0.12193546,  0.16026053, -0.2104451 , -0.16820604,\n",
-       "         0.05141479, -0.16766544,  0.19905202,  0.00216742, -0.02905482,\n",
-       "         0.18797643, -0.20268849,  0.04693165, -0.20051906,  0.12352565],\n",
-       "       [-0.15712015, -0.09339907, -0.18187661,  0.17578691,  0.05235326,\n",
-       "        -0.20340748,  0.18773216,  0.06509778, -0.09778243,  0.06552774,\n",
-       "         0.14802836,  0.05967927, -0.16579376, -0.11907542, -0.16632716,\n",
-       "        -0.1770228 ,  0.14406262, -0.08252464, -0.07413307, -0.18931122,\n",
-       "        -0.1598736 ,  0.12601586,  0.13650092, -0.02211493,  0.10583386,\n",
-       "         0.17770673,  0.21288018, -0.1147599 ,  0.14306472,  0.13834943,\n",
-       "         0.0010584 , -0.08187279, -0.17594308, -0.20091459, -0.16830356,\n",
-       "         0.18279408,  0.13213131,  0.10698148,  0.11420707,  0.0391953 ,\n",
-       "        -0.16844204,  0.07081551, -0.14734401, -0.07963501, -0.04659073,\n",
-       "        -0.22094299, -0.17782325, -0.19483247, -0.07195067,  0.19556463],\n",
-       "       [ 0.04810758, -0.17026302, -0.17347021,  0.03226259, -0.24219921,\n",
-       "        -0.13925387, -0.15466104,  0.19831258, -0.03611675, -0.17005117,\n",
-       "        -0.05808706,  0.09096552,  0.02419714, -0.00962409,  0.0933923 ,\n",
-       "        -0.05835747, -0.12071109,  0.08933903, -0.06352695, -0.21402836,\n",
-       "        -0.23980798,  0.25110883, -0.01910508, -0.04188502,  0.08119819,\n",
-       "         0.13139316, -0.09077525, -0.19752157, -0.00256558, -0.21086687,\n",
-       "         0.18824519,  0.19447424, -0.05278929,  0.00044852,  0.00343218,\n",
-       "        -0.13617216, -0.24112174,  0.09149597,  0.13875993,  0.25846294,\n",
-       "        -0.08163656, -0.05617552, -0.2228135 , -0.18411545,  0.08131476,\n",
-       "         0.07919797, -0.06930111,  0.06961904,  0.24592707,  0.08589988],\n",
-       "       [-0.14640518,  0.11928324, -0.07409123, -0.09792867, -0.22891715,\n",
-       "         0.09990875,  0.17659894,  0.19763768, -0.14523974, -0.0068074 ,\n",
-       "        -0.15434922,  0.07462037, -0.16678724, -0.07306221,  0.22598466,\n",
-       "        -0.09478337, -0.10829914,  0.00068458, -0.09439491, -0.0108681 ,\n",
-       "         0.17655665, -0.06254452, -0.01635562, -0.14420186,  0.07649746,\n",
-       "        -0.25066355,  0.22975226,  0.07027449,  0.11947302,  0.07098107,\n",
-       "        -0.16550283,  0.16987841,  0.23524453, -0.03749033, -0.09777185,\n",
-       "         0.09331265,  0.13934706, -0.13473545, -0.01010561,  0.01892876,\n",
-       "         0.11611211,  0.21830612, -0.02559492,  0.205865  , -0.0318163 ,\n",
-       "        -0.25584567, -0.20395377, -0.00614056, -0.19248585, -0.17786255],\n",
-       "       [-0.24650532,  0.0174387 , -0.1660184 , -0.05952626, -0.17698249,\n",
-       "        -0.20361026,  0.0642914 , -0.17894012, -0.0026814 ,  0.04739688,\n",
-       "         0.24289759, -0.14653091, -0.23783526,  0.14575554,  0.08590838,\n",
-       "        -0.198968  , -0.17858475, -0.02507035,  0.23852605,  0.21492226,\n",
-       "        -0.07290644,  0.0805637 ,  0.22773932, -0.06190724,  0.18047015,\n",
-       "         0.22446801, -0.05679401,  0.09598429, -0.11902507,  0.09209193,\n",
-       "         0.01739208, -0.09718507,  0.12873136,  0.01577665, -0.17447105,\n",
-       "        -0.11633729, -0.12534788,  0.08593753,  0.11171566, -0.10956202,\n",
-       "         0.15098822, -0.07863545, -0.12281843, -0.07502203,  0.03448169,\n",
-       "         0.20291723,  0.03189318,  0.06703094,  0.22824211, -0.0494961 ],\n",
-       "       [ 0.2309472 , -0.0347519 ,  0.15192923,  0.14296553,  0.00791517,\n",
-       "         0.20240901,  0.12905163,  0.00152958,  0.14889598, -0.04421031,\n",
-       "        -0.26571572, -0.06320024, -0.00330904,  0.0169428 ,  0.21382515,\n",
-       "        -0.06933823, -0.12908475,  0.02640192,  0.07430518, -0.23661894,\n",
-       "        -0.19168346,  0.06941147, -0.12096203,  0.07644739, -0.22937258,\n",
-       "        -0.16333365,  0.2565038 , -0.02651494,  0.1594433 , -0.22664072,\n",
-       "         0.06456865, -0.22137389,  0.03152997,  0.10104731, -0.04997271,\n",
-       "         0.12303443,  0.18517588,  0.14006168,  0.0004982 , -0.0760086 ,\n",
-       "         0.076054  ,  0.10895997,  0.1418402 , -0.12021562, -0.13591073,\n",
-       "        -0.12286597, -0.20992152, -0.06680023,  0.02430071,  0.23366396],\n",
-       "       [-0.24478927, -0.11449192, -0.16088825, -0.20149426, -0.04054394,\n",
-       "        -0.11813155,  0.1438273 ,  0.03971998, -0.27001834, -0.05435505,\n",
-       "        -0.04079188,  0.21486373, -0.10834973,  0.08587287, -0.20321299,\n",
-       "         0.05906281,  0.11607219,  0.04154148, -0.08319036,  0.07159384,\n",
-       "         0.09396406,  0.04175074,  0.15517867, -0.0870461 ,  0.26161745,\n",
-       "        -0.27072328, -0.03725231,  0.17339472,  0.15486923,  0.10055586,\n",
-       "         0.05352404, -0.09070709,  0.12953727,  0.03590197,  0.15577438,\n",
-       "        -0.24922238,  0.2079427 ,  0.0739115 , -0.02793941, -0.09390473,\n",
-       "         0.12230153, -0.2537305 , -0.089622  ,  0.09086446,  0.1463312 ,\n",
-       "        -0.06537158, -0.13445222, -0.00909109,  0.1755829 ,  0.11886591],\n",
-       "       [-0.17689785, -0.02789728,  0.0926782 ,  0.2087373 , -0.1755333 ,\n",
-       "        -0.00630249,  0.0835392 , -0.12393183, -0.0994062 ,  0.15981975,\n",
-       "         0.05307345,  0.13503858, -0.07245373, -0.13480204,  0.08635263,\n",
-       "        -0.19374774,  0.16315757,  0.20526557,  0.16031891,  0.06866097,\n",
-       "        -0.14652589,  0.1959019 , -0.11914818,  0.20059603, -0.07110467,\n",
-       "        -0.17224883,  0.02061648, -0.07414847,  0.22295515,  0.20749398,\n",
-       "        -0.06189835, -0.20381938,  0.17843392, -0.07110257, -0.16802898,\n",
-       "        -0.03861536,  0.22009614, -0.17320083,  0.1363343 , -0.16094032,\n",
-       "         0.10635982, -0.01472931,  0.01764147,  0.04561342, -0.20185001,\n",
-       "         0.19735058, -0.14901745, -0.07684697, -0.08739794, -0.16290334],\n",
-       "       [ 0.18726055, -0.05893308, -0.09456284,  0.04616243, -0.09222145,\n",
-       "         0.22188978,  0.11333711, -0.14913557, -0.05030894,  0.25899947,\n",
-       "        -0.25867885,  0.20845415, -0.07103726,  0.09113341, -0.23880838,\n",
-       "         0.01396979,  0.05398359,  0.21133317,  0.15613663, -0.12770912,\n",
-       "        -0.04797746,  0.10115518,  0.17674786,  0.05452147, -0.23155946,\n",
-       "        -0.00970665,  0.09051169,  0.12437607, -0.05990403, -0.02490725,\n",
-       "        -0.13321383, -0.21961826, -0.05132074,  0.03969955, -0.05327285,\n",
-       "        -0.16781576, -0.1956676 , -0.03650107,  0.20310017,  0.20638321,\n",
-       "         0.06410098,  0.16357383, -0.01761731, -0.01029921, -0.15756902,\n",
-       "        -0.14147511, -0.06397694, -0.26314327,  0.06933402, -0.10719081],\n",
-       "       [-0.23495518, -0.17530571,  0.11807019, -0.2164792 , -0.07659577,\n",
-       "         0.07663359,  0.13860796, -0.03602291, -0.14626017,  0.08137257,\n",
-       "        -0.17461006,  0.16130821, -0.00179765,  0.22117947, -0.18971075,\n",
-       "         0.12791681,  0.05122582, -0.04478684, -0.03979537,  0.22464836,\n",
-       "        -0.1075406 , -0.19393307,  0.0192949 ,  0.00732254,  0.22900571,\n",
-       "        -0.21262728,  0.08959627, -0.03409915,  0.08159275,  0.20853665,\n",
-       "         0.21737494,  0.12393425, -0.04873991,  0.19730753,  0.06971721,\n",
-       "        -0.03161063,  0.19812284, -0.0796216 , -0.14827883,  0.21757792,\n",
-       "        -0.15493284,  0.09250676, -0.20186517, -0.14941402,  0.04221082,\n",
-       "         0.16807486,  0.09295556, -0.07256678, -0.04787219, -0.07334726]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result.model.entity_representations[0](indices=None).cpu().detach().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "infrared-memorial",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "british-petite",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pykeen.pipeline.api:No random seed is specified. Setting to 3648325710.\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-d9464a9d11c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mrelation_to_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelation_to_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m )\n\u001b[0;32m---> 10\u001b[0;31m result = pipeline(\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mtesting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pykeen/pipeline/api.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(dataset, dataset_kwargs, training, testing, validation, evaluation_entity_whitelist, evaluation_relation_whitelist, model, model_kwargs, interaction, interaction_kwargs, dimensions, loss, loss_kwargs, regularizer, regularizer_kwargs, optimizer, optimizer_kwargs, clear_optimizer, lr_scheduler, lr_scheduler_kwargs, training_loop, training_loop_kwargs, negative_sampler, negative_sampler_kwargs, epochs, training_kwargs, stopper, stopper_kwargs, evaluator, evaluator_kwargs, evaluation_kwargs, result_tracker, result_tracker_kwargs, metadata, device, random_seed, use_testing_data, evaluation_fallback, filter_validation_when_testing, use_tqdm)\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0;31m# TODO should kwargs for loss and regularizer be checked and raised for?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m         model_instance = _build_model_helper(\n\u001b[0m\u001b[1;32m    922\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m             \u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pykeen/pipeline/api.py\u001b[0m in \u001b[0;36m_build_model_helper\u001b[0;34m(model, model_kwargs, loss, loss_kwargs, _device, _random_seed, regularizer, regularizer_kwargs, training_triples_factory)\u001b[0m\n\u001b[1;32m    642\u001b[0m     \u001b[0mloss_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_resolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m     return model_resolver.make(\n\u001b[0m\u001b[1;32m    645\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0mtriples_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_triples_factory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/class_resolver/api.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, query, pos_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    255\u001b[0m             \u001b[0mcls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mType\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos_kwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"required keyword-only argument\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pykeen/models/unimodal/compgcn.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, triples_factory, embedding_dim, encoder_kwargs, interaction, interaction_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;31m# combined representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         entity_representations, relation_representations = CombinedCompGCNRepresentations(\n\u001b[0m\u001b[1;32m     70\u001b[0m             \u001b[0mtriples_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtriples_factory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mencoder_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pykeen/nn/emb.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, triples_factory, embedding_specification, num_layers, dims, layer_kwargs)\u001b[0m\n\u001b[1;32m    944\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m         \u001b[0;31m# TODO: Check\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mtriples_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_inverse_triples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    947\u001b[0m         self.entity_representations = embedding_specification.make(\n\u001b[1;32m    948\u001b[0m             \u001b[0mnum_embeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtriples_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_entities\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "from pykeen.triples import TriplesFactory\n",
-    "from pykeen.pipeline import pipeline\n",
-    "from pykeen.datasets.nations import NATIONS_TRAIN_PATH, NATIONS_TEST_PATH\n",
-    "training = TriplesFactory.from_path(NATIONS_TRAIN_PATH)\n",
-    "testing = TriplesFactory.from_path(\n",
-    "    NATIONS_TEST_PATH,\n",
-    "    entity_to_id=training.entity_to_id,\n",
-    "    relation_to_id=training.relation_to_id,\n",
-    ")\n",
-    "result = pipeline(\n",
-    "    training=training,\n",
-    "    testing=testing,\n",
-    "    model='CompGCN',\n",
-    "    model_kwargs=dict(embedding_dim=2),\n",
-    "    epochs=5,  # short epochs for testing - you should go higher\n",
-    ")\n",
-    "result.save_to_directory('../data/pykeen_exp/test_pre_stratified_transe')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "pursuant-colors",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(100,)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result.model.entity_representations[0](indices=None).cpu().detach().numpy()[0].shape"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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diff --git a/Retrofitting using 19k + 19k dataset REEXEC Reorganized.ipynb b/Retrofitting using 19k + 19k dataset REEXEC Reorganized.ipynb
deleted file mode 100644
index 513c907..0000000
--- a/Retrofitting using 19k + 19k dataset REEXEC Reorganized.ipynb	
+++ /dev/null
@@ -1,18134 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "strange-saturn",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "random-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "incorporated-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'id', 'node1_label', 'label', 'node2_label', 'sent',\n",
-       "       'node1_emb', 'node2_emb', 'bert2SentSim', 'abstract',\n",
-       "       'abstract_firstSent', 'BERT_abstract_emb',\n",
-       "       'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'textEmb_7props_left',\n",
-       "       'textEmb_7props_right', 'textEmb_2props_left', 'textEmb_2props_right',\n",
-       "       'textEmb_7props_cosSim', 'textEmb_2props_cosSim',\n",
-       "       'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.772184\n",
-       "std          0.122326\n",
-       "min          0.120480\n",
-       "25%          0.684141\n",
-       "50%          0.793718\n",
-       "75%          0.874284\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279Seeded_SiblingsDF3_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "under-rabbit",
-   "metadata": {},
-   "source": [
-    "# Embeddings Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "annoying-intake",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Complex, Transe Embeddings Datasets generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "tamil-activity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "immune-nashville",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "residential-cleaners",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "current-great",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id  \n",
-       "0        Q454  \n",
-       "1   Q13648784  \n",
-       "2     Q178022  \n",
-       "3   Q21004260  \n",
-       "4      Q49112  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "valued-progressive",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# wordSimSet = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "different-vancouver",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list() + p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list() + wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-lewis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19166"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "collected-regular",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1499717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4765290\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16000518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6423382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2177259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q100448831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2857578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13577338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q96158854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7892\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7675683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2380954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21083881\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q211521\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16023742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q97627995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16721350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q32979618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4553362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q98232491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66194218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5372\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25481995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61466331\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6983403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84105477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30107768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56753514\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91927989\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12047900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11698973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q517386\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17097928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19838691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q101541623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17376918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23013268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66363580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55693905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68131879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85803433\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29053744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21406831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29957548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78710574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6462051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1042920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7925\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28324850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2849391\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "elect-gothic",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(compEmbeddings.items(), columns=['node', 'complex_embedding']).to_csv('../data/wordsim353_complex_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "combined-theme",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(transeEmbeddings.items(), columns=['node', 'transe_embedding']).to_csv('../data/wordsim353_transe_embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-extraction",
-   "metadata": {},
-   "source": [
-    "# Basis Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hairy-tuition",
-   "metadata": {},
-   "source": [
-    "## Probase Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "opened-quarter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = pd.read_csv('../data/probase/probase_WQnodes_subset_and_sim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "major-peter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>no_of_relations</th>\n",
-       "      <th>node1_qnode</th>\n",
-       "      <th>node2_qnode</th>\n",
-       "      <th>n1_final_qnode</th>\n",
-       "      <th>n2_final_qnode</th>\n",
-       "      <th>sim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>age</td>\n",
-       "      <td>35167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>gender</td>\n",
-       "      <td>14230</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>Q48277</td>\n",
-       "      <td>0.913568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>factor</td>\n",
-       "      <td>temperature</td>\n",
-       "      <td>13660</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q15061738</td>\n",
-       "      <td>P2076</td>\n",
-       "      <td>0.909663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>7</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>copper</td>\n",
-       "      <td>11142</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>Q15830500</td>\n",
-       "      <td>0.890199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
-       "      <td>variable</td>\n",
-       "      <td>age</td>\n",
-       "      <td>9375</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q10954303</td>\n",
-       "      <td>Q100343219</td>\n",
-       "      <td>0.873703</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0 node1_label  node2_label  no_of_relations node1_qnode  \\\n",
-       "0           0      factor          age            35167         NaN   \n",
-       "1           5      factor       gender            14230         NaN   \n",
-       "2           6      factor  temperature            13660         NaN   \n",
-       "3           7       metal       copper            11142      Q11426   \n",
-       "4           9    variable          age             9375         NaN   \n",
-       "\n",
-       "  node2_qnode n1_final_qnode n2_final_qnode       sim  \n",
-       "0         NaN      Q15061738     Q100343219  1.000000  \n",
-       "1      Q48277      Q15061738         Q48277  0.913568  \n",
-       "2         NaN      Q15061738          P2076  0.909663  \n",
-       "3         NaN         Q11426      Q15830500  0.890199  \n",
-       "4         NaN      Q10954303     Q100343219  0.873703  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "photographic-character",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset = probDF_Qnodes_DF_WQnodes1_subset.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'bert2SentSim'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "russian-proceeding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'] = 0.5 + 0.5 * probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "painted-selection",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.600841e+06\n",
-       "mean     5.429549e-01\n",
-       "std      5.363352e-02\n",
-       "min      5.000000e-01\n",
-       "25%      5.000000e-01\n",
-       "50%      5.331083e-01\n",
-       "75%      5.662167e-01\n",
-       "max      1.000000e+00\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probDF_Qnodes_DF_WQnodes1_subset['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-portsmouth",
-   "metadata": {},
-   "source": [
-    "## 19k+19k dataset with class similarity scores generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "sublime-girlfriend",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "classSim = []\n",
-    "\n",
-    "for _, row in tqdm(p279WordSimSeededDF_wabs_text.iterrows()):\n",
-    "    resp = requests.get(\"https://dsbox02.isi.edu:8888/qnode-similarity?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "    row['bert2SentSim'] = float(resp) if resp else -1\n",
-    "    classSim.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "optional-bracelet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff86112887254e7f950c4586072a22f5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "for _, row in tqdm(p279Seeded_SiblingsDF3_wabs_text.iterrows()):\n",
-    "    resp = requests.get(\"https://dsbox02.isi.edu:8888/qnode-similarity?q1=\"+row['node1']+\"&q2=\"+row['node2']+\"&embedding_type=class\").json()['similarity']\n",
-    "    row['bert2SentSim'] = float(resp) if resp else -1\n",
-    "    classSim.append(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "interior-yield",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimT = []\n",
-    "for class1 in classSim:\n",
-    "    classSimT.append(pd.DataFrame(class1).transpose())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "single-ethiopia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimDF = pd.concat(classSimT, ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "stuck-formation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimDF['bert2SentSim'] = classSimDF.bert2SentSim.astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "enclosed-plenty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimDF.loc[classSimDF['bert2SentSim'] == -1, ['bert2SentSim']] = 0.621981"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "public-thesis",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    38790.000000\n",
-       "mean         0.621981\n",
-       "std          0.248664\n",
-       "min         -0.035900\n",
-       "25%          0.426818\n",
-       "50%          0.720367\n",
-       "75%          0.840006\n",
-       "max          0.985841\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classSimDF[classSimDF['bert2SentSim'] != -1]['bert2SentSim'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "becoming-thomson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max1 = classSimDF['bert2SentSim'].max()\n",
-    "min1 = classSimDF['bert2SentSim'].min()\n",
-    "classSimDF['bert2SentSim'] = classSimDF['bert2SentSim'].apply(lambda p: (p - min1) / (max1 - min1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "coastal-edition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "      <th>parent</th>\n",
-       "      <th>par_label</th>\n",
-       "      <th>node1_sent</th>\n",
-       "      <th>node2_sent</th>\n",
-       "      <th>comb_sent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.840243</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.672269</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.613978</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.50806</td>\n",
-       "      <td>0.455747</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798362</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.77003</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.320911</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.840243  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.672269  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.613978  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798362  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.320911  ...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \\\n",
-       "0              0.823419                 -1.0                           -1.0   \n",
-       "1              0.734428                 -1.0                           -1.0   \n",
-       "2              0.707611              0.50806                       0.455747   \n",
-       "3              0.847591             0.855959                        0.77003   \n",
-       "4              0.780342             0.752693                       0.515319   \n",
-       "\n",
-       "  parent par_label node1_sent node2_sent comb_sent  \n",
-       "0    NaN       NaN        NaN        NaN       NaN  \n",
-       "1    NaN       NaN        NaN        NaN       NaN  \n",
-       "2    NaN       NaN        NaN        NaN       NaN  \n",
-       "3    NaN       NaN        NaN        NaN       NaN  \n",
-       "4    NaN       NaN        NaN        NaN       NaN  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classSimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "id": "governing-detector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimDF[:19454].to_csv('../data/P279_ChildPar_19k_WEmbAndClassSim.csv', index=None)\n",
-    "classSimDF[19454:].to_csv('../data/P279_Siblings_19k_WEmbAndClassSim.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compact-bridges",
-   "metadata": {},
-   "source": [
-    "## 19k+19k dataset with class similarity scores fetch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "nominated-blogger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classSimTargetChildPar = pd.read_csv('../data/P279_ChildPar_19k_WEmbAndClassSim.csv')\n",
-    "classSimTargetSiblings = pd.read_csv('../data/P279_Siblings_19k_WEmbAndClassSim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "korean-guitar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(classSimTargetSiblings['bert2SentSim'] == 0.5).sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hundred-virus",
-   "metadata": {},
-   "source": [
-    "# Ground Truth Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "limited-hormone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDF = pd.read_csv('../data/pedersen2007measures_table1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "alien-happiness",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Term1</th>\n",
-       "      <th>Term1_kg_id</th>\n",
-       "      <th>Term1_kg_label</th>\n",
-       "      <th>Term1_description</th>\n",
-       "      <th>Term2</th>\n",
-       "      <th>Term2_kg_id</th>\n",
-       "      <th>Term2_kg_label</th>\n",
-       "      <th>Term2_description</th>\n",
-       "      <th>Physician</th>\n",
-       "      <th>Coder</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Renal failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>Kidney failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Heart</td>\n",
-       "      <td>Q1072</td>\n",
-       "      <td>heart</td>\n",
-       "      <td>organ for the circulation of blood in animal c...</td>\n",
-       "      <td>Myocardium</td>\n",
-       "      <td>Q84133</td>\n",
-       "      <td>myocardium</td>\n",
-       "      <td>middle layer of the heart wall, which consists...</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Stroke</td>\n",
-       "      <td>Q12202</td>\n",
-       "      <td>stroke</td>\n",
-       "      <td>problem with the arteries supplying blood to t...</td>\n",
-       "      <td>Infarct</td>\n",
-       "      <td>Q207550</td>\n",
-       "      <td>infarction</td>\n",
-       "      <td>tissue death caused by a local lack of oxygen,...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Abortion</td>\n",
-       "      <td>Q8452</td>\n",
-       "      <td>abortion</td>\n",
-       "      <td>intentional ending of a pregnancy</td>\n",
-       "      <td>Miscarriage</td>\n",
-       "      <td>Q28693</td>\n",
-       "      <td>miscarriage</td>\n",
-       "      <td>natural death of an embryo or fetus before it ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Delusion</td>\n",
-       "      <td>Q189643</td>\n",
-       "      <td>delusion</td>\n",
-       "      <td>firm and fixed belief based on inadequate grou...</td>\n",
-       "      <td>Schizophrenia</td>\n",
-       "      <td>Q41112</td>\n",
-       "      <td>schizophrenia</td>\n",
-       "      <td>psychotic disorder characterized by emotional ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Term1 Term1_kg_id  Term1_kg_label  \\\n",
-       "0  Renal failure     Q476921  kidney failure   \n",
-       "1          Heart       Q1072           heart   \n",
-       "2         Stroke      Q12202          stroke   \n",
-       "3       Abortion       Q8452        abortion   \n",
-       "4       Delusion     Q189643        delusion   \n",
-       "\n",
-       "                                   Term1_description           Term2  \\\n",
-       "0  disease where the kidneys fail to adequately f...  Kidney failure   \n",
-       "1  organ for the circulation of blood in animal c...      Myocardium   \n",
-       "2  problem with the arteries supplying blood to t...         Infarct   \n",
-       "3                  intentional ending of a pregnancy     Miscarriage   \n",
-       "4  firm and fixed belief based on inadequate grou...   Schizophrenia   \n",
-       "\n",
-       "  Term2_kg_id  Term2_kg_label  \\\n",
-       "0     Q476921  kidney failure   \n",
-       "1      Q84133      myocardium   \n",
-       "2     Q207550      infarction   \n",
-       "3      Q28693     miscarriage   \n",
-       "4      Q41112   schizophrenia   \n",
-       "\n",
-       "                                   Term2_description  Physician  Coder  \n",
-       "0  disease where the kidneys fail to adequately f...        4.0    4.0  \n",
-       "1  middle layer of the heart wall, which consists...        3.3    3.0  \n",
-       "2  tissue death caused by a local lack of oxygen,...        3.0    2.8  \n",
-       "3  natural death of an embryo or fetus before it ...        3.0    3.3  \n",
-       "4  psychotic disorder characterized by emotional ...        3.0    2.2  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bioDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parental-event",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDFNodesSet = set(bioDF.Term1_kg_id.to_list() + bioDF.Term2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "wicked-proportion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childParNodesSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list())\n",
-    "P279siblingsNodesSet = set(p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "pediatric-tomato",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 1)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "transparent-diversity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 25)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probaseNodesSet = set(probDF_Qnodes_DF_WQnodes1_subset.node1.to_list() + probDF_Qnodes_DF_WQnodes1_subset.node2.to_list())\n",
-    "\n",
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in probaseNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in probaseNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "uniform-civilian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "voluntary-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "consolidated-investigation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "irish-disposal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict, weightCase, weightAssignment=False):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            if weightAssignment:\n",
-    "                sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            else:\n",
-    "                sumOfSims = sum([1 for neighb in neighbs])\n",
-    "                sumOfEmbs = sum([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "                \n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {},
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-milan",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Sample attempt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "substantial-aggregate",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text_sample = p279WordSimSeededDF_wabs_text[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "analyzed-passion",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "japanese-defense",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dying-nigeria",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "reduced-avenue",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1034"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "advised-hollywood",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "authentic-greene",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1034.000000\n",
-       "mean        0.281990\n",
-       "std         0.054759\n",
-       "min         0.069792\n",
-       "25%         0.244698\n",
-       "50%         0.278019\n",
-       "75%         0.319656\n",
-       "max         0.450444\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "variable-transportation",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## ChildPar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "central-spyware",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "white-genesis",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "registered-original",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "experienced-conspiracy",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "every-interface",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "specified-berry",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.283712\n",
-       "std          0.056573\n",
-       "min          0.050719\n",
-       "25%          0.246137\n",
-       "50%          0.281897\n",
-       "75%          0.319687\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incoming-calvin",
-   "metadata": {},
-   "source": [
-    "# Correlation Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "answering-exhaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "LABELS = ['I','U','M']\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    responseDict = {}\n",
-    "    responseDict['wordSimMissingSet'] = wordSimMissingSet\n",
-    "    responseDict['coveredPairs'] = len(wordSim353AnnotDF_New2)\n",
-    "    responseDict['totalPairs'] = len(wordSim353AnnotDF_New)\n",
-    "    \n",
-    "#     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    \n",
-    "    # Logic 1: Scale min,max value to 1,4 strictly\n",
-    "#     min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "#     min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "#     wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    \n",
-    "    # Logic 2: Scale abs value to 1,4 strictly\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * abs(p))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "    \n",
-    "#     print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "#     print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "#     print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "#     print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "#     print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    responseDict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "    responseDict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "    responseDict['old_acc'] = accuracy_score(wordSim353AnnotDF_New['textOld'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    responseDict['new_acc'] = accuracy_score(wordSim353AnnotDF_New['textNew'].apply(labelSamples), wordSim353AnnotDF_New['category'])\n",
-    "    \n",
-    "    responseDict['class_rep_old'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), output_dict=True)\n",
-    "    responseDict['class_rep_new'] = classification_report(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), output_dict=True)\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New['category'], wordSim353AnnotDF_New['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    responseDict['cm_old'] = cm_old\n",
-    "    responseDict['cm_new'] = cm_new\n",
-    "    \n",
-    "    return responseDict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "premier-crisis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "confusionMatrixMaster = {}\n",
-    "neighDictMaster = {}\n",
-    "responsesDictMaster = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "previous-applicant",
-   "metadata": {},
-   "source": [
-    "## Input Datasets Processing and Parsing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spoken-buying",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Text 7 props Embeddings\n",
-    "textEmb_7props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-7-props-all.tsv', sep='\\t')\n",
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "embedDict = fetchEmbeddings(textEmb_7props_DF)\n",
-    "embedDictMaster['text_7props'] = embedDict\n",
-    "len(textEmb_7props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "closing-biotechnology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Text 2 props Embeddings\n",
-    "textEmb_2props_DF = pd.read_csv('../data/P279_dataset/output/P279-text-embedding-2-props-all.tsv', sep='\\t')\n",
-    "textEmb_2props_DF['value'] = textEmb_2props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "embedDict = fetchEmbeddings(textEmb_2props_DF)\n",
-    "embedDictMaster['text_2props'] = embedDict\n",
-    "len(textEmb_2props_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "oriented-brush",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Complex Embeddings\n",
-    "compEmbeddingsDF = pd.read_csv('../data/wordsim353_complex_embeddings.csv')\n",
-    "compEmbeddingsDF['complex_embedding'] = compEmbeddingsDF['complex_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "compEmbeddings = {}\n",
-    "for _, row in compEmbeddingsDF.iterrows():\n",
-    "    compEmbeddings[row['node']] = np.array(row['complex_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "absolute-brazil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['complex'] = normalize(compEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "transsexual-lender",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transe Embeddings\n",
-    "transeEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')\n",
-    "transeEmbeddingsDF['transe_embedding'] = transeEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "transeEmbeddings = {}\n",
-    "for _, row in transeEmbeddingsDF.iterrows():\n",
-    "    transeEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "printable-trinity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['transe'] = normalize(transeEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "federal-conservation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "def getSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries)\n",
-    "#     print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "narrative-security",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "choice-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptions = pd.read_csv('../../wd-correctness/gdrive-kgtk-dump-2020-12-07/descriptions.en.tsv.gz', compression='gzip', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "quick-insulin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_of_nodes = set(wordSim353AnnotDF_New.word1_kg_id.to_list() + wordSim353AnnotDF_New.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "objective-pendant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "descriptions1 = descriptions[descriptions.node1.apply(lambda p: p in list_of_nodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "headed-cheese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "desc_dict = {row['node1']: row['node2'][1:-4] for _, row in descriptions1.iterrows()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "mature-folder",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f13d01b193b1463fb947c4a1b0f71dcb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "426e07be03de401096d623a573192db0",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Abstract Text Embeddings\n",
-    "absEmbeddings = {}\n",
-    "for _, row in tqdm(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows(), total=len(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))):\n",
-    "    if not(pd.isna(row['BERT_abstract_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_emb_right'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])\n",
-    "\n",
-    "# missingWordSimSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absEmbeddings)])\n",
-    "for _, row in tqdm(wordSim353AnnotDF_New.iterrows(), total=len(wordSim353AnnotDF_New)):\n",
-    "    if row.word1_kg_id not in absEmbeddings:\n",
-    "        if row.word1_kg_id in desc_dict:\n",
-    "            absEmbeddings[row.word1_kg_id] = getSentEmbeddings(desc_dict[row.word1_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absEmbeddings:\n",
-    "        if row.word2_kg_id in desc_dict:\n",
-    "            absEmbeddings[row.word2_kg_id] = getSentEmbeddings(desc_dict[row.word2_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "metropolitan-matrix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['abstract'] = normalize(absEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "fossil-provision",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8698306e64854387a145a6a9adae6d99",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8f2d993df1a48f7be6d59bd7bc73464",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Abstract First Sentence Embeddings\n",
-    "absFirstSentEmbeddings = {}\n",
-    "for _, row in tqdm(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]).iterrows(), total=len(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))):\n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node1']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node1_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node1']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node1_emb'][1:-1])))])\n",
-    "        \n",
-    "    if not(pd.isna(row['BERT_abstract_firstSent_emb_right'])):\n",
-    "        absFirstSentEmbeddings[row['node2']] = np.array([float(val1) for val1 in row['BERT_abstract_firstSent_emb_right'][1:-1].split(',')])\n",
-    "    elif not(pd.isna(row['node2_emb'])):\n",
-    "        absFirstSentEmbeddings[row['node2']] = np.array([float(val1) for val1 in list(filter(lambda p: p, re.split(\"[\\n\\s]\", row['node2_emb'][1:-1])))])\n",
-    "\n",
-    "# missingWordSimFirstSentSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in absFirstSentEmbeddings)] + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in absFirstSentEmbeddings)])\n",
-    "for _, row in tqdm(wordSim353AnnotDF_New.iterrows(), total=len(wordSim353AnnotDF_New)):\n",
-    "    if row.word1_kg_id not in absFirstSentEmbeddings:\n",
-    "        if row.word1_kg_id in desc_dict:\n",
-    "            absFirstSentEmbeddings[row.word1_kg_id] = getSentEmbeddings(desc_dict[row.word1_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absFirstSentEmbeddings[row.word1_kg_id] = getSentEmbeddings(row['Word 1'], 'bert-base-nli-mean-tokens')\n",
-    "    if row.word2_kg_id not in absFirstSentEmbeddings:\n",
-    "        if row.word2_kg_id in desc_dict:\n",
-    "            absFirstSentEmbeddings[row.word2_kg_id] = getSentEmbeddings(desc_dict[row.word2_kg_id], 'bert-base-nli-mean-tokens')\n",
-    "        else:\n",
-    "            absFirstSentEmbeddings[row.word2_kg_id] = getSentEmbeddings(row['Word 2'], 'bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "transparent-czech",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['abstract_first_sent'] = normalize(absFirstSentEmbeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "brazilian-wisdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "embedDictMaster['h_emb'] = json.load(open('../data/HAS_embeddings/H_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['a_emb'] = json.load(open('../data/HAS_embeddings/A_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['s_emb'] = json.load(open('../data/HAS_embeddings/S_embeddings_original_19k_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "appreciated-motor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "# Three Embeddings\n",
-    "embedThreeDictConcat = {}\n",
-    "tempThreeDictLens = defaultdict(int)\n",
-    "for key1 in ['complex', 'transe', \n",
-    "            'abstract_first_sent']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempThreeDictLens[key1] = max(tempThreeDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedThreeDictConcat:\n",
-    "            embedThreeDictConcat[word] = []\n",
-    "for word in embedThreeDictConcat.keys():\n",
-    "    for key1 in ['complex', 'transe', \n",
-    "            'abstract_first_sent']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedThreeDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedThreeDictConcat[word].append([0]*(tempThreeDictLens[key1]))\n",
-    "for word in embedThreeDictConcat.keys():\n",
-    "    embedThreeDictConcat[word] = [item for sublist in embedThreeDictConcat[word] for item in sublist]\n",
-    "# for word1 in list(embedThreeDictConcat.keys()):\n",
-    "#     if len(embedThreeDictConcat[word1]) != 968:\n",
-    "#         del embedThreeDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "conservative-fitting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['three_emb'] = normalize(embedThreeDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "digital-license",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Six Embeddings\n",
-    "embedSixDictConcat = {}\n",
-    "tempSixDictLens = defaultdict(int)\n",
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempSixDictLens[key1] = max(tempSixDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedSixDictConcat:\n",
-    "            embedSixDictConcat[word] = []\n",
-    "for word in embedSixDictConcat.keys():\n",
-    "    for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedSixDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedSixDictConcat[word].append([0]*(tempSixDictLens[key1]))\n",
-    "for word in embedSixDictConcat.keys():\n",
-    "    embedSixDictConcat[word] = [item for sublist in embedSixDictConcat[word] for item in sublist]\n",
-    "# for word1 in list(embedSixDictConcat.keys()):\n",
-    "#     if len(embedSixDictConcat[word1]) != 3784:\n",
-    "#         del embedSixDictConcat[word1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "brazilian-prefix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['six_emb'] = normalize(embedSixDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "homeless-converter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Eight Embeddings\n",
-    "embedEightDictConcat = {}\n",
-    "tempEightDictLens = defaultdict(int)\n",
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent', 'h_emb', 's_emb']:\n",
-    "    for word in embedDictMaster[key1].keys():\n",
-    "        tempEightDictLens[key1] = max(tempEightDictLens[key1], len(embedDictMaster[key1][word]))\n",
-    "        if word not in embedEightDictConcat:\n",
-    "            embedEightDictConcat[word] = []\n",
-    "\n",
-    "for word in embedEightDictConcat.keys():\n",
-    "    for key1 in ['text_7props', 'text_2props', 'complex', 'transe', \n",
-    "            'abstract', 'abstract_first_sent', 'h_emb', 's_emb']:\n",
-    "        if word in embedDictMaster[key1]:\n",
-    "            embedEightDictConcat[word].append(embedDictMaster[key1][word])\n",
-    "        else:\n",
-    "            embedEightDictConcat[word].append([0]*(tempEightDictLens[key1]))\n",
-    "for word in embedEightDictConcat.keys():\n",
-    "    embedEightDictConcat[word] = [item for sublist in embedEightDictConcat[word] for item in sublist]\n",
-    "\n",
-    "# for word1 in list(embedEightDictConcat.keys()):\n",
-    "#     if len(embedEightDictConcat[word1]) != 3784:\n",
-    "#         del embedEightDictConcat[word1]\n",
-    "        \n",
-    "        \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "overhead-healthcare",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDictMaster['eight_emb'] = normalize(embedEightDictConcat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "reflected-finger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for word_key in embedDictMaster[key1].keys():\n",
-    "        if type(embedDictMaster[key1][word_key]) != list:\n",
-    "            embedDictMaster[key1][word_key] = embedDictMaster[key1][word_key].tolist()\n",
-    "    \n",
-    "    with open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json', 'w') as f:\n",
-    "        json.dump(embedDictMaster[key1], f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "premier-membership",
-   "metadata": {},
-   "source": [
-    "## Load input datasets from checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "strange-sussex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in ['text_7props', 'text_2props', 'complex', 'transe', 'abstract', 'abstract_first_sent', 'three_emb', 'six_emb', 'eight_emb']:\n",
-    "    embedDictMaster[key1] = json.load(open('../data/orig_embeddings/'+key1+'_original_embeddings_dict.json'))\n",
-    "embedDictMaster['h_emb'] = json.load(open('../data/HAS_embeddings/H_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['a_emb'] = json.load(open('../data/HAS_embeddings/A_embeddings_original_19k_dict.json'))\n",
-    "embedDictMaster['s_emb'] = json.load(open('../data/HAS_embeddings/S_embeddings_original_19k_dict.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "exclusive-principle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key1 in embedDictMaster.keys():\n",
-    "    for key2 in embedDictMaster[key1].keys():\n",
-    "        embedDictMaster[key1][key2] = np.array(embedDictMaster[key1][key2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "innocent-allergy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embList = list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "bound-produce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props',\n",
-       " 'text_2props',\n",
-       " 'complex',\n",
-       " 'transe',\n",
-       " 'abstract',\n",
-       " 'abstract_first_sent',\n",
-       " 'three_emb',\n",
-       " 'six_emb',\n",
-       " 'eight_emb',\n",
-       " 'h_emb',\n",
-       " 'a_emb',\n",
-       " 's_emb']"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(embedDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excessive-significance",
-   "metadata": {},
-   "source": [
-    "## Neighbours Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "guided-evans",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "17845\n",
-      "17849\n",
-      "2662\n",
-      "23\n",
-      "2662\n",
-      "23\n",
-      "2662\n"
-     ]
-    }
-   ],
-   "source": [
-    "neighDictMaster['19k_2'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "neighDictMaster['probase'] = fetchNeighbours(probDF_Qnodes_DF_WQnodes1_subset)\n",
-    "neighDictMaster['probase+19k_2'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, probDF_Qnodes_DF_WQnodes1_subset]))\n",
-    "neighDictMaster['19k_childPar'] = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text]))\n",
-    "neighDictMaster['19k_siblings'] = fetchNeighbours(pd.concat([p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "neighDictMaster['19k_class_childPar'] = fetchNeighbours(classSimTargetChildPar)\n",
-    "neighDictMaster['19k_class_siblings'] = fetchNeighbours(classSimTargetSiblings)\n",
-    "neighDictMaster['19k_2_class'] = fetchNeighbours(pd.concat([classSimTargetChildPar, classSimTargetSiblings]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "burning-decade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basisList = list(neighDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "increasing-survival",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['19k_2', 'probase', 'probase+19k_2', '19k_childPar', '19k_siblings', '19k_class_childPar', '19k_class_siblings', '19k_2_class'])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neighDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incorporate-ecuador",
-   "metadata": {},
-   "source": [
-    "## Retrofitting using combination of input and basis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "studied-providence",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'neighDictMaster' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-42beab44fa25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mneighDictMasterBKP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneighDictMaster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0membedDictMasterBKP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedDictMaster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'neighDictMaster' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "import copy\n",
-    "neighDictMasterBKP = copy.deepcopy(neighDictMaster)\n",
-    "embedDictMasterBKP = copy.deepcopy(embedDictMaster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prepared-culture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import copy\n",
-    "# neighDictMaster = copy.deepcopy(neighDictMasterBKP)\n",
-    "# embedDictMaster = copy.deepcopy(embedDictMasterBKP)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "assisted-virtue",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0deeddfa23149abb21e197b4dee3ea1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "results = []\n",
-    "NUM_ITERS = 10\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            groupResults = []\n",
-    "            for weightCase in [1,2]:\n",
-    "                embedDict = embedDictMaster[emb]\n",
-    "                if weightedNess:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                else:\n",
-    "                    caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                for iterNum in range(1,NUM_ITERS+1):\n",
-    "                    newEmbedDict = retrofit(embedDict, neighDictMaster[basis], weightCase, weightedNess)\n",
-    "        #             dists = determineDistances(embedDict, newEmbedDict)\n",
-    "                    responsesDict = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        #                 print(responsesDict.keys())\n",
-    "                    groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                    responsesDict['old_acc']*100, \\\n",
-    "                                    responsesDict['new_acc']*100, \\\n",
-    "                                    (responsesDict['new_acc'] - responsesDict['old_acc'])*100, \\\n",
-    "                                    responsesDict['coveredPairs'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                     responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                    ])\n",
-    "                    embedDict = newEmbedDict\n",
-    "\n",
-    "                newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                responsesDictMaster[caseName] = responsesDict\n",
-    "            for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "                results.append(gR+[rank])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "super-engine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# newEmbedDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "strong-chamber",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'Rank'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opening-thomson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.sort_values(by=['Increase'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "reasonable-occasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resultsDF.to_csv('../data/retrofitting/retro_Aug17_2021.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "strong-singer",
-   "metadata": {},
-   "source": [
-    "## Analyze Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "id": "baking-ranking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def addColumnToGroundTruth(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "#     print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textOld'] == -1, 'textOld'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textOld'] != -1]['textOld'].mean()\n",
-    "    wordSim353AnnotDF_New.loc[wordSim353AnnotDF_New['textNew'] == -1, 'textNew'] = wordSim353AnnotDF_New[wordSim353AnnotDF_New['textNew'] != -1]['textNew'].mean()\n",
-    "    min1, max1 = wordSim353AnnotDF_New['textOld'].min(), wordSim353AnnotDF_New['textOld'].max()\n",
-    "    min2, max2 = wordSim353AnnotDF_New['textNew'].min(), wordSim353AnnotDF_New['textNew'].max()\n",
-    "#     assert wordSim353AnnotDF_New['textOld'].min() >= 0, wordSim353AnnotDF_New['textOld'].min()\n",
-    "#     assert wordSim353AnnotDF_New['textNew'].min() >= 0, wordSim353AnnotDF_New['textNew'].min()\n",
-    "    wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.textOld.apply(lambda p: 4 - 3 * (p - min1) / (max1 - min1))\n",
-    "    wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.textNew.apply(lambda p: 4 - 3 * (p - min2) / (max2 - min2))\n",
-    "    wordSim353AnnotDF_New['old_class'] = wordSim353AnnotDF_New['textOld'].apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['new_class'] = wordSim353AnnotDF_New['textNew'].apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New['retro_worked'] = ((wordSim353AnnotDF_New['new_class'] == wordSim353AnnotDF_New['category']) & (wordSim353AnnotDF_New['old_class'] != wordSim353AnnotDF_New['category']))\n",
-    "    return wordSim353AnnotDF_New\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "id": "fatty-secretary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = addColumnToGroundTruth(embedDictMaster['three_emb'], newEmbedDictMaster['three_emb_19k_2_class_1_weighted'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "id": "antique-moscow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = addColumnToGroundTruth(embedDictMaster['six_emb'], newEmbedDictMaster['six_emb_19k_2_class_1_weighted'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "id": "defined-sense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>category</th>\n",
-       "      <th>textOld</th>\n",
-       "      <th>textNew</th>\n",
-       "      <th>old_class</th>\n",
-       "      <th>new_class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.260638</td>\n",
-       "      <td>3.557514</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Q5369</td>\n",
-       "      <td>baseball</td>\n",
-       "      <td>Q1539532</td>\n",
-       "      <td>season</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.584545</td>\n",
-       "      <td>3.246840</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Q622821</td>\n",
-       "      <td>calculation</td>\n",
-       "      <td>Q12525525</td>\n",
-       "      <td>computation</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.173112</td>\n",
-       "      <td>1.663656</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Q578</td>\n",
-       "      <td>century</td>\n",
-       "      <td>Q6266</td>\n",
-       "      <td>nation</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.455742</td>\n",
-       "      <td>3.616025</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Q93352</td>\n",
-       "      <td>coast</td>\n",
-       "      <td>Q468756</td>\n",
-       "      <td>shore</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.925905</td>\n",
-       "      <td>1.667819</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Q6881511</td>\n",
-       "      <td>company</td>\n",
-       "      <td>Q1196314</td>\n",
-       "      <td>stock</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.508146</td>\n",
-       "      <td>3.224211</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>Q2095</td>\n",
-       "      <td>food</td>\n",
-       "      <td>Q2216236</td>\n",
-       "      <td>rooster</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.828730</td>\n",
-       "      <td>3.393004</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133</th>\n",
-       "      <td>Q11410</td>\n",
-       "      <td>game</td>\n",
-       "      <td>Q11499267</td>\n",
-       "      <td>defeat</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.699801</td>\n",
-       "      <td>3.245156</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>Q798505</td>\n",
-       "      <td>listing</td>\n",
-       "      <td>Q21146257</td>\n",
-       "      <td>category</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.554506</td>\n",
-       "      <td>3.300242</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>Q61864890</td>\n",
-       "      <td>lobster</td>\n",
-       "      <td>Q282</td>\n",
-       "      <td>wine</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.534090</td>\n",
-       "      <td>3.345835</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190</th>\n",
-       "      <td>Q1368</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Q693464</td>\n",
-       "      <td>cash</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1.914554</td>\n",
-       "      <td>1.497877</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>Q132821</td>\n",
-       "      <td>murder</td>\n",
-       "      <td>Q3882220</td>\n",
-       "      <td>manslaughter</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.023082</td>\n",
-       "      <td>1.646781</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>227</th>\n",
-       "      <td>Q17285</td>\n",
-       "      <td>plane</td>\n",
-       "      <td>Q1420</td>\n",
-       "      <td>car</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.646583</td>\n",
-       "      <td>3.394859</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>Q529711</td>\n",
-       "      <td>start</td>\n",
-       "      <td>Q577</td>\n",
-       "      <td>year</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>M</td>\n",
-       "      <td>3.530899</td>\n",
-       "      <td>3.088973</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>Q79007</td>\n",
-       "      <td>street</td>\n",
-       "      <td>Q7543083</td>\n",
-       "      <td>avenue</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.513824</td>\n",
-       "      <td>1.561554</td>\n",
-       "      <td>M</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    word1_kg_id       Word 1 word2_kg_id        Word 2  Avg category  \\\n",
-       "2         Q8333          FBI     Q178022   fingerprint  3.6        U   \n",
-       "28        Q5369     baseball    Q1539532        season  3.2        M   \n",
-       "41      Q622821  calculation   Q12525525   computation  1.0        I   \n",
-       "47         Q578      century       Q6266        nation  4.0        U   \n",
-       "56       Q93352        coast     Q468756         shore  1.6        I   \n",
-       "57     Q6881511      company    Q1196314         stock  3.0        M   \n",
-       "126       Q2095         food    Q2216236       rooster  3.2        M   \n",
-       "133      Q11410         game   Q11499267        defeat  3.0        M   \n",
-       "170     Q798505      listing   Q21146257      category  3.2        M   \n",
-       "173   Q61864890      lobster        Q282          wine  3.2        M   \n",
-       "190       Q1368        money     Q693464          cash  1.0        I   \n",
-       "209     Q132821       murder    Q3882220  manslaughter  1.6        I   \n",
-       "227      Q17285        plane       Q1420           car  2.6        M   \n",
-       "295     Q529711        start        Q577          year  3.4        M   \n",
-       "303      Q79007       street    Q7543083        avenue  1.4        I   \n",
-       "\n",
-       "      textOld   textNew old_class new_class  \n",
-       "2    3.260638  3.557514         M         U  \n",
-       "28   3.584545  3.246840         U         M  \n",
-       "41   2.173112  1.663656         M         I  \n",
-       "47   3.455742  3.616025         M         U  \n",
-       "56   1.925905  1.667819         M         I  \n",
-       "57   3.508146  3.224211         U         M  \n",
-       "126  3.828730  3.393004         U         M  \n",
-       "133  3.699801  3.245156         U         M  \n",
-       "170  3.554506  3.300242         U         M  \n",
-       "173  3.534090  3.345835         U         M  \n",
-       "190  1.914554  1.497877         M         I  \n",
-       "209  2.023082  1.646781         M         I  \n",
-       "227  3.646583  3.394859         U         M  \n",
-       "295  3.530899  3.088973         U         M  \n",
-       "303  2.513824  1.561554         M         I  "
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp1[temp1['retro_worked']][['word1_kg_id', 'Word 1', 'word2_kg_id', 'Word 2', 'Avg', 'category', 'textOld', 'textNew', 'old_class','new_class']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "renewable-stylus",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "leading-frontier",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "extensive-forty",
-   "metadata": {},
-   "source": [
-    "## Confusion Matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "maritime-director",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusionMatrixMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qualified-empty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = embedDictMaster['text_7props_19k']\n",
-    "newEmbedDict = newEmbedDictMaster['text_7props_19k_2']\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "\n",
-    "print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "distinct-timothy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    210\n",
-       "U    101\n",
-       "I     20\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['category'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "antique-infrastructure",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(figsize=(20,20))\n",
-    "for num, key1 in enumerate(['text_7props_19k_old_2', 'text_7props_19k_new_2', 'text_2props_19k_old_2', 'text_2props_19k_new_2', \n",
-    "             'transe_19k_old_1', 'transe_19k_new_1', 'complex_19k_old_1', 'complex_19k_new_1']):\n",
-    "    plt.subplot(4, 2, num+1)\n",
-    "    sns.heatmap(pd.DataFrame(confusionMatrixMaster[key1], LABELS, LABELS), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "micro-phrase",
-   "metadata": {},
-   "source": [
-    "# Save all embeddings and similarity scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "boring-campus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDictMaster['concat_probase_19k_v1_2'] = newEmbedDictMaster['concat_probase_19k__v1_2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "automatic-update",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d97162bbb58542999eb317ede34a3300",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "\n",
-    "for key1, key2 in tqdm(emb_pairs):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "related-opposition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "allied-sunrise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004515859562266559, -0.011723668837741234,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.384407</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.0050116109222357935, -0.013383541509188317...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004971298302113362, -0.013254267455943004,...</td>\n",
-       "      <td>3.468370</td>\n",
-       "      <td>3.463734</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0067204430541292475, 0.008331487632641807, ...</td>\n",
-       "      <td>[-0.004862257036458548, 0.014077170447753274, ...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.005805280378137928, 0.015115419540555773, ...</td>\n",
-       "      <td>[0.006720440253943506, 0.00833148416118722, 0....</td>\n",
-       "      <td>[-0.004862255010517295, 0.01407716458226321, 0...</td>\n",
-       "      <td>3.035534</td>\n",
-       "      <td>2.991152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.01523113313292473, 0.005271417432183582, -0...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.850218</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013922101038066187, 0.0023911841962956006, ...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.01381456413075283, 0.0024352598782685863, -...</td>\n",
-       "      <td>2.902356</td>\n",
-       "      <td>2.895993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897046995817, 0.014694837513784692, -...</td>\n",
-       "      <td>[0.013016090555331788, 0.01114268765690899, -0...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.962306</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013274960537868754, 0.010965634834463656, -...</td>\n",
-       "      <td>[0.0064668943524542844, 0.014694831390933237, ...</td>\n",
-       "      <td>[0.013242882478240212, 0.010953106140234146, -...</td>\n",
-       "      <td>2.995206</td>\n",
-       "      <td>2.993596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626153814759, 0.005047111609608307, -0...</td>\n",
-       "      <td>[0.010410829355084429, 0.014776997941327313, -...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>[0.013226256027203037, 0.00504710950664428, -0...</td>\n",
-       "      <td>[0.010410825017237097, 0.01477699178424233, -0...</td>\n",
-       "      <td>1.689315</td>\n",
-       "      <td>1.689315</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "1  ...  [0.0067204430541292475, 0.008331487632641807, ...   \n",
-       "2  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "3  ...  [0.006466897046995817, 0.014694837513784692, -...   \n",
-       "4  ...  [0.01322626153814759, 0.005047111609608307, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859562266559, -0.011723668837741234,...                 3.468370   \n",
-       "1  [-0.004862257036458548, 0.014077170447753274, ...                 3.035534   \n",
-       "2  [0.01523113313292473, 0.005271417432183582, -0...                 2.902356   \n",
-       "3  [0.013016090555331788, 0.01114268765690899, -0...                 2.995206   \n",
-       "4  [0.010410829355084429, 0.014776997941327313, -...                 1.689315   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.384407   \n",
-       "1                    2.991152   \n",
-       "2                    2.850218   \n",
-       "3                    2.962306   \n",
-       "4                    1.689315   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.0050116109222357935, -0.013383541509188317...   \n",
-       "1  [-0.005805280378137928, 0.015115419540555773, ...   \n",
-       "2  [0.013922101038066187, 0.0023911841962956006, ...   \n",
-       "3  [0.013274960537868754, 0.010965634834463656, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "1  [0.006720440253943506, 0.00833148416118722, 0....   \n",
-       "2  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "3  [0.0064668943524542844, 0.014694831390933237, ...   \n",
-       "4  [0.013226256027203037, 0.00504710950664428, -0...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.004971298302113362, -0.013254267455943004,...   \n",
-       "1  [-0.004862255010517295, 0.01407716458226321, 0...   \n",
-       "2  [0.01381456413075283, 0.0024352598782685863, -...   \n",
-       "3  [0.013242882478240212, 0.010953106140234146, -...   \n",
-       "4  [0.010410825017237097, 0.01477699178424233, -0...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.468370                           3.463734  \n",
-       "1                         3.035534                           2.991152  \n",
-       "2                         2.902356                           2.895993  \n",
-       "3                         2.995206                           2.993596  \n",
-       "4                         1.689315                           1.689315  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "id": "connected-limit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cons_ann_3 = pd.read_csv('../data/consolidated_annotation_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "id": "trying-authorization",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tool</td>\n",
-       "      <td>implement</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>5.313</td>\n",
-       "      <td>4.688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>arrangement</td>\n",
-       "      <td>accommodation</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.304</td>\n",
-       "      <td>5.125</td>\n",
-       "      <td>4.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>l</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>7.938</td>\n",
-       "      <td>2.063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>benchmark</td>\n",
-       "      <td>index</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>1.095</td>\n",
-       "      <td>5.500</td>\n",
-       "      <td>4.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>practice</td>\n",
-       "      <td>institution</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L,I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.447</td>\n",
-       "      <td>3.563</td>\n",
-       "      <td>6.438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Word 1         Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0         tool      implement      1   NaN      3     U      1   NaN      2   \n",
-       "1  arrangement  accommodation      1   NaN      4   NaN      2     I      1   \n",
-       "2         wood         forest      3     H      1   NaN      1   NaN      1   \n",
-       "3    benchmark          index      2     I      2     I      2     I      4   \n",
-       "4     practice    institution      2     I      3   L,I      3     I      3   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg  Stdev  H_orig  H_reversed  \n",
-       "0     i      1   NaN  1.6  0.894   5.313       4.688  \n",
-       "1   NaN      1   NaN  1.8  1.304   5.125       4.875  \n",
-       "2   NaN      3     l  1.8  1.095   7.938       2.063  \n",
-       "3   NaN      4   NaN  2.8  1.095   5.500       4.500  \n",
-       "4     i      3     i  2.8  0.447   3.563       6.438  "
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cons_ann_3.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "id": "political-laptop",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(cons_ann_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "id": "improved-relaxation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ca_cols = cons_ann_3.columns\n",
-    "for _,row in cons_ann_3.iterrows():\n",
-    "#     if ((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum() == 2:\n",
-    "#         print(row['Word 1'], row['Word 2'])\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2']))])\n",
-    "#     print (((wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])).sum())\n",
-    "    for col in ca_cols:\n",
-    "        if col in ['Word 1', 'Word 2']:\n",
-    "            continue\n",
-    "        \n",
-    "        wordSim353AnnotDF_New_Merged_DF.at[wordSim353AnnotDF_New_Merged_DF.index[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])], col] = row[col]\n",
-    "#         print(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Word 1'] == row['Word 1']) & (wordSim353AnnotDF_New_Merged_DF['Word 2'] == row['Word 2'])][col])\n",
-    "#     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "id": "relevant-dodge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[~wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2']].duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outside-letters",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "pretty-scholar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "gorgeous-syndrome",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'ID',\n",
-    " 'H_Sim',\n",
-    " 'H_Dim',\n",
-    " 'F_Sim',\n",
-    " 'F_Dim',\n",
-    " 'N_Sim',\n",
-    " 'N_Dim',\n",
-    " 'D_Sim',\n",
-    " 'D_Dim',\n",
-    " 'P_Sim',\n",
-    " 'P_Dim',\n",
-    " 'Avg',\n",
-    " 'Stdev',\n",
-    " 'H_orig',\n",
-    " 'H_reversed',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].to_csv('../data/wordsim353_all_embeddings_with_retrofits_scores_only_new.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ahead-syndication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "racial-behalf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.580594\n",
-       "std        0.457960\n",
-       "min        0.000000\n",
-       "25%        0.222619\n",
-       "50%        0.495179\n",
-       "75%        0.852326\n",
-       "max        5.000000\n",
-       "Name: transe_19k_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_old_cosSim_Diff'],ascending=False)['transe_19k_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.645644\n",
-       "std        0.526932\n",
-       "min        0.000000\n",
-       "25%        0.250461\n",
-       "50%        0.562808\n",
-       "75%        0.927608\n",
-       "max        5.000000\n",
-       "Name: transe_19k_1_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['transe_19k_1_new_cosSim_Diff'],ascending=False)['transe_19k_1_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.495179:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.562808:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'transe_19k_old_cosSim', 'transe_19k_1_new_cosSim', 'transe_19k_old_cosSim_Diff', 'transe_19k_1_new_cosSim_Diff', 'transe_19k_old_cosSim_Diff_Cat', 'transe_19k_1_new_cosSim_Diff_Cat']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff</th>\n",
-       "      <th>transe_19k_old_cosSim_Diff_Cat</th>\n",
-       "      <th>transe_19k_1_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>0.523697</td>\n",
-       "      <td>0.122335</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>0.496332</td>\n",
-       "      <td>0.647819</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>0.688666</td>\n",
-       "      <td>0.473791</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>0.118237</td>\n",
-       "      <td>0.056250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>0.064958</td>\n",
-       "      <td>0.119978</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.807539</td>\n",
-       "      <td>3.161937</td>\n",
-       "      <td>0.192461</td>\n",
-       "      <td>0.161937</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.310261</td>\n",
-       "      <td>2.793832</td>\n",
-       "      <td>0.489739</td>\n",
-       "      <td>1.006168</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.449602</td>\n",
-       "      <td>2.739280</td>\n",
-       "      <td>0.550398</td>\n",
-       "      <td>0.260720</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.931610</td>\n",
-       "      <td>3.541754</td>\n",
-       "      <td>1.131610</td>\n",
-       "      <td>1.741754</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.648427</td>\n",
-       "      <td>2.758156</td>\n",
-       "      <td>1.101573</td>\n",
-       "      <td>0.991844</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  transe_19k_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60               3.076303   \n",
-       "1     Arafat         terror  3.60               3.103668   \n",
-       "2        FBI    fingerprint  3.60               2.911334   \n",
-       "3        FBI  investigation  3.00               2.881763   \n",
-       "4    Harvard           Yale  2.20               2.135042   \n",
-       "..       ...            ...   ...                    ...   \n",
-       "339      war         troops  3.00               2.807539   \n",
-       "340   weapon         secret  3.80               3.310261   \n",
-       "341  weather       forecast  3.00               2.449602   \n",
-       "342     wood         forest  1.80               2.931610   \n",
-       "343     word     similarity  3.75               2.648427   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim  transe_19k_old_cosSim_Diff  \\\n",
-       "0                   3.477665                    0.523697   \n",
-       "1                   2.952181                    0.496332   \n",
-       "2                   3.126209                    0.688666   \n",
-       "3                   2.943750                    0.118237   \n",
-       "4                   2.080022                    0.064958   \n",
-       "..                       ...                         ...   \n",
-       "339                 3.161937                    0.192461   \n",
-       "340                 2.793832                    0.489739   \n",
-       "341                 2.739280                    0.550398   \n",
-       "342                 3.541754                    1.131610   \n",
-       "343                 2.758156                    1.101573   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff  transe_19k_old_cosSim_Diff_Cat  \\\n",
-       "0                        0.122335                               1   \n",
-       "1                        0.647819                               1   \n",
-       "2                        0.473791                               1   \n",
-       "3                        0.056250                               0   \n",
-       "4                        0.119978                               0   \n",
-       "..                            ...                             ...   \n",
-       "339                      0.161937                               0   \n",
-       "340                      1.006168                               0   \n",
-       "341                      0.260720                               1   \n",
-       "342                      1.741754                               1   \n",
-       "343                      0.991844                               1   \n",
-       "\n",
-       "     transe_19k_1_new_cosSim_Diff_Cat  \n",
-       "0                                   0  \n",
-       "1                                   1  \n",
-       "2                                   0  \n",
-       "3                                   0  \n",
-       "4                                   0  \n",
-       "..                                ...  \n",
-       "339                                 0  \n",
-       "340                                 1  \n",
-       "341                                 0  \n",
-       "342                                 1  \n",
-       "343                                 1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "compressed-emergency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['old_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "alert-holmes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['new_cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "psychological-walter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['cat'] = wordSim353AnnotDF_New_Merged_DF['Avg'].apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badBefore.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_cat'] != wordSim353AnnotDF_New_Merged_DF['cat']].to_csv('../data/transEmb/badAfter.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "consistent-difference",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "worldwide-genius",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "developing-option",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "catholic-compensation",
-   "metadata": {},
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
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-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
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-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
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-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "expanded-uniform",
-   "metadata": {},
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
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-       "      <th>complex_19k_2_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>abstract_19k_old_cosSim</th>\n",
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-       "      <th>...</th>\n",
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-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "indian-embassy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "vocational-nurse",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[col].apply(labelSamples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(labelSamples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(labelSamples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
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-       "12350 -1.470588  8      \n",
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-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
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-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
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-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
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-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
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-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
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-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
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-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
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-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
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-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
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-       "1035  2.613445  4      \n",
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-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
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-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
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-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
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-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
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-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
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-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
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-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
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-     },
-     "execution_count": 259,
-     "metadata": {},
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-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
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-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <td>7</td>\n",
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-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
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-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
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-     "metadata": {},
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-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
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-   "execution_count": 261,
-   "id": "binary-committee",
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-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
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-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "215px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
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-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
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-   "window_display": false
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- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Retrofitting using 19k + 19k dataset.ipynb b/Retrofitting using 19k + 19k dataset.ipynb
deleted file mode 100644
index 9dd967d..0000000
--- a/Retrofitting using 19k + 19k dataset.ipynb	
+++ /dev/null
@@ -1,1689 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "objective-saturday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "joint-magnet",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1_label</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2_label</th>\n",
-       "      <th>sent</th>\n",
-       "      <th>node1_emb</th>\n",
-       "      <th>node2_emb</th>\n",
-       "      <th>bert2SentSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>textEmb_7props_left</th>\n",
-       "      <th>textEmb_7props_right</th>\n",
-       "      <th>textEmb_2props_left</th>\n",
-       "      <th>textEmb_2props_right</th>\n",
-       "      <th>textEmb_7props_cosSim</th>\n",
-       "      <th>textEmb_2props_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q100171002</td>\n",
-       "      <td>Q1503443</td>\n",
-       "      <td>Q1503443-P279-Q100171002-8282ae74-0</td>\n",
-       "      <td>secrecy</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>concealment</td>\n",
-       "      <td>secrecy is concealment</td>\n",
-       "      <td>[ 4.83600467e-01  1.25958338e-01  1.84784138e+...</td>\n",
-       "      <td>[ 3.61319035e-01  1.03393383e-01  1.50166345e+...</td>\n",
-       "      <td>0.901134</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.87660795, 0.4991357, -0.39552155, 0.7046891...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>[0.79082793, 0.53679967, -0.3825465, 0.6778466...</td>\n",
-       "      <td>[0.36156428, -0.015657336, -0.14483449, -0.253...</td>\n",
-       "      <td>0.832602</td>\n",
-       "      <td>0.823419</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q1002697</td>\n",
-       "      <td>Q49850</td>\n",
-       "      <td>Q49850-P279-Q1002697-0c52cf68-0</td>\n",
-       "      <td>journal</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>periodical</td>\n",
-       "      <td>journal is periodical</td>\n",
-       "      <td>[-5.87019742e-01 -3.76376987e-01  2.21003819e+...</td>\n",
-       "      <td>[-2.23196611e-01 -4.97725368e-01  2.20271492e+...</td>\n",
-       "      <td>0.863449</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.28667202591896057, -0.4099476933479309, 0....</td>\n",
-       "      <td>[-0.25435054302215576, -0.92085862159729, 1.68...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[0.92698413, 0.32396156, -0.7647308, 0.6973268...</td>\n",
-       "      <td>[1.256428, 0.3254261, -0.26960722, 0.101883665...</td>\n",
-       "      <td>[1.0344226, 0.39984593, -0.76180136, 0.8256311...</td>\n",
-       "      <td>0.742638</td>\n",
-       "      <td>0.734428</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q101998</td>\n",
-       "      <td>Q4421</td>\n",
-       "      <td>Q4421-P279-Q101998-d0983886-0</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>biome</td>\n",
-       "      <td>forest is biome</td>\n",
-       "      <td>[-4.80742343e-02 -4.51617390e-01  1.48896754e+...</td>\n",
-       "      <td>[ 5.09670794e-01 -4.42380428e-01  1.53245163e+...</td>\n",
-       "      <td>0.507148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.12320789694786072, 0.2723684310913086, 0.05...</td>\n",
-       "      <td>[0.5991100072860718, 0.15195603668689728, 0.59...</td>\n",
-       "      <td>[0.8141036, 0.14491142, -0.59703183, 0.335001,...</td>\n",
-       "      <td>[-0.16750671, -0.17960861, -0.58211946, -0.687...</td>\n",
-       "      <td>[0.91628444, 0.104261845, -0.49683735, 0.21296...</td>\n",
-       "      <td>[-0.13534825, -0.23398273, -0.5165668, -0.7297...</td>\n",
-       "      <td>0.647031</td>\n",
-       "      <td>0.707611</td>\n",
-       "      <td>0.508060</td>\n",
-       "      <td>0.455747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q102165</td>\n",
-       "      <td>Q2294</td>\n",
-       "      <td>Q2294-P279-Q102165-0893a180-0</td>\n",
-       "      <td>proton</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>nucleon</td>\n",
-       "      <td>proton is nucleon</td>\n",
-       "      <td>[-3.39149237e-01  2.01575115e-01  1.08608294e+...</td>\n",
-       "      <td>[-6.87960759e-02 -1.65659860e-01  9.53789592e-...</td>\n",
-       "      <td>0.798707</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.17410606145858765, 0.6839145421981812, -0.8...</td>\n",
-       "      <td>[-0.04499293863773346, 0.6921464204788208, -0....</td>\n",
-       "      <td>[0.469377, 0.7162001, -0.36740896, 0.76742333,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>[0.4080446, 0.7498908, -0.2665757, 0.74437106,...</td>\n",
-       "      <td>[-0.11307852, 0.65730673, -0.13959256, 1.46633...</td>\n",
-       "      <td>0.836531</td>\n",
-       "      <td>0.847591</td>\n",
-       "      <td>0.855959</td>\n",
-       "      <td>0.770030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q102205</td>\n",
-       "      <td>Q11435</td>\n",
-       "      <td>Q11435-P279-Q102205-7b448d05-0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>P279</td>\n",
-       "      <td>fluid</td>\n",
-       "      <td>liquid is fluid</td>\n",
-       "      <td>[-5.40423356e-02 -1.01775277e+00  2.21057415e+...</td>\n",
-       "      <td>[ 1.30038410e-01 -8.53226840e-01  2.37821031e+...</td>\n",
-       "      <td>0.934246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.4393930733203888, 0.7159943580627441, 0.647...</td>\n",
-       "      <td>[0.5091343522071838, -0.05845202878117561, 1.0...</td>\n",
-       "      <td>[-0.19995248, 0.65605164, -0.14234662, 0.43931...</td>\n",
-       "      <td>[0.4293771, 1.1976917, 0.04422909, 0.5975481, ...</td>\n",
-       "      <td>[-0.45549682, 0.73478854, -0.1980287, 0.436793...</td>\n",
-       "      <td>[0.33631665, 1.1661042, 0.13744242, 0.6776439,...</td>\n",
-       "      <td>0.765589</td>\n",
-       "      <td>0.780342</td>\n",
-       "      <td>0.752693</td>\n",
-       "      <td>0.515319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node2     node1                                   id node1_label  \\\n",
-       "0  Q100171002  Q1503443  Q1503443-P279-Q100171002-8282ae74-0     secrecy   \n",
-       "1    Q1002697    Q49850      Q49850-P279-Q1002697-0c52cf68-0     journal   \n",
-       "2     Q101998     Q4421        Q4421-P279-Q101998-d0983886-0      forest   \n",
-       "3     Q102165     Q2294        Q2294-P279-Q102165-0893a180-0      proton   \n",
-       "4     Q102205    Q11435       Q11435-P279-Q102205-7b448d05-0      liquid   \n",
-       "\n",
-       "  label  node2_label                    sent  \\\n",
-       "0  P279  concealment  secrecy is concealment   \n",
-       "1  P279   periodical   journal is periodical   \n",
-       "2  P279        biome         forest is biome   \n",
-       "3  P279      nucleon       proton is nucleon   \n",
-       "4  P279        fluid         liquid is fluid   \n",
-       "\n",
-       "                                           node1_emb  \\\n",
-       "0  [ 4.83600467e-01  1.25958338e-01  1.84784138e+...   \n",
-       "1  [-5.87019742e-01 -3.76376987e-01  2.21003819e+...   \n",
-       "2  [-4.80742343e-02 -4.51617390e-01  1.48896754e+...   \n",
-       "3  [-3.39149237e-01  2.01575115e-01  1.08608294e+...   \n",
-       "4  [-5.40423356e-02 -1.01775277e+00  2.21057415e+...   \n",
-       "\n",
-       "                                           node2_emb  bert2SentSim  ...  \\\n",
-       "0  [ 3.61319035e-01  1.03393383e-01  1.50166345e+...      0.901134  ...   \n",
-       "1  [-2.23196611e-01 -4.97725368e-01  2.20271492e+...      0.863449  ...   \n",
-       "2  [ 5.09670794e-01 -4.42380428e-01  1.53245163e+...      0.507148  ...   \n",
-       "3  [-6.87960759e-02 -1.65659860e-01  9.53789592e-...      0.798707  ...   \n",
-       "4  [ 1.30038410e-01 -8.53226840e-01  2.37821031e+...      0.934246  ...   \n",
-       "\n",
-       "                             BERT_abstract_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.28667202591896057, -0.4099476933479309, 0....   \n",
-       "2  [0.12320789694786072, 0.2723684310913086, 0.05...   \n",
-       "3  [0.17410606145858765, 0.6839145421981812, -0.8...   \n",
-       "4  [0.4393930733203888, 0.7159943580627441, 0.647...   \n",
-       "\n",
-       "                   BERT_abstract_firstSent_emb_right  \\\n",
-       "0                                                NaN   \n",
-       "1  [-0.25435054302215576, -0.92085862159729, 1.68...   \n",
-       "2  [0.5991100072860718, 0.15195603668689728, 0.59...   \n",
-       "3  [-0.04499293863773346, 0.6921464204788208, -0....   \n",
-       "4  [0.5091343522071838, -0.05845202878117561, 1.0...   \n",
-       "\n",
-       "                                 textEmb_7props_left  \\\n",
-       "0  [0.87660795, 0.4991357, -0.39552155, 0.7046891...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.8141036, 0.14491142, -0.59703183, 0.335001,...   \n",
-       "3  [0.469377, 0.7162001, -0.36740896, 0.76742333,...   \n",
-       "4  [-0.19995248, 0.65605164, -0.14234662, 0.43931...   \n",
-       "\n",
-       "                                textEmb_7props_right  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...   \n",
-       "1  [0.92698413, 0.32396156, -0.7647308, 0.6973268...   \n",
-       "2  [-0.16750671, -0.17960861, -0.58211946, -0.687...   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...   \n",
-       "4  [0.4293771, 1.1976917, 0.04422909, 0.5975481, ...   \n",
-       "\n",
-       "                                 textEmb_2props_left  \\\n",
-       "0  [0.79082793, 0.53679967, -0.3825465, 0.6778466...   \n",
-       "1  [1.256428, 0.3254261, -0.26960722, 0.101883665...   \n",
-       "2  [0.91628444, 0.104261845, -0.49683735, 0.21296...   \n",
-       "3  [0.4080446, 0.7498908, -0.2665757, 0.74437106,...   \n",
-       "4  [-0.45549682, 0.73478854, -0.1980287, 0.436793...   \n",
-       "\n",
-       "                                textEmb_2props_right textEmb_7props_cosSim  \\\n",
-       "0  [0.36156428, -0.015657336, -0.14483449, -0.253...              0.832602   \n",
-       "1  [1.0344226, 0.39984593, -0.76180136, 0.8256311...              0.742638   \n",
-       "2  [-0.13534825, -0.23398273, -0.5165668, -0.7297...              0.647031   \n",
-       "3  [-0.11307852, 0.65730673, -0.13959256, 1.46633...              0.836531   \n",
-       "4  [0.33631665, 1.1661042, 0.13744242, 0.6776439,...              0.765589   \n",
-       "\n",
-       "  textEmb_2props_cosSim BERT_abstract_cosSim BERT_abstract_firstSent_cosSim  \n",
-       "0              0.823419            -1.000000                      -1.000000  \n",
-       "1              0.734428            -1.000000                      -1.000000  \n",
-       "2              0.707611             0.508060                       0.455747  \n",
-       "3              0.847591             0.855959                       0.770030  \n",
-       "4              0.780342             0.752693                       0.515319  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "incorporated-ambassador",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'id', 'node1_label', 'label', 'node2_label', 'sent',\n",
-       "       'node1_emb', 'node2_emb', 'bert2SentSim', 'abstract',\n",
-       "       'abstract_firstSent', 'BERT_abstract_emb',\n",
-       "       'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'textEmb_7props_left',\n",
-       "       'textEmb_7props_right', 'textEmb_2props_left', 'textEmb_2props_right',\n",
-       "       'textEmb_7props_cosSim', 'textEmb_2props_cosSim',\n",
-       "       'BERT_abstract_cosSim', 'BERT_abstract_firstSent_cosSim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "original-grass",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    19454.000000\n",
-       "mean         0.602876\n",
-       "std          0.146181\n",
-       "min          0.064503\n",
-       "25%          0.493110\n",
-       "50%          0.597949\n",
-       "75%          0.711631\n",
-       "max          1.000000\n",
-       "Name: bert2SentSim, dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p279WordSimSeededDF_wabs_text.bert2SentSim.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-occupation",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "english-english",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node1_label] = row.textEmb_7props_left\n",
-    "        embedDict[row.node2_label] = row.textEmb_7props_right\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val[1:-1].split(',')])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "#         embedDict[key] = temp\n",
-    "    return embedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "orange-person",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetchNeighbours(df):\n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1_label not in neighboursDict:\n",
-    "            neighboursDict[row.node1_label] = []\n",
-    "        neighboursDict[row.node1_label].append((row.node2_label, row.bert2SentSim))\n",
-    "        \n",
-    "        if row.node2_label not in neighboursDict:\n",
-    "            neighboursDict[row.node2_label] = []\n",
-    "        neighboursDict[row.node2_label].append((row.node1_label, row.bert2SentSim))\n",
-    "    return neighboursDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "joined-kazakhstan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def retrofit(embedDict, neighDict):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        neighbs = neighDict[word]\n",
-    "#         print([embedDict[neighb[0]] for neighb in neighbs])\n",
-    "        newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])) / ((len(neighbs)) + sum([neighb[1] for neighb in neighbs]))\n",
-    "#         newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])) / ((len(neighbs))**2 + sum([neighb[1] for neighb in neighbs]))\n",
-    "#         newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])) / ((len(neighbs))**0.5 + sum([neighb[1] for neighb in neighbs]))\n",
-    "    return newEmbedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "collected-performance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-dimension",
-   "metadata": {},
-   "source": [
-    "# Attempts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-milan",
-   "metadata": {},
-   "source": [
-    "## Sample attempt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "substantial-aggregate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text_sample = p279WordSimSeededDF_wabs_text[:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "analyzed-passion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "japanese-defense",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "dying-nigeria",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "reduced-avenue",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1034"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "advised-hollywood",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "authentic-greene",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1034.000000\n",
-       "mean        0.281990\n",
-       "std         0.054759\n",
-       "min         0.069792\n",
-       "25%         0.244698\n",
-       "50%         0.278019\n",
-       "75%         0.319656\n",
-       "max         0.450444\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "variable-transportation",
-   "metadata": {},
-   "source": [
-    "## ChildPar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "helpful-wellington",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp1 = p279WordSimSeededDF_wabs_text.apply(lambda p: (p.node1, p.node1_label), axis=1).to_list() + p279WordSimSeededDF_wabs_text.apply(lambda p: (p.node2, p.node2_label), axis=1).to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "sorted-capability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp1 = p279WordSimSeededDF_wabs_text.apply(lambda p: (p.node1, p.node1_label), axis=1).to_list() + p279WordSimSeededDF_wabs_text.apply(lambda p: (p.node2, p.node2_label), axis=1).to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "senior-psychology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temp1Dict = {}\n",
-    "# for node, val in temp1:\n",
-    "#     print(node, val)\n",
-    "#     if node not in temp1Dict:\n",
-    "#         temp1Dict[node] = val\n",
-    "#     else:\n",
-    "#         if temp1Dict[node] != val:\n",
-    "#             print(node,val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "frequent-sight",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18762"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# len(temp1Dict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "central-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "white-genesis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(p279WordSimSeededDF_wabs_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "registered-original",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "experienced-conspiracy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "every-interface",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "specified-berry",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.283712\n",
-       "std          0.056573\n",
-       "min          0.050719\n",
-       "25%          0.246137\n",
-       "50%          0.281897\n",
-       "75%          0.319687\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "divine-chair",
-   "metadata": {},
-   "source": [
-    "## Concatenated ChildPar + Siblings Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "fourth-exhibit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedDict = fetchEmbeddings(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "hungry-cream",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "leading-invasion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newEmbedDict = retrofit(embedDict, neighDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "id": "protecting-devon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18860"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(embedDict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "prescribed-right",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dists = determineDistances(embedDict, newEmbedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "important-university",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    18860.000000\n",
-       "mean         0.272088\n",
-       "std          0.057231\n",
-       "min          0.050719\n",
-       "25%          0.231393\n",
-       "50%          0.267868\n",
-       "75%          0.308657\n",
-       "max          0.564922\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(dists).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "single-scope",
-   "metadata": {},
-   "source": [
-    "# Correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "id": "opposite-amino",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "looking-wonder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>admission</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>4.4640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>5.8750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>3.3750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.9375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>news</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>2.8125</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID        Word 1     Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   1     admission     ticket      3     U      3     U      3     U      3   \n",
-       "1   2       alcohol  chemistry      3     D      3     D      3     D      4   \n",
-       "2   3      aluminum      metal      2     I      3     S      2     I      3   \n",
-       "3   4  announcement     effort      4   NaN      4   NaN      4   NaN      4   \n",
-       "4   5  announcement       news      2     I      2     I      3     I      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed  \n",
-       "0     u    3.0     U  3.0  0.000000  5.5360      4.4640  \n",
-       "1   NaN    3.0     H  3.2  0.447214  4.1250      5.8750  \n",
-       "2     i    2.0     I  2.4  0.547723  6.6250      3.3750  \n",
-       "3   NaN    4.0   NaN  4.0  0.000000  2.0625      7.9375  \n",
-       "4     i    2.0     I  2.2  0.447214  7.1875      2.8125  "
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "choice-hurricane",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "secret-photography",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "patent-blade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['Word 1'] in embedDict and p['Word 2'] in embedDict, axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "referenced-gossip",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-151-37f43aeff81a>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['Word 1']]).reshape(1,-1), np.array(embedDict[p['Word 2']]).reshape(1,-1))[0][0], axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['Word 1']]).reshape(1,-1), np.array(embedDict[p['Word 2']]).reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "traditional-bailey",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-152-8a6d0faea853>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['Word 1']]).reshape(1,-1), np.array(newEmbedDict[p['Word 2']]).reshape(1,-1))[0][0], axis=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['Word 1']]).reshape(1,-1), np.array(newEmbedDict[p['Word 2']]).reshape(1,-1))[0][0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "hollow-increase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>category</th>\n",
-       "      <th>textOld</th>\n",
-       "      <th>textNew</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>3.3750</td>\n",
-       "      <td>M</td>\n",
-       "      <td>0.640705</td>\n",
-       "      <td>0.768632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>news</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>2.8125</td>\n",
-       "      <td>M</td>\n",
-       "      <td>0.855666</td>\n",
-       "      <td>0.867691</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>production</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.6875</td>\n",
-       "      <td>8.3125</td>\n",
-       "      <td>U</td>\n",
-       "      <td>0.622575</td>\n",
-       "      <td>0.690300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>warning</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.6250</td>\n",
-       "      <td>5.3750</td>\n",
-       "      <td>M</td>\n",
-       "      <td>0.666838</td>\n",
-       "      <td>0.741316</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>architecture</td>\n",
-       "      <td>century</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>1.9290</td>\n",
-       "      <td>8.0710</td>\n",
-       "      <td>U</td>\n",
-       "      <td>0.264764</td>\n",
-       "      <td>0.458752</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID        Word 1      Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "2   3      aluminum       metal      2     I      3     S      2     I      3   \n",
-       "4   5  announcement        news      2     I      2     I      3     I      2   \n",
-       "5   6  announcement  production      4   NaN      4   NaN      4   NaN      4   \n",
-       "6   7  announcement     warning      2     I      3     I      3     I      3   \n",
-       "9  10  architecture     century      4   NaN      3     H      4   NaN      4   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed category   textOld  \\\n",
-       "2     i    2.0     I  2.4  0.547723  6.6250      3.3750        M  0.640705   \n",
-       "4     i    2.0     I  2.2  0.447214  7.1875      2.8125        M  0.855666   \n",
-       "5   NaN    4.0   NaN  4.0  0.000000  1.6875      8.3125        U  0.622575   \n",
-       "6     i    2.0     I  2.6  0.547723  4.6250      5.3750        M  0.666838   \n",
-       "9   NaN    4.0   NaN  3.8  0.447214  1.9290      8.0710        U  0.264764   \n",
-       "\n",
-       "    textNew  \n",
-       "2  0.768632  \n",
-       "4  0.867691  \n",
-       "5  0.690300  \n",
-       "6  0.741316  \n",
-       "9  0.458752  "
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "charming-double",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-154-9282f100fe8d>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-154-9282f100fe8d>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "orange-justice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "204"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "strong-steal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "satisfied-crisis",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.40669723469267965, pvalue=2.159786077235558e-16)"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "encouraging-theorem",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.40392814495955587, pvalue=3.436121838707459e-16)"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "alert-priest",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.4470576546988487, pvalue=2.8893276225801627e-21)"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "genetic-visiting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.4545191133336838, pvalue=6.291422918165968e-22)"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "effective-bronze",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "perfect-priority",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5637254901960784"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "missing-track",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4803921568627451"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2['Word 1'], wordSim353AnnotDF_New2['Word 2']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList.apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList.apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList.to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Retrofitting using Master datasets-Copy1.ipynb b/Retrofitting using Master datasets-Copy1.ipynb
deleted file mode 100644
index 3567de4..0000000
--- a/Retrofitting using Master datasets-Copy1.ipynb	
+++ /dev/null
@@ -1,8003 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import scipy.stats as stats\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from time import time\n",
-    "from datetime import date\n",
-    "import warnings\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "from sklearn.model_selection import StratifiedKFold, KFold\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC, SVR\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error, classification_report, plot_confusion_matrix\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "warnings.filterwarnings('ignore')\n",
-    "# pd.set_option('mode.chained_assignment', None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "clinical-orleans",
-   "metadata": {},
-   "source": [
-    "# Retrofitting Pre-Req Class Definitions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continued-meeting",
-   "metadata": {},
-   "source": [
-    "## Utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ultimate-worship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Utils:\n",
-    "    \"\"\"\n",
-    "    This contains all the utility functions needed by any part of retrofitting\n",
-    "    \"\"\"\n",
-    "    _today = date.today()\n",
-    "    today_date = _today.strftime(\"%b_%d_%Y\")\n",
-    "    LABELS = ['I','U','M']\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def normalize(cls, embed_dict):\n",
-    "        for key, val in embed_dict.items():\n",
-    "            temp = np.array([float(val1) for val1 in val])\n",
-    "            temp2 = temp**2\n",
-    "            embed_dict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def fetch_embeddings(cls, df):\n",
-    "        embed_dict = {}\n",
-    "        for _, row in df.iterrows():\n",
-    "            embed_dict[row.node] = row.value\n",
-    "        return normalize(embed_dict)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def fill_coverage(cls, embed_dict):\n",
-    "        # TODO: Coverage for all datasets\n",
-    "        wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        wordSim353AnnotDF_set = set(wordSim353AnnotDF_New['word1_kg_id'].to_list() + wordSim353AnnotDF_New['word2_kg_id'].to_list())\n",
-    "        embed_size = len(embed_dict[next(iter(embed_dict))])\n",
-    "        count = 0\n",
-    "        for word in wordSim353AnnotDF_set:\n",
-    "            if word not in embed_dict:\n",
-    "                embed_dict[word] = np.zeros((embed_size))\n",
-    "                count += 1\n",
-    "        print(f\"Added {count} corrections\")\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def check_coverage(cls, embed_dict):\n",
-    "        wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        wordSim353AnnotDF_set = set(wordSim353AnnotDF_New['word1_kg_id'].to_list() + wordSim353AnnotDF_New['word2_kg_id'].to_list())\n",
-    "        embed_size = len(embed_dict[next(iter(embed_dict))])\n",
-    "        count = 0\n",
-    "        for word in wordSim353AnnotDF_set:\n",
-    "            if word not in embed_dict:\n",
-    "                count += 1\n",
-    "        return (len(wordSim353AnnotDF_New) - count)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def determine_distances(cls, embed_dict, new_embed_dict):\n",
-    "        dist = []\n",
-    "        for word in embed_dict.keys():\n",
-    "            dist.append(euclidean_distances([embed_dict[word]], [new_embed_dict[word]])[0][0])\n",
-    "        return dist\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def serialize_embedding_dict(cls, embed_dict):\n",
-    "        for key2 in embed_dict.keys():\n",
-    "            embed_dict[key2] = embed_dict[key2].tolist() if type(embed_dict[key2]) != list else embed_dict[key2]\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def deserialize_embedding_dict(cls, embed_dict):\n",
-    "        for key2 in embed_dict.keys():\n",
-    "            embed_dict[key2] = np.array(embed_dict[key2])\n",
-    "        return embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def label_samples(cls, score):\n",
-    "        return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def determine_cos_sim(cls, emb1, emb2):\n",
-    "        return cosine_similarity(\n",
-    "                np.array(emb1).reshape(1,-1), \n",
-    "                np.array(emb2).reshape(1,-1)\n",
-    "            )[0][0]\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stuffed-rebate",
-   "metadata": {},
-   "source": [
-    "## InputEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "hollywood-stomach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class InputEmbeddings:\n",
-    "    \"\"\"\n",
-    "    Instance variables:\n",
-    "        - embed_dict_master - holds all qnode to embedding mappings as a dictionary\n",
-    "        - embedding_names - list of all keys of the above dictionary\n",
-    "    \"\"\"\n",
-    "    def __init__(self, has_embeddings_include: bool = False):\n",
-    "        self.embed_dict_master = {}\n",
-    "        self.embedding_names = ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent']\n",
-    "        if has_embeddings_include: # TODO\n",
-    "            self.embedding_names += ['h', 'a', 's']\n",
-    "        self.embedding_lengths = {}\n",
-    "        \n",
-    "        for emb_key in tqdm(self.embedding_names, desc='Input Embeddings'):\n",
-    "            self.embed_dict_master[emb_key] = self.fetch_embedding(emb_key)\n",
-    "            \n",
-    "        self.fetch_embedding_stats()\n",
-    "        print(\"Fetched all input embeddings\")\n",
-    "\n",
-    "    def fetch_embedding(self, emb_key):\n",
-    "        return Utils.fill_coverage(\n",
-    "                Utils.deserialize_embedding_dict(\n",
-    "                    json.load(open('../data/Master_P279_dataset/embeddings/'+emb_key+'_orig_embedding_dict_updated.json'))\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "    def fetch_embedding_stats(self):\n",
-    "        for emb_name in embed_dict_master.keys():\n",
-    "            embedding_lengths[emb_name] = len(next(iter(embedDictMaster[emb_name].values())))\n",
-    "            print(f\"Embedding: {emb_name}, Length: {embedding_lengths[emb_name]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "civil-exhaust",
-   "metadata": {},
-   "source": [
-    "## NeighborDatasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "saving-banking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NeighborDatasets:\n",
-    "    \"\"\"\n",
-    "    Instance variables:\n",
-    "        - neighbors_dict_master - holds all qnode to neighbor qnode mappings as a dictionary\n",
-    "        - basis_list - list of all keys of the above dictionary\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    def __init__(self, class_datasets_fetch: bool = False, probase_datasets_fetch: bool = False):\n",
-    "        self.neighbors_dict_master = {}\n",
-    "        \n",
-    "        pbar = tqdm(desc='Neighbor Datasets', total = \n",
-    "                    3 \n",
-    "                    + (3 if class_datasets_fetch else 0) \n",
-    "                    + (2 if probase_datasets_fetch else 0) \n",
-    "                   )\n",
-    "        \n",
-    "        bert_P279_child_par_df = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered.csv')\n",
-    "        bert_P279_siblings_df = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_min_cols_with_desc_dups_removed.csv')\n",
-    "        \n",
-    "        self.neighbors_dict_master['bert_child_par'] = self.fetch_neighbours(bert_P279_child_par_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['bert_siblings'] = self.fetch_neighbours(bert_P279_siblings_df)\n",
-    "        pbar.update(1)\n",
-    "        self.neighbors_dict_master['bert_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                bert_P279_child_par_df, bert_P279_siblings_df\n",
-    "            ]))\n",
-    "        pbar.update(1)\n",
-    "            \n",
-    "        if class_datasets_fetch:\n",
-    "            class_P279_child_par_df = pd.read_csv('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_w_classSim.csv')\n",
-    "            class_P279_child_par_df['similarity_value'] = class_P279_child_par_df['classSim']\n",
-    "            \n",
-    "            class_P279_siblings_df = pd.read_csv('../data/Master_P279_dataset/P279Siblings_transP279_filtered_w_classSim.csv')\n",
-    "            class_P279_siblings_df['similarity_value'] = class_P279_siblings_df['classSim']\n",
-    "            \n",
-    "            self.neighbors_dict_master['class_child_par'] = self.fetch_neighbours(class_P279_child_par_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['class_siblings'] = self.fetch_neighbours(class_P279_siblings_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['class_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                    class_P279_child_par_df, class_P279_siblings_df\n",
-    "                ]))\n",
-    "            pbar.update(1)\n",
-    "\n",
-    "        if probase_datasets_fetch:\n",
-    "            probase_df = self.fetch_probase('../data/probase/probase_WQnodes_subset_and_sim.csv')\n",
-    "            \n",
-    "            self.neighbors_dict_master['probase'] = self.fetch_neighbours(probase_df)\n",
-    "            pbar.update(1)\n",
-    "            self.neighbors_dict_master['probase+bert_all'] = self.fetch_neighbours(pd.concat([\n",
-    "                    bert_P279_child_par_df, bert_P279_siblings_df, probase_df\n",
-    "                ]))\n",
-    "            pbar.update(1)\n",
-    "        \n",
-    "        self.basis_list = list(neighbors_dict_master.keys())\n",
-    "        \n",
-    "        pbar.close()\n",
-    "\n",
-    "        print(f\"Fetched neighbour datasets: {self.basis_list}\")\n",
-    "    \n",
-    "    def process_probase(self, probase_file_path):\n",
-    "        probase_df = pd.read_csv(probase_file_path)\n",
-    "        probase_df = probase_df.rename(columns={'n1_final_qnode': 'node1', 'n2_final_qnode': 'node2', 'sim': 'similarity_value'})\n",
-    "        probase_df['similarity_value'] = 0.5 + 0.5 * probase_df['similarity_value']\n",
-    "        \n",
-    "        return probase_df\n",
-    "        \n",
-    "    def fetch_neighbours(self, df):\n",
-    "        neighbours_dict = {}\n",
-    "        for _, row in df.iterrows():\n",
-    "            if row.node1 not in neighbours_dict:\n",
-    "                neighbours_dict[row.node1] = []\n",
-    "            neighbours_dict[row.node1].append((row.node2, row.bert2SentSim))\n",
-    "\n",
-    "            if row.node2 not in neighbours_dict:\n",
-    "                neighbours_dict[row.node2] = []\n",
-    "            neighbours_dict[row.node2].append((row.node1, row.bert2SentSim))\n",
-    "#         print(max([len(neigh) for neigh in neighbours_dict.values()]))\n",
-    "        \n",
-    "        return neighbours_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tired-pitch",
-   "metadata": {},
-   "source": [
-    "## EvaluationDatasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "rough-cliff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EvaluationDatasets:\n",
-    "    def __init__(self):\n",
-    "        self.wordsim_df = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "        self.fetch_distribution_stats(\"Wordsim-353\", self.wordsim_df)\n",
-    "        \n",
-    "        self.wiki_cs_df = pd.read_csv('../data/wikidata-cs_categorized.csv')\n",
-    "        self.fetch_distribution_stats(\"Wikidata CS\", self.wiki_cs_df)\n",
-    "        \n",
-    "        self.concept_net_df = pd.read_csv('../data/kgtk_conceptnet_evaluation.csv')\n",
-    "        self.fetch_distribution_stats(\"Concept Net\", self.concept_net_df)\n",
-    "        \n",
-    "    def fetch_distribution_stats(self, name, dataset):\n",
-    "        print(f\"Dataset: {name}\")\n",
-    "        print(dataset.category.value_counts())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cleared-color",
-   "metadata": {},
-   "source": [
-    "## ResultMetrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "returning-belarus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ResultMetrics:\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def compute_classification_results(\n",
-    "            embed_dict, \n",
-    "            eval_dataset,\n",
-    "            get_output_values: bool,\n",
-    "            old_accuracy = None\n",
-    "            ):\n",
-    "        \"\"\"\n",
-    "        Inputs:\n",
-    "            - embed_dict - dictionary of qnodes with node embeddings as its values\n",
-    "            - eval_dataset - evaluation dataset as pandas dataframe that must have the \n",
-    "                following columns for this function to work correctly:\n",
-    "                * word1_kg_id - Qnode of node1 in the evaluation pair\n",
-    "                * word2_kg_id - Qnode of node2 in the evaluation pair\n",
-    "                * category - Category of the evaluation pair. One of the labels: I/U/M\n",
-    "        Outputs:\n",
-    "            - response_dict - Returns a dictionary with the following keys:\n",
-    "                * covered_pairs - Indicates the number of pairs of the evaluation dataset that the \n",
-    "                    embedding dictionary can cover\n",
-    "                \n",
-    "        \"\"\"\n",
-    "        response_dict = {}\n",
-    "        \n",
-    "        eval_dataset = eval_dataset.copy()\n",
-    "\n",
-    "        missing_words_set = set(\n",
-    "            eval_dataset[eval_dataset.word1_kg_id.apply(lambda p: p not in embed_dict)].word1_kg_id.to_list() \n",
-    "            + eval_dataset[eval_dataset.word2_kg_id.apply(lambda p: p not in embed_dict)].word2_kg_id.to_list()\n",
-    "        )\n",
-    "        \n",
-    "        response_dict['covered_pairs'] = len(eval_dataset)\n",
-    "\n",
-    "        eval_dataset['embedding_cos_sim'] = eval_dataset.apply(lambda p: determine_cos_sim(embed_dict[p['word1_kg_id']], embed_dict[p['word2_kg_id']]) \n",
-    "                                                   if p['word1_kg_id'] in embed_dict and p['word2_kg_id'] in embed_dict \n",
-    "                                                   else None, axis=1)\n",
-    "        \n",
-    "        eval_dataset['embedding_cos_sim'].fillna(eval_dataset['embedding_cos_sim'].mean(skipna=True), inplace=True)\n",
-    "        \n",
-    "        # Scale abs value of cosine similarities to 1,4 strictly\n",
-    "        eval_dataset['embedding_cos_sim'] = eval_dataset['embedding_cos_sim'].apply(lambda p: 4 - 3 * abs(p))\n",
-    "\n",
-    "#         response_dict['KT_old_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['Avg'])\n",
-    "#         response_dict['KT_new_vs_Avg'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['Avg'])\n",
-    "#         response_dict['KT_old_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textOld'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "#         response_dict['KT_new_vs_Human'] = stats.kendalltau(wordSim353AnnotDF_New['textNew'], wordSim353AnnotDF_New['H_reversed'])\n",
-    "        response_dict['accuracy'] = 100 * accuracy_score(\n",
-    "                eval_dataset['category'],\n",
-    "                eval_dataset['embedding_cos_sim'].apply(label_samples)\n",
-    "            )\n",
-    "    \n",
-    "        response_dict['classification_report'] = classification_report(\n",
-    "                eval_dataset['category'], \n",
-    "                eval_dataset['embedding_cos_sim'].apply(label_samples), \n",
-    "                output_dict=True\n",
-    "            )\n",
-    "\n",
-    "        response_dict['conf_matrix'] = confusion_matrix(\n",
-    "                eval_dataset['category'], \n",
-    "                eval_dataset['embedding_cos_sim'].apply(label_samples), \n",
-    "                labels=LABELS\n",
-    "            )\n",
-    "        \n",
-    "        if old_accuracy is not None:\n",
-    "            response_dict['increase_acc'] = response_dict['accuracy'] - old_accuracy\n",
-    "        else:\n",
-    "            response_dict['increase_acc'] = None\n",
-    "        \n",
-    "        if get_output_values:\n",
-    "            response_dict['preds'] = eval_dataset['embedding_cos_sim'].apply(label_samples)\n",
-    "\n",
-    "        return response_dict, \\\n",
-    "                (response_dict['covered_pairs'],  \\\n",
-    "                 response_dict['accuracy'], \\\n",
-    "                 response_dict['increase_acc'], \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['I']['precision'],  \\\n",
-    "                 response_dict['classification_report']['I']['recall'],  \\\n",
-    "                 response_dict['classification_report']['I']['f1-score'],   \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['M']['precision'],  \\\n",
-    "                 response_dict['classification_report']['M']['recall'],  \\\n",
-    "                 response_dict['classification_report']['M']['f1-score'], \\\n",
-    "                 \n",
-    "                 response_dict['classification_report']['U']['precision'],  \\\n",
-    "                 response_dict['classification_report']['U']['recall'],  \\\n",
-    "                 response_dict['classification_report']['U']['f1-score'])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flying-raleigh",
-   "metadata": {},
-   "source": [
-    "## RetrofittingProcedures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "interior-incidence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RetrofittingProcedures:\n",
-    "    \n",
-    "    np_label_samples = np.vectorize(Utils.label_samples)\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def retrofit(embed_dict, neighbors_dict, weight_case, weight_assignment=False):\n",
-    "        new_embed_dict = {}\n",
-    "        for word in embed_dict.keys():\n",
-    "            if word in neighbors_dict:\n",
-    "                neighbs = neighbors_dict[word]\n",
-    "                neighbs = list(filter(lambda p: p[0] in embed_dict, neighbs))\n",
-    "                if len(neighbs) == 0:\n",
-    "                    new_embed_dict[word] = embed_dict[word]\n",
-    "                    continue\n",
-    "                if weight_assignment:\n",
-    "                    sum_of_sims = sum([neighb[1] for neighb in neighbs])\n",
-    "                    sum_of_embs = sum([embed_dict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "                else:\n",
-    "                    sum_of_sims = sum([1 for neighb in neighbs])\n",
-    "                    sum_of_embs = sum([embed_dict[neighb[0]] for neighb in neighbs])\n",
-    "\n",
-    "                if weight_case == 1:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs)) + sum_of_embs) / ((len(neighbs)) + sum_of_sims)\n",
-    "                elif weight_case == 2:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs))**2 + sum_of_embs) / ((len(neighbs))**2 + sum_of_sims)\n",
-    "                elif weight_case == 0.5:\n",
-    "                    new_embed_dict[word] = (embed_dict[word] * (len(neighbs))**0.5 + sum_of_embs) / ((len(neighbs))**0.5 + sum_of_sims)\n",
-    "                else:\n",
-    "                    raise\n",
-    "            else:\n",
-    "                new_embed_dict[word] = embed_dict[word]\n",
-    "        return new_embed_dict\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def execute_all_unsupervised_scenarios(\n",
-    "                emb_list, basis_list, \n",
-    "                embed_dict_master, neigh_dict_master,\n",
-    "                eval_dataset,\n",
-    "                scenario_name: str,\n",
-    "                num_of_iterations: int = 3, \n",
-    "                weightedness_list: list = [True],\n",
-    "                weight_cases_list: list = [1,2],\n",
-    "                get_output_values: bool = False\n",
-    "            ):\n",
-    "        \n",
-    "        new_embed_dict_master = {}\n",
-    "        responses_dict_master = {}\n",
-    "        results = []\n",
-    "        \n",
-    "        for basis in tqdm(basis_list, desc='Basis', leave=False):\n",
-    "            for emb in tqdm(emb_list, desc='Embedding', leave=False):\n",
-    "                for weightedness in weightedness_list:\n",
-    "                    for weight_case in tqdm(weight_cases_list, desc='Weight Case', leave=False):\n",
-    "                        # Base Reference Initializations and Calculations\n",
-    "                        embed_dict = embed_dict_master[emb]\n",
-    "                        responses_dict, result_values = compute_classification_results(\n",
-    "                            embed_dict, eval_dataset, get_output_values=get_output_values, old_accuracy=None)\n",
-    "                        results.append(emb, basis, weight_case, weightedness, 0, 'base', *result_values)\n",
-    "                        old_accuracy = responses_dict['accuracy']\n",
-    "                        \n",
-    "                        for iter_num in tqdm(range(1,num_of_iterations+1), desc='Iteration', leave=False):\n",
-    "                            start_time = time()\n",
-    "                            \n",
-    "                            case_name = emb + '_' + basis + '_' + str(weight_case) + ('_weighted' if weightedness else '_unweighted')\n",
-    "                            \n",
-    "                            new_embed_dict = retrofit(embed_dict[weight_case], neigh_dict_master[basis], weight_case, weightedness)\n",
-    "                            \n",
-    "                            responses_dict, result_values = compute_classification_results(\n",
-    "                                new_embed_dict, eval_dataset, get_output_values=get_output_values, old_accuracy=old_accuracy)\n",
-    "                            \n",
-    "                            results.append([emb, basis, weight_case, weightedness, iter_num, case_name, \\\n",
-    "                                                *result_values, \\\n",
-    "                                                time() - start_time\n",
-    "                                            ])\n",
-    "                            \n",
-    "                            new_embed_dict_master[case_name] = embed_dict = new_embed_dict\n",
-    "                            responses_dict_master[case_name] = responses_dict\n",
-    "\n",
-    "        #                     if iter_num == num_of_iterations and highestOne:\n",
-    "        #                         case_name = gR[0] + '_' + gR[1] + '_' + str(gR[2]) + '_weighted'\n",
-    "        #                         new_embed_dict_master[case_name] = serializeEmbeddingDict(new_embed_dict_master[case_name])\n",
-    "        #                         highestOne = False\n",
-    "        #                         json.dump(new_embed_dict_master[case_name],open('../data/Master_P279_dataset/embeddings/new_embedding_dict_'+case_name+'.json','w'))\n",
-    "        #                         new_embed_dict_master[case_name] = deserializeEmbeddingDict(new_embed_dict_master[case_name])\n",
-    "        resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight Case', 'Weightedness', \n",
-    "                                                   'Iteration Num', 'Case Name', \\\n",
-    "                                                   'No. of Pairs Covered', 'Accuracy', 'Increase in Accuracy', \\\n",
-    "                                                   'I Precision', 'I Recall', 'I F1-Score', \\\n",
-    "                                                   'M Precision', 'M Recall', 'M F1-Score', \\\n",
-    "                                                   'U Precision', 'U Recall', 'U F1-Score', \\\n",
-    "                                                   'Time to Retrofit'])\n",
-    "        resultsDF.to_csv('../data/retrofitting/retro_results.' + scenario_name + '.'+ Utils.today_date +'.csv', index=False)\n",
-    "        \n",
-    "        return new_embed_dict_master, responses_dict_master\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def execute_supervised_scenario(\n",
-    "                eval_dataset, case, embed_dict_master, new_embed_dict_master, \n",
-    "                num_of_splits = 10, \n",
-    "                concat_mode: bool = False, SVC_or_SVR: str = 'SVC'\n",
-    "            ):\n",
-    "        \n",
-    "        X = []        \n",
-    "        \n",
-    "        ################\n",
-    "        # 2 Approaches based on argument: `concat_mode`\n",
-    "        ################\n",
-    "        \n",
-    "        if concat_mode: ########## CONCAT MODE CODE ####################\n",
-    "            case_name = \" & \".join(case['emb']) + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "            \n",
-    "            for _, row in eval_dataset.iterrows():\n",
-    "                if case['svm_input'] == 'emb':\n",
-    "                    tempX = []\n",
-    "                    for individual_emb in case['emb']:\n",
-    "                        ind_case_name = individual_emb + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "                        if case['iter_num'] != 0 and ind_case_name not in new_embed_dict_master:\n",
-    "                            return case_name, case, None\n",
-    "                        if case['iter_num'] == 0:\n",
-    "                            tempX += embed_dict_master[individual_emb][row['word1_kg_id']].tolist() + embedDictMaster[individual_emb][row['word2_kg_id']].tolist()\n",
-    "                        else:\n",
-    "                            tempX += new_embed_dict_master[ind_case_name][row['word1_kg_id']].tolist() + newEmbedDictMaster[ind_case_name][row['word2_kg_id']].tolist()\n",
-    "                    X.append(tempX)\n",
-    "                else:\n",
-    "                    tempX = []\n",
-    "                    for individual_emb in case['emb']:\n",
-    "                        ind_case_name = individual_emb + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "                        if case['iter_num'] != 0 and ind_case_name not in new_embed_dict_master:\n",
-    "                            return case_name, case, None\n",
-    "                        if case['iter_num'] == 0:\n",
-    "                            tempX.append(abs(Utils.determine_cos_sim(\n",
-    "                                embed_dict_master[individual_emb][row['word1_kg_id']], \n",
-    "                                embed_dict_master[individual_emb][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                        else:\n",
-    "                            tempX.append(abs(Utils.determine_cos_sim(\n",
-    "                                new_embed_dict_master[ind_case_name][row['word1_kg_id']],\n",
-    "                                new_embed_dict_master[ind_case_name][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    X.append(tempX)\n",
-    "\n",
-    "        else: ########## NON-CONCAT MODE CODE ####################\n",
-    "            case_name = case['emb'] + '_' + case['basis'] + '_' + str(case['weight_case']) + ('_weighted' if case['weightedness'] else '_unweighted')\n",
-    "            if case['iter_num'] != 0 and case_name not in new_embed_dict_master:\n",
-    "                return case_name, case, None\n",
-    "            for _, row in eval_dataset.iterrows():\n",
-    "                if case['svm_input'] == 'emb':\n",
-    "                    if case['iter_num'] == 0:\n",
-    "                        X.append(embed_dict_master[case['emb']][row['word1_kg_id']].tolist() + embed_dict_master[case['emb']][row['word2_kg_id']].tolist())\n",
-    "                    else:\n",
-    "                        X.append(new_embed_dict_master[case_name][row['word1_kg_id']].tolist() + new_embed_dict_master[case_name][row['word2_kg_id']].tolist())\n",
-    "                else:\n",
-    "                    if case['iter_num'] == 0:\n",
-    "                        X.append(abs(Utils.determine_cos_sim(\n",
-    "                                embed_dict_master[case['emb']][row['word1_kg_id']], \n",
-    "                                embed_dict_master[case['emb']][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    else:\n",
-    "                        X.append(abs(Utils.determine_cos_sim(\n",
-    "                                new_embed_dict_master[case_name][row['word1_kg_id']],\n",
-    "                                new_embed_dict_master[case_name][row['word2_kg_id']]\n",
-    "                            )))\n",
-    "                    \n",
-    "        X = pd.DataFrame(X)\n",
-    "        \n",
-    "        ################\n",
-    "        # 2 Approaches based on argument: `SVC_or_SVR`\n",
-    "        ################\n",
-    "        \n",
-    "        # Target split depending on SVC or SVM\n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            Y = eval_dataset['category']\n",
-    "        elif SVC_or_SVR == 'SVR':\n",
-    "            if 'Avg' not in eval_dataset.columns:\n",
-    "                raise ValueError(\"Avg column not present in the provided eval_dataset\")\n",
-    "            Y = (eval_dataset['Avg'] - 1) / 3\n",
-    "        else:\n",
-    "            raise ValueError(\"Invalid SVC_or_SVR provided\")\n",
-    "\n",
-    "        skf = StratifiedKFold(n_splits=num_of_splits, random_state=19, shuffle=True)\n",
-    "        X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "        for train_index, test_index in skf.split(X, Y):\n",
-    "            X_train_splits.append(X.iloc[train_index])\n",
-    "            X_test_splits.append(X.iloc[test_index])\n",
-    "            Y_train_splits.append(Y.iloc[train_index])\n",
-    "            Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "        preds = []\n",
-    "        tempVals = []\n",
-    "        \n",
-    "        # Classifier/Regressor training depending on SVC or SVM\n",
-    "        if SVC_or_SVR == 'SVC':\n",
-    "            for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "                clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100, max_iter=100))\n",
-    "                clf.fit(X_train1, Y_train1)\n",
-    "                preds.append(clf.predict(X_test1))\n",
-    "                \n",
-    "            acc = 0\n",
-    "            for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "                acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "            return case_name, *list(case.values()), acc/num_of_splits\n",
-    "        \n",
-    "        elif SVC_or_SVR == 'SVR':\n",
-    "            for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "                clf = make_pipeline(StandardScaler(), SVR(gamma='auto', max_iter=100))\n",
-    "                clf.fit(X_train1, Y_train1)\n",
-    "                preds.append(clf.predict(X_test1))\n",
-    "            \n",
-    "            acc = 0\n",
-    "            for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "                acc += mean_squared_error(pred * 3 + 1, Y_test1 * 3 + 1)\n",
-    "                \n",
-    "            return case_name, *list(case.values()), acc/num_of_splits\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def execute_all_supervised_scenarios(\n",
-    "                eval_dataset, case, embed_dict_master, new_embed_dict_master,\n",
-    "                num_of_splits = 10,\n",
-    "                concat_mode: bool = False, SVC_or_SVR: str = 'SVC'\n",
-    "            ):\n",
-    "\n",
-    "\n",
-    "        results = Parallel(n_jobs=3)(delayed(execute_supervised_scenario)(\n",
-    "                eval_dataset, caseDict, embed_dict_master, \n",
-    "                new_embed_dict_master,num_of_spli,\n",
-    "                concat_mode, SVC_or_SVR\n",
-    "            ) for caseDict in tqdm(svmCasesList))\n",
-    "        # print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qualified-projector",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caring-hamilton",
-   "metadata": {},
-   "source": [
-    "# The Master Controller"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sensitive-moscow",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d5fa2d2cf8f4f799e107c1f1c6c4574",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Input Embeddings:   0%|          | 0/6 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Added 11 corrections\n",
-      "Added 11 corrections\n",
-      "Added 0 corrections\n",
-      "Added 0 corrections\n"
-     ]
-    }
-   ],
-   "source": [
-    "inp = InputEmbeddings()\n",
-    "basis = NeighborDatasets()\n",
-    "evalD = EvaluationDatasets()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "arabic-commissioner",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hundred-virus",
-   "metadata": {},
-   "source": [
-    "# Evaluation Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "limited-hormone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDF = pd.read_csv('../data/pedersen2007measures_table1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "alien-happiness",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Term1</th>\n",
-       "      <th>Term1_kg_id</th>\n",
-       "      <th>Term1_kg_label</th>\n",
-       "      <th>Term1_description</th>\n",
-       "      <th>Term2</th>\n",
-       "      <th>Term2_kg_id</th>\n",
-       "      <th>Term2_kg_label</th>\n",
-       "      <th>Term2_description</th>\n",
-       "      <th>Physician</th>\n",
-       "      <th>Coder</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Renal failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>Kidney failure</td>\n",
-       "      <td>Q476921</td>\n",
-       "      <td>kidney failure</td>\n",
-       "      <td>disease where the kidneys fail to adequately f...</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Heart</td>\n",
-       "      <td>Q1072</td>\n",
-       "      <td>heart</td>\n",
-       "      <td>organ for the circulation of blood in animal c...</td>\n",
-       "      <td>Myocardium</td>\n",
-       "      <td>Q84133</td>\n",
-       "      <td>myocardium</td>\n",
-       "      <td>middle layer of the heart wall, which consists...</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Stroke</td>\n",
-       "      <td>Q12202</td>\n",
-       "      <td>stroke</td>\n",
-       "      <td>problem with the arteries supplying blood to t...</td>\n",
-       "      <td>Infarct</td>\n",
-       "      <td>Q207550</td>\n",
-       "      <td>infarction</td>\n",
-       "      <td>tissue death caused by a local lack of oxygen,...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Abortion</td>\n",
-       "      <td>Q8452</td>\n",
-       "      <td>abortion</td>\n",
-       "      <td>intentional ending of a pregnancy</td>\n",
-       "      <td>Miscarriage</td>\n",
-       "      <td>Q28693</td>\n",
-       "      <td>miscarriage</td>\n",
-       "      <td>natural death of an embryo or fetus before it ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Delusion</td>\n",
-       "      <td>Q189643</td>\n",
-       "      <td>delusion</td>\n",
-       "      <td>firm and fixed belief based on inadequate grou...</td>\n",
-       "      <td>Schizophrenia</td>\n",
-       "      <td>Q41112</td>\n",
-       "      <td>schizophrenia</td>\n",
-       "      <td>psychotic disorder characterized by emotional ...</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Term1 Term1_kg_id  Term1_kg_label  \\\n",
-       "0  Renal failure     Q476921  kidney failure   \n",
-       "1          Heart       Q1072           heart   \n",
-       "2         Stroke      Q12202          stroke   \n",
-       "3       Abortion       Q8452        abortion   \n",
-       "4       Delusion     Q189643        delusion   \n",
-       "\n",
-       "                                   Term1_description           Term2  \\\n",
-       "0  disease where the kidneys fail to adequately f...  Kidney failure   \n",
-       "1  organ for the circulation of blood in animal c...      Myocardium   \n",
-       "2  problem with the arteries supplying blood to t...         Infarct   \n",
-       "3                  intentional ending of a pregnancy     Miscarriage   \n",
-       "4  firm and fixed belief based on inadequate grou...   Schizophrenia   \n",
-       "\n",
-       "  Term2_kg_id  Term2_kg_label  \\\n",
-       "0     Q476921  kidney failure   \n",
-       "1      Q84133      myocardium   \n",
-       "2     Q207550      infarction   \n",
-       "3      Q28693     miscarriage   \n",
-       "4      Q41112   schizophrenia   \n",
-       "\n",
-       "                                   Term2_description  Physician  Coder  \n",
-       "0  disease where the kidneys fail to adequately f...        4.0    4.0  \n",
-       "1  middle layer of the heart wall, which consists...        3.3    3.0  \n",
-       "2  tissue death caused by a local lack of oxygen,...        3.0    2.8  \n",
-       "3  natural death of an embryo or fetus before it ...        3.0    3.3  \n",
-       "4  psychotic disorder characterized by emotional ...        3.0    2.2  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bioDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "parental-event",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bioDFNodesSet = set(bioDF.Term1_kg_id.to_list() + bioDF.Term2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "wicked-proportion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P279childParNodesSet = set(p279WordSimSeededDF_wabs_text.node1.to_list() + p279WordSimSeededDF_wabs_text.node2.to_list())\n",
-    "P279siblingsNodesSet = set(p279Seeded_SiblingsDF3_wabs_text.node1.to_list() + p279Seeded_SiblingsDF3_wabs_text.node2.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "pediatric-tomato",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 1)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in P279childParNodesSet or p in P279siblingsNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "transparent-diversity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 25)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probaseNodesSet = set(probDF_Qnodes_DF_WQnodes1_subset.node1.to_list() + probDF_Qnodes_DF_WQnodes1_subset.node2.to_list())\n",
-    "\n",
-    "sum(bioDF.Term1_kg_id.apply(lambda p: p in probaseNodesSet)), \\\n",
-    "sum(bioDF.Term2_kg_id.apply(lambda p: p in probaseNodesSet))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "uniform-civilian",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "voluntary-logic",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incoming-calvin",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Correlation Code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sacred-bacon",
-   "metadata": {},
-   "source": [
-    "# Dataset-Target Combinations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amended-forth",
-   "metadata": {},
-   "source": [
-    "## Retrofitting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incorporate-ecuador",
-   "metadata": {},
-   "source": [
-    "### Retrofitting using combination of input and basis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fourth-finding",
-   "metadata": {},
-   "source": [
-    "### Retrofitting using combination - selected few"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "naked-artist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Embedding</th>\n",
-       "      <th>Basis</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Weightedness</th>\n",
-       "      <th>Iteration Num</th>\n",
-       "      <th>Old Acc</th>\n",
-       "      <th>New Acc</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>Pairs Covered</th>\n",
-       "      <th>Old I Precision</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Old U F1-Score</th>\n",
-       "      <th>New I Precision</th>\n",
-       "      <th>New I Recall</th>\n",
-       "      <th>New I F1-Score</th>\n",
-       "      <th>New U Precision</th>\n",
-       "      <th>New U Recall</th>\n",
-       "      <th>New U F1-Score</th>\n",
-       "      <th>Avg F1-Score</th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>abstract</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63.953488</td>\n",
-       "      <td>64.244186</td>\n",
-       "      <td>0.290698</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.089286</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.038835</td>\n",
-       "      <td>0.073394</td>\n",
-       "      <td>0.459021</td>\n",
-       "      <td>2.365892</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>abstract_first_sent</td>\n",
-       "      <td>19k_class_siblings</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>65.697674</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.155172</td>\n",
-       "      <td>0.520000</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.577778</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.067961</td>\n",
-       "      <td>0.123894</td>\n",
-       "      <td>0.485447</td>\n",
-       "      <td>10.267434</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex</td>\n",
-       "      <td>19k_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>68.023256</td>\n",
-       "      <td>67.732558</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.463158</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.727273</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.398058</td>\n",
-       "      <td>0.443243</td>\n",
-       "      <td>0.652007</td>\n",
-       "      <td>15.403201</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>text_2_props</td>\n",
-       "      <td>19k_2_class</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.686047</td>\n",
-       "      <td>56.104651</td>\n",
-       "      <td>-0.581395</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.292683</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.065574</td>\n",
-       "      <td>0.288889</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.210526</td>\n",
-       "      <td>0.038835</td>\n",
-       "      <td>0.065574</td>\n",
-       "      <td>0.389309</td>\n",
-       "      <td>45.090151</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>text_7_props</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57.267442</td>\n",
-       "      <td>56.976744</td>\n",
-       "      <td>-0.290698</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.325000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.065574</td>\n",
-       "      <td>0.340909</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.029126</td>\n",
-       "      <td>0.049587</td>\n",
-       "      <td>0.403457</td>\n",
-       "      <td>7.334071</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>transe</td>\n",
-       "      <td>19k_class_childPar</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>65.406977</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>344</td>\n",
-       "      <td>0.652174</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.231884</td>\n",
-       "      <td>0.640000</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.711111</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.145631</td>\n",
-       "      <td>0.220588</td>\n",
-       "      <td>0.564948</td>\n",
-       "      <td>16.995019</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Embedding               Basis  Weight  Weightedness  \\\n",
-       "0             abstract  19k_class_childPar       2          True   \n",
-       "1  abstract_first_sent  19k_class_siblings       2          True   \n",
-       "2              complex        19k_childPar       1          True   \n",
-       "3         text_2_props         19k_2_class       2          True   \n",
-       "4         text_7_props  19k_class_childPar       2          True   \n",
-       "5               transe  19k_class_childPar       1          True   \n",
-       "\n",
-       "   Iteration Num    Old Acc    New Acc  Increase  Pairs Covered  \\\n",
-       "0              1  63.953488  64.244186  0.290698            344   \n",
-       "1              1  65.697674  65.697674  0.000000            344   \n",
-       "2              4  68.023256  67.732558 -0.290698            344   \n",
-       "3              1  56.686047  56.104651 -0.581395            344   \n",
-       "4              1  57.267442  56.976744 -0.290698            344   \n",
-       "5              4  65.406977  65.406977  0.000000            344   \n",
-       "\n",
-       "   Old I Precision  ...  Old U F1-Score  New I Precision  New I Recall  \\\n",
-       "0         0.461538  ...        0.089286         0.468750          0.75   \n",
-       "1         0.500000  ...        0.155172         0.520000          0.65   \n",
-       "2         0.923077  ...        0.463158         0.923077          0.60   \n",
-       "3         0.292683  ...        0.065574         0.288889          0.65   \n",
-       "4         0.325000  ...        0.065574         0.340909          0.75   \n",
-       "5         0.652174  ...        0.231884         0.640000          0.80   \n",
-       "\n",
-       "   New I F1-Score  New U Precision  New U Recall  New U F1-Score  \\\n",
-       "0        0.576923         0.666667      0.038835        0.073394   \n",
-       "1        0.577778         0.700000      0.067961        0.123894   \n",
-       "2        0.727273         0.500000      0.398058        0.443243   \n",
-       "3        0.400000         0.210526      0.038835        0.065574   \n",
-       "4        0.468750         0.166667      0.029126        0.049587   \n",
-       "5        0.711111         0.454545      0.145631        0.220588   \n",
-       "\n",
-       "   Avg F1-Score       Time  Rank  \n",
-       "0      0.459021   2.365892     0  \n",
-       "1      0.485447  10.267434     0  \n",
-       "2      0.652007  15.403201     0  \n",
-       "3      0.389309  45.090151     0  \n",
-       "4      0.403457   7.334071     0  \n",
-       "5      0.564948  16.995019     0  \n",
-       "\n",
-       "[6 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resultsDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "choice-brazilian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['abstract_19k_class_childPar_2_weighted', 'abstract_first_sent_19k_class_siblings_2_weighted', 'complex_19k_childPar_1_weighted', 'text_2_props_19k_2_class_2_weighted', 'text_7_props_19k_class_childPar_2_weighted', 'transe_19k_class_childPar_1_weighted'])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "responsesDictMaster.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "naughty-soccer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 13,   0,   7],\n",
-       "       [  0,   7,  96],\n",
-       "       [ 12,   3, 206]])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "responsesDictMaster['abstract_first_sent_19k_class_siblings_2_weighted']['cm_new']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "common-certificate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'BERT abstract_first_sent confusion matrix')"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.close()\n",
-    "sns.heatmap(responsesDictMaster['abstract_first_sent_19k_class_siblings_2_weighted']['cm_new'], xticklabels=['I', 'U', 'M'], yticklabels=['I', 'U', 'M'], annot=True)\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('Actual')\n",
-    "plt.title('BERT abstract_first_sent confusion matrix')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "bearing-retail",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'BERT text_7_props confusion matrix')"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.close()\n",
-    "sns.heatmap(responsesDictMaster['text_7_props_19k_class_childPar_2_weighted']['cm_new'], xticklabels=['I', 'U', 'M'], yticklabels=['I', 'U', 'M'], annot=True)\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('Actual')\n",
-    "plt.title('BERT text_7_props confusion matrix')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "approximate-showcase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'BERT complex confusion matrix')"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.close()\n",
-    "sns.heatmap(responsesDictMaster['complex_19k_childPar_1_weighted']['cm_new'], xticklabels=['I', 'U', 'M'], yticklabels=['I', 'U', 'M'], annot=True)\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('Actual')\n",
-    "plt.title('BERT complex confusion matrix')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "forced-treatment",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "stable-shark",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9331395348837209"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(responsesDictMaster['abstract_19k_class_childPar_2_weighted']['newOP'], responsesDictMaster['abstract_first_sent_19k_class_siblings_2_weighted']['newOP'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "appointed-factor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7877906976744186"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(responsesDictMaster['complex_19k_childPar_1_weighted']['newOP'], responsesDictMaster['transe_19k_class_childPar_1_weighted']['newOP'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "affiliated-wholesale",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7005813953488372"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(responsesDictMaster['complex_19k_childPar_1_weighted']['newOP'], responsesDictMaster['abstract_first_sent_19k_class_siblings_2_weighted']['newOP'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "hawaiian-worker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>abstract_19k_class_childPar_2_weighted</th>\n",
-       "      <th>abstract_first_sent_19k_class_siblings_2_weighted</th>\n",
-       "      <th>complex_19k_childPar_1_weighted</th>\n",
-       "      <th>text_2_props_19k_2_class_2_weighted</th>\n",
-       "      <th>text_7_props_19k_class_childPar_2_weighted</th>\n",
-       "      <th>transe_19k_class_childPar_1_weighted</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>abstract_19k_class_childPar_2_weighted</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.933140</td>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.857558</td>\n",
-       "      <td>0.880814</td>\n",
-       "      <td>0.811047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>abstract_first_sent_19k_class_siblings_2_weighted</th>\n",
-       "      <td>0.933140</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.700581</td>\n",
-       "      <td>0.834302</td>\n",
-       "      <td>0.845930</td>\n",
-       "      <td>0.816860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex_19k_childPar_1_weighted</th>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.700581</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.619186</td>\n",
-       "      <td>0.630814</td>\n",
-       "      <td>0.787791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text_2_props_19k_2_class_2_weighted</th>\n",
-       "      <td>0.857558</td>\n",
-       "      <td>0.834302</td>\n",
-       "      <td>0.619186</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.959302</td>\n",
-       "      <td>0.735465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text_7_props_19k_class_childPar_2_weighted</th>\n",
-       "      <td>0.880814</td>\n",
-       "      <td>0.845930</td>\n",
-       "      <td>0.630814</td>\n",
-       "      <td>0.959302</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.747093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe_19k_class_childPar_1_weighted</th>\n",
-       "      <td>0.811047</td>\n",
-       "      <td>0.816860</td>\n",
-       "      <td>0.787791</td>\n",
-       "      <td>0.735465</td>\n",
-       "      <td>0.747093</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   abstract_19k_class_childPar_2_weighted  \\\n",
-       "abstract_19k_class_childPar_2_weighted                                           1.000000   \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                                0.933140   \n",
-       "complex_19k_childPar_1_weighted                                                  0.697674   \n",
-       "text_2_props_19k_2_class_2_weighted                                              0.857558   \n",
-       "text_7_props_19k_class_childPar_2_weighted                                       0.880814   \n",
-       "transe_19k_class_childPar_1_weighted                                             0.811047   \n",
-       "\n",
-       "                                                   abstract_first_sent_19k_class_siblings_2_weighted  \\\n",
-       "abstract_19k_class_childPar_2_weighted                                                      0.933140   \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                                           1.000000   \n",
-       "complex_19k_childPar_1_weighted                                                             0.700581   \n",
-       "text_2_props_19k_2_class_2_weighted                                                         0.834302   \n",
-       "text_7_props_19k_class_childPar_2_weighted                                                  0.845930   \n",
-       "transe_19k_class_childPar_1_weighted                                                        0.816860   \n",
-       "\n",
-       "                                                   complex_19k_childPar_1_weighted  \\\n",
-       "abstract_19k_class_childPar_2_weighted                                    0.697674   \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                         0.700581   \n",
-       "complex_19k_childPar_1_weighted                                           1.000000   \n",
-       "text_2_props_19k_2_class_2_weighted                                       0.619186   \n",
-       "text_7_props_19k_class_childPar_2_weighted                                0.630814   \n",
-       "transe_19k_class_childPar_1_weighted                                      0.787791   \n",
-       "\n",
-       "                                                   text_2_props_19k_2_class_2_weighted  \\\n",
-       "abstract_19k_class_childPar_2_weighted                                        0.857558   \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                             0.834302   \n",
-       "complex_19k_childPar_1_weighted                                               0.619186   \n",
-       "text_2_props_19k_2_class_2_weighted                                           1.000000   \n",
-       "text_7_props_19k_class_childPar_2_weighted                                    0.959302   \n",
-       "transe_19k_class_childPar_1_weighted                                          0.735465   \n",
-       "\n",
-       "                                                   text_7_props_19k_class_childPar_2_weighted  \\\n",
-       "abstract_19k_class_childPar_2_weighted                                               0.880814   \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                                    0.845930   \n",
-       "complex_19k_childPar_1_weighted                                                      0.630814   \n",
-       "text_2_props_19k_2_class_2_weighted                                                  0.959302   \n",
-       "text_7_props_19k_class_childPar_2_weighted                                           1.000000   \n",
-       "transe_19k_class_childPar_1_weighted                                                 0.747093   \n",
-       "\n",
-       "                                                   transe_19k_class_childPar_1_weighted  \n",
-       "abstract_19k_class_childPar_2_weighted                                         0.811047  \n",
-       "abstract_first_sent_19k_class_siblings_2_weighted                              0.816860  \n",
-       "complex_19k_childPar_1_weighted                                                0.787791  \n",
-       "text_2_props_19k_2_class_2_weighted                                            0.735465  \n",
-       "text_7_props_19k_class_childPar_2_weighted                                     0.747093  \n",
-       "transe_19k_class_childPar_1_weighted                                           1.000000  "
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corrAccMatrix = []\n",
-    "for x in responsesDictMaster.keys():\n",
-    "    temp = []\n",
-    "    for y in responsesDictMaster.keys():\n",
-    "        temp.append(accuracy_score(responsesDictMaster[x]['newOP'], responsesDictMaster[y]['newOP']))\n",
-    "    corrAccMatrix.append(temp)\n",
-    "pd.DataFrame(corrAccMatrix, index=responsesDictMaster.keys(), columns=responsesDictMaster.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "corporate-quarter",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dirty-magic",
-   "metadata": {},
-   "source": [
-    "### Retrofitting using concatenation of embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "twenty-chinese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "def generateConcatenatedEmbeddingDict(embedDictMaster, key_comb, embedding_lengths):\n",
-    "    embedDict = defaultdict(list)\n",
-    "    masterKeySet = set()\n",
-    "    for key in key_comb:\n",
-    "        for qnode in embedDictMaster[key]:\n",
-    "            masterKeySet.add(qnode)\n",
-    "    for qnode in masterKeySet:\n",
-    "        for key in key_comb:\n",
-    "            if qnode in embedDictMaster[key]:\n",
-    "                embedDict[qnode] = embedDict[qnode] + (embedDictMaster[key][qnode].tolist())\n",
-    "            else:\n",
-    "                embedDict[qnode] = embedDict[qnode] + [0]*embedding_lengths[key]\n",
-    "        embedDict[qnode] = np.array(embedDict[qnode])\n",
-    "    return dict(embedDict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "cooperative-reference",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1min 16s, sys: 5.4 s, total: 1min 21s\n",
-      "Wall time: 1min 21s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "embedDictMaster['text_7-text_2-comp-tran-abs-abs_first'] = generateConcatenatedEmbeddingDict(embedDictMaster, \n",
-    "                ['text_7_props', 'text_2_props', 'complex', 'transe', 'abstract', 'abstract_first_sent'], \n",
-    "                embedding_lengths)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "direct-discrimination",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55c060d3c04441008eb1631416aa2a38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "880c54ab554f47c3bd423f4cd96a9652",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "782a811c6d614335a86eaadac0ce6766",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "07cfda2280d3482d8923fd4093acfe6b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "newEmbedDictMaster = {}\n",
-    "responsesDictMaster = {}\n",
-    "\n",
-    "results = []\n",
-    "NUM_ITERS = 2\n",
-    "for basis in tqdm(basisList):\n",
-    "    for weightedNess in [True]:\n",
-    "        for i in tqdm(range(2,len(embList)+1)):\n",
-    "            for embComb in tqdm(combinations(embList, i)):\n",
-    "                emb = '-'.join(embComb)\n",
-    "                start_time = time()\n",
-    "                embedDict = {}\n",
-    "                embedDict[1] = embedDict[2] = generateConcatenatedEmbeddingDict(embedDictMaster,embComb, embedding_lengths)\n",
-    "                \n",
-    "                for weightCase in [1,2]:\n",
-    "                    oldAcc = 0\n",
-    "#                     groupResults = []\n",
-    "                    for iterNum in range(1,NUM_ITERS+1):\n",
-    "                        if weightedNess:\n",
-    "                            caseName = emb + '_' + basis + '_' + str(weightCase) + '_weighted'\n",
-    "                        else:\n",
-    "                            caseName = emb + '_' + basis + '_' + str(weightCase) + '_unweighted'\n",
-    "                        newEmbedDict = retrofit(embedDict[weightCase], neighDictMaster[basis], weightCase, weightedNess)\n",
-    "            #             dists = determine_distances(embedDict, newEmbedDict)\n",
-    "                        responsesDict = fetchCorrelationResults(embedDict[weightCase], newEmbedDict)\n",
-    "            #                 print(responsesDict.keys())\n",
-    "                        if iterNum == 1:\n",
-    "                            oldAcc = responsesDict['old_acc']*100\n",
-    "                            groupResults.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                        -1, \\\n",
-    "                                        responsesDict['old_acc']*100, \\\n",
-    "                                        0, \\\n",
-    "                                        responsesDict['coveredPairs'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                         i, \\\n",
-    "                                         time() - start_time\n",
-    "                                        ])\n",
-    "                        results.append([emb, basis, weightCase, weightedNess, iterNum, \\\n",
-    "                                        -1, \\\n",
-    "                                        responsesDict['new_acc']*100, \\\n",
-    "                                        (responsesDict['new_acc'] - oldAcc)*100, \\\n",
-    "                                        responsesDict['coveredPairs'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_old']['U']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['I']['f1-score'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['precision'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['recall'], \\\n",
-    "                                         responsesDict['class_rep_new']['U']['f1-score'], \\\n",
-    "                                         i, \\\n",
-    "                                         time() - start_time\n",
-    "                                        ])\n",
-    "                        embedDict[weightCase] = newEmbedDict\n",
-    "\n",
-    "                        newEmbedDictMaster[caseName] = newEmbedDict\n",
-    "                        responsesDictMaster[caseName] = responsesDict\n",
-    "                        if responsesDict['new_acc']*100 < oldAcc:\n",
-    "                            break\n",
-    "#                 results.append(gr)\n",
-    "#                     highestOne = True\n",
-    "#                     for gR, rank in zip(groupResults, np.argsort([-p[6] for p in groupResults])):\n",
-    "#                         results.append(gR+[rank])\n",
-    "#                     if iterNum == NUM_ITERS and highestOne:\n",
-    "#                         caseName = gR[0] + '_' + gR[1] + '_' + str(gR[2]) + '_weighted'\n",
-    "#                         newEmbedDictMaster[caseName] = serializeEmbeddingDict(newEmbedDictMaster[caseName])\n",
-    "#                         highestOne = False\n",
-    "#                         json.dump(newEmbedDictMaster[caseName],open('../data/Master_P279_dataset/embeddings/new_embedding_dict_'+caseName+'.json','w'))\n",
-    "#                         newEmbedDictMaster[caseName] = deserializeEmbeddingDict(newEmbedDictMaster[caseName])\n",
-    "resultsDF = pd.DataFrame(results, columns=['Embedding', 'Basis', 'Weight', 'Weightedness', 'Iteration Num', 'Old Acc', 'New Acc', 'Increase', 'Pairs Covered', \\\n",
-    "                                           'Old I Precision', 'Old I Recall', 'Old I F1-Score', \\\n",
-    "                                           'Old U Precision', 'Old U Recall', 'Old U F1-Score', \\\n",
-    "                                           'New I Precision', 'New I Recall', 'New I F1-Score', \\\n",
-    "                                           'New U Precision', 'New U Recall', 'New U F1-Score', \\\n",
-    "                                           'No. of embeddings', \\\n",
-    "                                           'Time'])\n",
-    "resultsDF.to_csv('../data/retrofitting/retro_concat_'+ today_date +'.csv', index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "elementary-console",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "korean-albania",
-   "metadata": {},
-   "source": [
-    "## SVM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "golden-melissa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4aca6f0bfc654b178bd71a5c7cfe22d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "svmCasesList = []\n",
-    "for basis in tqdm(basisList):\n",
-    "    for emb in embList:\n",
-    "        for weightedNess in [True]:\n",
-    "            for iterNum in range(0,NUM_ITERS+1):\n",
-    "                for weightCase in [1,2]:\n",
-    "                    for svm_input in ['emb','score']:\n",
-    "                        temp_dict = {'basis': basis, 'emb': emb, 'weightedNess': weightedNess, \n",
-    "                                    'iterNum': iterNum, 'weightCase': weightCase, 'svm_input': svm_input}\n",
-    "                        svmCasesList.append(temp_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "renewable-selling",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "broadband-column",
-   "metadata": {},
-   "source": [
-    "### SVM Classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "swedish-bride",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'basis': '19k_2',\n",
-       " 'emb': 'text_7_props',\n",
-       " 'weightedNess': True,\n",
-       " 'iterNum': 0,\n",
-       " 'weightCase': 1,\n",
-       " 'svm_input': 'emb'}"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmCasesList[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "orange-planet",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "trainAndFindAccuracy(wordSim353AnnotDF_New, svmCasesList[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "portuguese-darwin",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "accompanied-tuner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(results, columns=['caseName','basis','emb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy']).to_csv('../data/retrofitting/retro_SVM_test_'+ today_date +'.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "immediate-writer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyCombs(wordSim353AnnotDF_New_Merged_DF, case):\n",
-    "    X = []\n",
-    "    \n",
-    "    if case['svm_input'] == 'emb':\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += embedDictMaster[emb][row['word1_kg_id']].tolist() + embedDictMaster[emb][row['word2_kg_id']].tolist()\n",
-    "                else:\n",
-    "                    tempX += newEmbedDictMaster[caseName][row['word1_kg_id']].tolist() + newEmbedDictMaster[caseName][row['word2_kg_id']].tolist()\n",
-    "            X.append(tempX)\n",
-    "    else:\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += [(abs(cosine_similarity(embedDictMaster[emb][row['word1_kg_id']].reshape(1,-1), embedDictMaster[emb][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "                else:\n",
-    "                    tempX += [(abs(cosine_similarity(newEmbedDictMaster[caseName][row['word1_kg_id']].reshape(1,-1), newEmbedDictMaster[caseName][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "            X.append(tempX)\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    X = pd.DataFrame(X)\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return caseName, *list(case.values()), acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "insured-escape",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'basisList' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-23-f8d710c8d206>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msvmCombCasesList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbasis\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mweightedNess\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0miterNum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNUM_ITERS\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mweightCase\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'basisList' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "svmCombCasesList = []\n",
-    "for basis in tqdm(basisList):\n",
-    "        for weightedNess in [True]:\n",
-    "            for iterNum in range(0,NUM_ITERS+1):\n",
-    "                for weightCase in [1,2]:\n",
-    "                    for svm_input in ['score']:\n",
-    "                        for i in range(1,len(embList)+1):\n",
-    "                            for embComb in combinations(embList, i):\n",
-    "                                temp_dict = {'basis': basis, 'emb': embComb, 'weightedNess': weightedNess, \n",
-    "                                            'iterNum': iterNum, 'weightCase': weightCase, 'svm_input': svm_input}\n",
-    "                                svmCombCasesList.append(temp_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "ahead-letters",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3024"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(svmCombCasesList)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "chicken-computer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3024 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 18min 7s, sys: 24.6 s, total: 18min 32s\n",
-      "Wall time: 18min 27s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindAccuracyCombs)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCombCasesList))\n",
-    "resDF = pd.DataFrame(results, columns=['caseName','basis','emb_comb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy'])\n",
-    "resDF['emb_comb'] = resDF['emb_comb'].apply(lambda p: \" & \".join(p))\n",
-    "resDF.to_csv('../data/retrofitting/retro_SVM_comb_test_'+ today_date +'.csv', index=False)\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "featured-blast",
-   "metadata": {},
-   "source": [
-    "### SVM Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "photographic-mileage",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>6.9375</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>5.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>4.9375</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>s</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id category  \n",
-       "0        Q454        U  \n",
-       "1   Q13648784        U  \n",
-       "2     Q178022        U  \n",
-       "3   Q21004260        M  \n",
-       "4      Q49112        M  "
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "pediatric-intelligence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f5e0e7ad7fb4e5589d10aaaf55783a8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/432 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindRegressionAccuracy)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCasesList))\n",
-    "pd.DataFrame(results, columns=['caseName','basis','emb','weightedNess', 'iteration', 'weightCase', 'technique','Acc']).to_csv('../data/retrofitting/retro_SVM_Reg_test_'+ today_date +'.csv', index=False)\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "painted-objective",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindRegressionAccuracyCombs(wordSim353AnnotDF_New_Merged_DF, case):\n",
-    "    X = []\n",
-    "    \n",
-    "    if case['svm_input'] == 'emb':\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += embedDictMaster[emb][row['word1_kg_id']].tolist() + embedDictMaster[emb][row['word2_kg_id']].tolist()\n",
-    "                else:\n",
-    "                    tempX += newEmbedDictMaster[caseName][row['word1_kg_id']].tolist() + newEmbedDictMaster[caseName][row['word2_kg_id']].tolist()\n",
-    "            X.append(tempX)\n",
-    "    else:\n",
-    "        for _, row in wordSim353AnnotDF_New_Merged_DF.iterrows():\n",
-    "            tempX = []\n",
-    "            for emb in case['emb']:\n",
-    "                caseName = emb + '_' + case['basis'] + '_' + str(case['weightCase']) + ('_weighted' if case['weightedNess'] else '_unweighted')\n",
-    "                # Guard check conditions\n",
-    "                if case['iterNum'] != 0 and caseName not in newEmbedDictMaster:\n",
-    "                    return caseName, case, None\n",
-    "                if case['iterNum'] == 0:\n",
-    "                    tempX += [(abs(cosine_similarity(embedDictMaster[emb][row['word1_kg_id']].reshape(1,-1), embedDictMaster[emb][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "                else:\n",
-    "                    tempX += [(abs(cosine_similarity(newEmbedDictMaster[caseName][row['word1_kg_id']].reshape(1,-1), newEmbedDictMaster[caseName][row['word2_kg_id']].reshape(1,-1))[0][0]))]\n",
-    "            X.append(tempX)\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    X = pd.DataFrame(X)\n",
-    "    Y = (wordSim353AnnotDF_New_Merged_DF['Avg'] - 1) / 3\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(np_label_samples(pred * 3 + 1), np_label_samples(Y_test1 * 3 + 1))\n",
-    "#         acc += mean_squared_error(pred * 3 + 1, Y_test1 * 3 + 1)\n",
-    "\n",
-    "    return caseName, *list(case.values()), acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "circular-marine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0704145482e94140a701edcd9d8c0a6f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3024 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 17min 14s, sys: 18.4 s, total: 17min 32s\n",
-      "Wall time: 17min 26s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')\n",
-    "    \n",
-    "results = Parallel(n_jobs=1)(delayed(trainAndFindRegressionAccuracyCombs)(wordSim353AnnotDF_New, caseDict,) for caseDict in tqdm(svmCombCasesList))\n",
-    "resDF = pd.DataFrame(results, columns=['caseName','basis','emb_comb','weightedNess', 'iteration', 'weightCase', 'technique','accuracy'])\n",
-    "resDF['emb_comb'] = resDF['emb_comb'].apply(lambda p: \" & \".join(p))\n",
-    "resDF.to_csv('../data/retrofitting/retro_SVM_Reg_comb_test_'+ today_date +'.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-department",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mathematical-graphics",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "moderate-fashion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "centered-concentration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldEmbedDictKey = 'transe'\n",
-    "newEmbedDictKey = 'transe_19k_childPar_1_weighted'\n",
-    "wordSim353AnnotDF_New['textOld'] = wordSim353AnnotDF_New.apply(lambda p: 4 - 3 * abs(cosine_similarity(np.array(embedDictMaster[oldEmbedDictKey][p['word1_kg_id']]).reshape(1,-1), np.array(embedDictMaster[oldEmbedDictKey][p['word2_kg_id']]).reshape(1,-1))[0][0]) if p['word1_kg_id'] in embedDictMaster[oldEmbedDictKey] and p['word2_kg_id'] in embedDictMaster[oldEmbedDictKey] else -1, axis=1)\n",
-    "wordSim353AnnotDF_New['textNew'] = wordSim353AnnotDF_New.apply(lambda p: 4 - 3 * abs(cosine_similarity(np.array(newEmbedDictMaster[newEmbedDictKey][p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDictMaster[newEmbedDictKey][p['word2_kg_id']]).reshape(1,-1))[0][0]) if p['word1_kg_id'] in embedDictMaster[oldEmbedDictKey] and p['word2_kg_id'] in embedDictMaster[oldEmbedDictKey] else -1, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "suspended-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New['old_diff'] = abs(wordSim353AnnotDF_New['textOld'] - wordSim353AnnotDF_New['Avg'])\n",
-    "wordSim353AnnotDF_New['new_diff'] = abs(wordSim353AnnotDF_New['textNew'] - wordSim353AnnotDF_New['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "another-mississippi",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "metallic-smith",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.506079\n",
-       "std        0.388162\n",
-       "min        0.000000\n",
-       "25%        0.201136\n",
-       "50%        0.430653\n",
-       "75%        0.744341\n",
-       "max        1.969069\n",
-       "Name: old_diff, dtype: float64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.sort_values(by=['old_diff'],ascending=False)['old_diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "designed-vacuum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.587435\n",
-       "std        0.440362\n",
-       "min        0.000000\n",
-       "25%        0.237281\n",
-       "50%        0.494043\n",
-       "75%        0.880977\n",
-       "max        1.900974\n",
-       "Name: new_diff, dtype: float64"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.sort_values(by=['new_diff'],ascending=False)['new_diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "facial-second",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.506079:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 0.587435:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New['Old_Diff_Cat'] = wordSim353AnnotDF_New['old_diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New['New_Diff_Cat'] = wordSim353AnnotDF_New['new_diff'].apply(determineFarNessOfNew)\n",
-    "wordSim353AnnotDF_New['correct_category'] = wordSim353AnnotDF_New.Avg.apply(label_samples)\n",
-    "wordSim353AnnotDF_New['old_predicted_category'] = wordSim353AnnotDF_New.textOld.apply(label_samples)\n",
-    "wordSim353AnnotDF_New['new_predicted_category'] = wordSim353AnnotDF_New.textNew.apply(label_samples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "lightweight-average",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New[['Word 1', 'Word 2', 'Avg', 'textOld', 'textNew', 'old_diff', 'new_diff', 'Old_Diff_Cat', 'New_Diff_Cat', 'correct_category', 'old_predicted_category', 'new_predicted_category']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "american-egypt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>textOld</th>\n",
-       "      <th>textNew</th>\n",
-       "      <th>old_diff</th>\n",
-       "      <th>new_diff</th>\n",
-       "      <th>Old_Diff_Cat</th>\n",
-       "      <th>New_Diff_Cat</th>\n",
-       "      <th>correct_category</th>\n",
-       "      <th>old_predicted_category</th>\n",
-       "      <th>new_predicted_category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.909414</td>\n",
-       "      <td>3.837159</td>\n",
-       "      <td>0.309414</td>\n",
-       "      <td>0.237159</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.646852</td>\n",
-       "      <td>3.593611</td>\n",
-       "      <td>0.046852</td>\n",
-       "      <td>0.006389</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.470644</td>\n",
-       "      <td>3.470644</td>\n",
-       "      <td>0.129356</td>\n",
-       "      <td>0.129356</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>3.674395</td>\n",
-       "      <td>3.625026</td>\n",
-       "      <td>0.674395</td>\n",
-       "      <td>0.625026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.514591</td>\n",
-       "      <td>2.514591</td>\n",
-       "      <td>0.314591</td>\n",
-       "      <td>0.314591</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.877362</td>\n",
-       "      <td>2.476802</td>\n",
-       "      <td>0.122638</td>\n",
-       "      <td>0.523198</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.544676</td>\n",
-       "      <td>3.078550</td>\n",
-       "      <td>0.255324</td>\n",
-       "      <td>0.721450</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.443100</td>\n",
-       "      <td>2.075806</td>\n",
-       "      <td>0.556900</td>\n",
-       "      <td>0.924194</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>3.666691</td>\n",
-       "      <td>3.692986</td>\n",
-       "      <td>1.866691</td>\n",
-       "      <td>1.892986</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>3.202513</td>\n",
-       "      <td>2.677294</td>\n",
-       "      <td>0.547487</td>\n",
-       "      <td>1.072706</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg   textOld   textNew  old_diff  new_diff  \\\n",
-       "0     Arafat          peace  3.60  3.909414  3.837159  0.309414  0.237159   \n",
-       "1     Arafat         terror  3.60  3.646852  3.593611  0.046852  0.006389   \n",
-       "2        FBI    fingerprint  3.60  3.470644  3.470644  0.129356  0.129356   \n",
-       "3        FBI  investigation  3.00  3.674395  3.625026  0.674395  0.625026   \n",
-       "4    Harvard           Yale  2.20  2.514591  2.514591  0.314591  0.314591   \n",
-       "..       ...            ...   ...       ...       ...       ...       ...   \n",
-       "339      war         troops  3.00  2.877362  2.476802  0.122638  0.523198   \n",
-       "340   weapon         secret  3.80  3.544676  3.078550  0.255324  0.721450   \n",
-       "341  weather       forecast  3.00  2.443100  2.075806  0.556900  0.924194   \n",
-       "342     wood         forest  1.80  3.666691  3.692986  1.866691  1.892986   \n",
-       "343     word     similarity  3.75  3.202513  2.677294  0.547487  1.072706   \n",
-       "\n",
-       "     Old_Diff_Cat  New_Diff_Cat correct_category old_predicted_category  \\\n",
-       "0               0             0                U                      U   \n",
-       "1               0             0                U                      U   \n",
-       "2               0             0                U                      M   \n",
-       "3               1             1                M                      U   \n",
-       "4               0             0                M                      M   \n",
-       "..            ...           ...              ...                    ...   \n",
-       "339             0             0                M                      M   \n",
-       "340             0             1                U                      U   \n",
-       "341             1             1                M                      M   \n",
-       "342             1             1                M                      U   \n",
-       "343             1             1                U                      M   \n",
-       "\n",
-       "    new_predicted_category  \n",
-       "0                        U  \n",
-       "1                        U  \n",
-       "2                        M  \n",
-       "3                        U  \n",
-       "4                        M  \n",
-       "..                     ...  \n",
-       "339                      M  \n",
-       "340                      M  \n",
-       "341                      M  \n",
-       "342                      U  \n",
-       "343                      M  \n",
-       "\n",
-       "[344 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "automotive-trouble",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "arabic-binding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['old_predicted_category'] != wordSim353AnnotDF_New_Merged_DF['correct_category']].to_csv('../data/transEmb/badBefore_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "acoustic-holocaust",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[wordSim353AnnotDF_New_Merged_DF['new_predicted_category'] != wordSim353AnnotDF_New_Merged_DF['correct_category']].to_csv('../data/transEmb/badAfter_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "accompanied-spyware",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.to_csv('../data/transEmb/entireSet.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "reverse-cincinnati",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "breathing-footage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "chief-chance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "shaped-letter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "adolescent-monte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['Old_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['New_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad_'+ today_date +'.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "swiss-bridal",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "current-mirror",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "placed-identity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "stopped-daisy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'] = abs(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim'] - wordSim353AnnotDF_New_Merged_DF['Avg'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deadly-forwarding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "southeast-canal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    344.000000\n",
-       "mean       0.567297\n",
-       "std        0.435379\n",
-       "min        0.000000\n",
-       "25%        0.250130\n",
-       "50%        0.508725\n",
-       "75%        0.835197\n",
-       "max        5.000000\n",
-       "Name: concat_19k_v1_old_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_old_cosSim_Diff'],ascending=False)['concat_19k_v1_old_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "adequate-terminology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    3.440000e+02\n",
-       "mean     5.938081e-01\n",
-       "std      4.503200e-01\n",
-       "min      4.440892e-16\n",
-       "25%      2.665817e-01\n",
-       "50%      5.349785e-01\n",
-       "75%      8.528840e-01\n",
-       "max      5.000000e+00\n",
-       "Name: concat_19k_v1_2_new_cosSim_Diff, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.sort_values(by=['concat_19k_v1_2_new_cosSim_Diff'],ascending=False)['concat_19k_v1_2_new_cosSim_Diff'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "wired-shoot",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def determineFarNessOfOld(val1):\n",
-    "    if 0.000000 <= val1 < 0.508725:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "def determineFarNessOfNew(val1):\n",
-    "    if 0 <= val1 < 5.349785e-01:\n",
-    "        return 0\n",
-    "    else:\n",
-    "        return 1\n",
-    "\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "operating-senegal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'concat_19k_v1_old_cosSim', 'concat_19k_v1_2_new_cosSim', 'concat_19k_v1_old_cosSim_Diff', 'concat_19k_v1_2_new_cosSim_Diff', 'concat_19k_v1_old_cosSim_Diff_Cat', 'concat_19k_v1_2_new_cosSim_Diff_Cat']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quantitative-money",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff</th>\n",
-       "      <th>concat_19k_v1_old_cosSim_Diff_Cat</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim_Diff_Cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.251198</td>\n",
-       "      <td>3.193271</td>\n",
-       "      <td>0.348802</td>\n",
-       "      <td>0.406729</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>3.231409</td>\n",
-       "      <td>3.157229</td>\n",
-       "      <td>0.368591</td>\n",
-       "      <td>0.442771</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.60</td>\n",
-       "      <td>2.948487</td>\n",
-       "      <td>2.853048</td>\n",
-       "      <td>0.651513</td>\n",
-       "      <td>0.746952</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.930365</td>\n",
-       "      <td>2.895604</td>\n",
-       "      <td>0.069635</td>\n",
-       "      <td>0.104396</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>1.991054</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0.208946</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>war</td>\n",
-       "      <td>troops</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.913931</td>\n",
-       "      <td>2.787213</td>\n",
-       "      <td>0.086069</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>weapon</td>\n",
-       "      <td>secret</td>\n",
-       "      <td>3.80</td>\n",
-       "      <td>3.152513</td>\n",
-       "      <td>3.119065</td>\n",
-       "      <td>0.647487</td>\n",
-       "      <td>0.680935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>weather</td>\n",
-       "      <td>forecast</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.492765</td>\n",
-       "      <td>2.462389</td>\n",
-       "      <td>0.507235</td>\n",
-       "      <td>0.537611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>wood</td>\n",
-       "      <td>forest</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>2.612681</td>\n",
-       "      <td>2.606941</td>\n",
-       "      <td>0.812681</td>\n",
-       "      <td>0.806941</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>word</td>\n",
-       "      <td>similarity</td>\n",
-       "      <td>3.75</td>\n",
-       "      <td>2.678276</td>\n",
-       "      <td>2.652829</td>\n",
-       "      <td>1.071724</td>\n",
-       "      <td>1.097171</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>344 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Word 1         Word 2   Avg  concat_19k_v1_old_cosSim  \\\n",
-       "0     Arafat          peace  3.60                  3.251198   \n",
-       "1     Arafat         terror  3.60                  3.231409   \n",
-       "2        FBI    fingerprint  3.60                  2.948487   \n",
-       "3        FBI  investigation  3.00                  2.930365   \n",
-       "4    Harvard           Yale  2.20                  1.991054   \n",
-       "..       ...            ...   ...                       ...   \n",
-       "339      war         troops  3.00                  2.913931   \n",
-       "340   weapon         secret  3.80                  3.152513   \n",
-       "341  weather       forecast  3.00                  2.492765   \n",
-       "342     wood         forest  1.80                  2.612681   \n",
-       "343     word     similarity  3.75                  2.678276   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim  concat_19k_v1_old_cosSim_Diff  \\\n",
-       "0                      3.193271                       0.348802   \n",
-       "1                      3.157229                       0.368591   \n",
-       "2                      2.853048                       0.651513   \n",
-       "3                      2.895604                       0.069635   \n",
-       "4                      1.991054                       0.208946   \n",
-       "..                          ...                            ...   \n",
-       "339                    2.787213                       0.086069   \n",
-       "340                    3.119065                       0.647487   \n",
-       "341                    2.462389                       0.507235   \n",
-       "342                    2.606941                       0.812681   \n",
-       "343                    2.652829                       1.071724   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff  concat_19k_v1_old_cosSim_Diff_Cat  \\\n",
-       "0                           0.406729                                  0   \n",
-       "1                           0.442771                                  0   \n",
-       "2                           0.746952                                  1   \n",
-       "3                           0.104396                                  0   \n",
-       "4                           0.208946                                  0   \n",
-       "..                               ...                                ...   \n",
-       "339                         0.212787                                  0   \n",
-       "340                         0.680935                                  1   \n",
-       "341                         0.537611                                  0   \n",
-       "342                         0.806941                                  1   \n",
-       "343                         1.097171                                  1   \n",
-       "\n",
-       "     concat_19k_v1_2_new_cosSim_Diff_Cat  \n",
-       "0                                      0  \n",
-       "1                                      0  \n",
-       "2                                      1  \n",
-       "3                                      0  \n",
-       "4                                      0  \n",
-       "..                                   ...  \n",
-       "339                                    0  \n",
-       "340                                    1  \n",
-       "341                                    1  \n",
-       "342                                    1  \n",
-       "343                                    1  \n",
-       "\n",
-       "[344 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "attractive-annex",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "proof-registrar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "elder-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "magnetic-marina",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/concEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "satisfied-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['concat_19k_v1_2_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/concEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surprising-milan",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "worldwide-genius",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting datacompy\n",
-      "  Downloading datacompy-0.7.2-py3-none-any.whl (27 kB)\n",
-      "Requirement already satisfied: numpy>=1.11.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=0.25.0 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from datacompy) (1.2.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from pandas>=0.25.0->datacompy) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in /nas/home/kshenoy/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->datacompy) (1.15.0)\n",
-      "Installing collected packages: datacompy\n",
-      "Successfully installed datacompy-0.7.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datacompy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "id": "overhead-board",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "id": "developing-option",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "catholic-compensation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.013016090623183913, 0.01114268771757639, -0...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.01327496060589727, 0.010965634890657862, -0...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626159341165, 0.005047111630696947, -0...</td>\n",
-       "      <td>[0.010410829393995813, 0.014776997996557633, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF_old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "id": "expanded-uniform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>text_7props_19k_old_cosSim</th>\n",
-       "      <th>text_7props_19k_2_new_cosSim</th>\n",
-       "      <th>text_2props_19k_old_cosSim</th>\n",
-       "      <th>text_2props_19k_2_new_cosSim</th>\n",
-       "      <th>complex_19k_old_cosSim</th>\n",
-       "      <th>complex_19k_2_new_cosSim</th>\n",
-       "      <th>transe_19k_old_cosSim</th>\n",
-       "      <th>transe_19k_1_new_cosSim</th>\n",
-       "      <th>abstract_19k_old_cosSim</th>\n",
-       "      <th>abstract_19k_2_new_cosSim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>transe_probase_19k_old_cosSim</th>\n",
-       "      <th>transe_probase_19k_1_new_cosSim</th>\n",
-       "      <th>concat_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v1_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v1_2_new_cosSim</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "      <td>344.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.511033</td>\n",
-       "      <td>2.464876</td>\n",
-       "      <td>2.482254</td>\n",
-       "      <td>2.435161</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.684818</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.680002</td>\n",
-       "      <td>2.652866</td>\n",
-       "      <td>2.593916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.983302</td>\n",
-       "      <td>2.565886</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.826493</td>\n",
-       "      <td>2.875381</td>\n",
-       "      <td>2.865101</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.816685</td>\n",
-       "      <td>2.868066</td>\n",
-       "      <td>2.856603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.148821</td>\n",
-       "      <td>1.152911</td>\n",
-       "      <td>1.151060</td>\n",
-       "      <td>1.155427</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.539092</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.619239</td>\n",
-       "      <td>0.680525</td>\n",
-       "      <td>0.653512</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.645911</td>\n",
-       "      <td>0.596750</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.575585</td>\n",
-       "      <td>0.573902</td>\n",
-       "      <td>0.574410</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.106362</td>\n",
-       "      <td>1.097121</td>\n",
-       "      <td>1.099179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.005097</td>\n",
-       "      <td>1.963229</td>\n",
-       "      <td>1.993424</td>\n",
-       "      <td>1.949869</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.455431</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.311818</td>\n",
-       "      <td>2.180912</td>\n",
-       "      <td>2.137568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.588591</td>\n",
-       "      <td>2.183686</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.551033</td>\n",
-       "      <td>2.577938</td>\n",
-       "      <td>2.583811</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.350645</td>\n",
-       "      <td>2.384337</td>\n",
-       "      <td>2.377672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.328276</td>\n",
-       "      <td>2.273674</td>\n",
-       "      <td>2.274668</td>\n",
-       "      <td>2.232156</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.708569</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.722278</td>\n",
-       "      <td>2.616810</td>\n",
-       "      <td>2.591640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.039087</td>\n",
-       "      <td>2.608041</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.912098</td>\n",
-       "      <td>2.950578</td>\n",
-       "      <td>2.933984</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.657078</td>\n",
-       "      <td>2.716471</td>\n",
-       "      <td>2.704941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.636907</td>\n",
-       "      <td>2.576155</td>\n",
-       "      <td>2.580482</td>\n",
-       "      <td>2.527554</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.959259</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>3.090563</td>\n",
-       "      <td>3.208919</td>\n",
-       "      <td>3.060782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.359243</td>\n",
-       "      <td>2.935226</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.164426</td>\n",
-       "      <td>3.208735</td>\n",
-       "      <td>3.196270</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.899626</td>\n",
-       "      <td>2.992265</td>\n",
-       "      <td>2.977770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>4.340089</td>\n",
-       "      <td>4.327902</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       text_7props_19k_old_cosSim  text_7props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.511033                      2.464876   \n",
-       "std                      1.148821                      1.152911   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      2.005097                      1.963229   \n",
-       "50%                      2.328276                      2.273674   \n",
-       "75%                      2.636907                      2.576155   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       text_2props_19k_old_cosSim  text_2props_19k_2_new_cosSim  \\\n",
-       "count                  344.000000                    344.000000   \n",
-       "mean                     2.482254                      2.435161   \n",
-       "std                      1.151060                      1.155427   \n",
-       "min                      1.000000                      1.000000   \n",
-       "25%                      1.993424                      1.949869   \n",
-       "50%                      2.274668                      2.232156   \n",
-       "75%                      2.580482                      2.527554   \n",
-       "max                      7.000000                      7.000000   \n",
-       "\n",
-       "       complex_19k_old_cosSim  complex_19k_2_new_cosSim  \\\n",
-       "count              344.000000                344.000000   \n",
-       "mean                 2.983302                  2.684818   \n",
-       "std                  0.645911                  0.539092   \n",
-       "min                  1.000000                  1.000000   \n",
-       "25%                  2.588591                  2.455431   \n",
-       "50%                  3.039087                  2.708569   \n",
-       "75%                  3.359243                  2.959259   \n",
-       "max                  7.000000                  7.000000   \n",
-       "\n",
-       "       transe_19k_old_cosSim  transe_19k_1_new_cosSim  \\\n",
-       "count             344.000000               344.000000   \n",
-       "mean                2.983302                 2.680002   \n",
-       "std                 0.645911                 0.619239   \n",
-       "min                 1.000000                 1.000000   \n",
-       "25%                 2.588591                 2.311818   \n",
-       "50%                 3.039087                 2.722278   \n",
-       "75%                 3.359243                 3.090563   \n",
-       "max                 7.000000                 7.000000   \n",
-       "\n",
-       "       abstract_19k_old_cosSim  abstract_19k_2_new_cosSim  ...  \\\n",
-       "count               344.000000                 344.000000  ...   \n",
-       "mean                  2.652866                   2.593916  ...   \n",
-       "std                   0.680525                   0.653512  ...   \n",
-       "min                   1.000000                   1.000000  ...   \n",
-       "25%                   2.180912                   2.137568  ...   \n",
-       "50%                   2.616810                   2.591640  ...   \n",
-       "75%                   3.208919                   3.060782  ...   \n",
-       "max                   4.340089                   4.327902  ...   \n",
-       "\n",
-       "       transe_probase_19k_old_cosSim  transe_probase_19k_1_new_cosSim  \\\n",
-       "count                     344.000000                       344.000000   \n",
-       "mean                        2.983302                         2.565886   \n",
-       "std                         0.645911                         0.596750   \n",
-       "min                         1.000000                         1.000000   \n",
-       "25%                         2.588591                         2.183686   \n",
-       "50%                         3.039087                         2.608041   \n",
-       "75%                         3.359243                         2.935226   \n",
-       "max                         7.000000                         7.000000   \n",
-       "\n",
-       "       concat_19k_v1_old_cosSim  concat_19k_v1_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.875381                    2.826493   \n",
-       "std                    0.573902                    0.575585   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.577938                    2.551033   \n",
-       "50%                    2.950578                    2.912098   \n",
-       "75%                    3.208735                    3.164426   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v1_old_cosSim  concat_probase_19k_v1_2_new_cosSim  \\\n",
-       "count                        344.000000                          344.000000   \n",
-       "mean                           2.875381                            2.865101   \n",
-       "std                            0.573902                            0.574410   \n",
-       "min                            1.000000                            1.000000   \n",
-       "25%                            2.577938                            2.583811   \n",
-       "50%                            2.950578                            2.933984   \n",
-       "75%                            3.208735                            3.196270   \n",
-       "max                            7.000000                            7.000000   \n",
-       "\n",
-       "       concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "count                344.000000                  344.000000   \n",
-       "mean                   2.868066                    2.816685   \n",
-       "std                    1.097121                    1.106362   \n",
-       "min                    1.000000                    1.000000   \n",
-       "25%                    2.384337                    2.350645   \n",
-       "50%                    2.716471                    2.657078   \n",
-       "75%                    2.992265                    2.899626   \n",
-       "max                    7.000000                    7.000000   \n",
-       "\n",
-       "       concat_probase_19k_v2_old_cosSim  concat_probase_19k_v2_2_new_cosSim  \n",
-       "count                        344.000000                          344.000000  \n",
-       "mean                           2.868066                            2.856603  \n",
-       "std                            1.097121                            1.099179  \n",
-       "min                            1.000000                            1.000000  \n",
-       "25%                            2.384337                            2.377672  \n",
-       "50%                            2.716471                            2.704941  \n",
-       "75%                            2.992265                            2.977770  \n",
-       "max                            7.000000                            7.000000  \n",
-       "\n",
-       "[8 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF[list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "indian-embassy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datacompy\n",
-    "compare = datacompy.Compare(\n",
-    "    wordSim353AnnotDF_New_Merged_DF[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns.to_list()))],\n",
-    "    wordSim353AnnotDF_New_Merged_DF_old[['Word 1',\n",
-    " 'Word 2',\n",
-    " 'Avg',\n",
-    " 'word1_kg_id',\n",
-    " 'word2_kg_id',\n",
-    " 'category'] + list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF_old.columns.to_list()))],\n",
-    "    join_columns = ['Word 1', 'Word 2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "vocational-nurse",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataComPy Comparison\n",
-      "--------------------\n",
-      "\n",
-      "DataFrame Summary\n",
-      "-----------------\n",
-      "\n",
-      "  DataFrame  Columns  Rows\n",
-      "0       df1       34   344\n",
-      "1       df2       34   344\n",
-      "\n",
-      "Column Summary\n",
-      "--------------\n",
-      "\n",
-      "Number of columns in common: 32\n",
-      "Number of columns in df1 but not in df2: 2\n",
-      "Number of columns in df2 but not in df1: 2\n",
-      "\n",
-      "Row Summary\n",
-      "-----------\n",
-      "\n",
-      "Matched on: word 1, word 2\n",
-      "Any duplicates on match values: No\n",
-      "Absolute Tolerance: 0\n",
-      "Relative Tolerance: 0\n",
-      "Number of rows in common: 344\n",
-      "Number of rows in df1 but not in df2: 0\n",
-      "Number of rows in df2 but not in df1: 0\n",
-      "\n",
-      "Number of rows with some compared columns unequal: 343\n",
-      "Number of rows with all compared columns equal: 1\n",
-      "\n",
-      "Column Comparison\n",
-      "-----------------\n",
-      "\n",
-      "Number of columns compared with some values unequal: 27\n",
-      "Number of columns compared with all values equal: 5\n",
-      "Total number of values which compare unequal: 5,136\n",
-      "\n",
-      "Columns with Unequal Values or Types\n",
-      "------------------------------------\n",
-      "\n",
-      "                                 Column df1 dtype df2 dtype  # Unequal      Max Diff  # Null Diff\n",
-      "25            abstract_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "17              abstract_19k_old_cossim   float64   float64          9  4.440892e-16            0\n",
-      "8   abstract_firstsent_19k_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "19    abstract_firstsent_19k_old_cossim   float64   float64          8  4.440892e-16            0\n",
-      "6                              category    object    object          4  0.000000e+00            0\n",
-      "12               complex_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "21       complex_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "11         complex_probase_2_new_cossim   float64   float64          8  4.440892e-16            0\n",
-      "18           complex_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "1            concat_19k_v1_2_new_cossim   float64   float64        343  9.656675e-01            0\n",
-      "22             concat_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "2            concat_19k_v2_2_new_cossim   float64   float64        325  4.810998e-01            0\n",
-      "3              concat_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "10   concat_probase_19k_v1_2_new_cossim   float64   float64        341  9.673423e-01            0\n",
-      "7      concat_probase_19k_v1_old_cossim   float64   float64        343  9.684394e-01            0\n",
-      "26   concat_probase_19k_v2_2_new_cossim   float64   float64        324  4.828636e-01            0\n",
-      "9      concat_probase_19k_v2_old_cossim   float64   float64        324  4.842197e-01            0\n",
-      "15         text_2props_19k_2_new_cossim   float64   float64          7  4.440892e-16            0\n",
-      "0            text_2props_19k_old_cossim   float64   float64          5  4.440892e-16            0\n",
-      "14         text_7props_19k_2_new_cossim   float64   float64         11  4.440892e-16            0\n",
-      "24           text_7props_19k_old_cossim   float64   float64          7  4.440892e-16            0\n",
-      "5               transe_19k_1_new_cossim   float64   float64          5  4.440892e-16            0\n",
-      "4                 transe_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "16      transe_probase_19k_1_new_cossim   float64   float64        340  1.192889e-08            0\n",
-      "20        transe_probase_19k_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "23          transe_probase_1_new_cossim   float64   float64          9  4.440892e-16            0\n",
-      "13            transe_probase_old_cossim   float64   float64        340  1.452659e+00            0\n",
-      "\n",
-      "Sample Rows with Unequal Values\n",
-      "-------------------------------\n",
-      "\n",
-      "           word 1       word 2  text_2props_19k_old_cossim (df1)  text_2props_19k_old_cossim (df2)\n",
-      "33         bishop        rabbi                          2.333195                          2.333195\n",
-      "69         credit         card                          1.834364                          1.834364\n",
-      "107         drink          ear                          2.515900                          2.515900\n",
-      "271  registration  arrangement                          1.826611                          1.826611\n",
-      "169          line    insurance                          2.670612                          2.670612\n",
-      "\n",
-      "        word 1         word 2  concat_19k_v1_2_new_cossim (df1)  concat_19k_v1_2_new_cossim (df2)\n",
-      "107      drink            ear                          3.116502                          2.813165\n",
-      "112     energy         crisis                          3.024137                          3.099512\n",
-      "14   admission         ticket                          2.647422                          2.819317\n",
-      "63    computer       software                          2.167120                          2.273544\n",
-      "241  precedent        example                          2.091422                          2.138805\n",
-      "44         car         flight                          3.064250                          2.948899\n",
-      "229     planet  constellation                          2.240850                          2.286295\n",
-      "159       king          queen                          2.169999                          2.241265\n",
-      "161        lad        brother                          2.755663                          2.733430\n",
-      "297      stock            egg                          2.943623                          2.952019\n",
-      "\n",
-      "           word 1       word 2  concat_19k_v2_2_new_cossim (df1)  concat_19k_v2_2_new_cossim (df2)\n",
-      "61       computer   laboratory                          2.670662                          2.635687\n",
-      "239     precedent    cognition                          2.643438                          2.676361\n",
-      "286     situation    isolation                          2.313211                          2.399693\n",
-      "225       physics    chemistry                          1.755500                          1.873616\n",
-      "262    psychology       doctor                          2.861167                          2.665892\n",
-      "271  registration  arrangement                          1.935898                          2.144032\n",
-      "111          drug        abuse                          2.653034                          2.716209\n",
-      "150  impartiality     interest                          2.395906                          2.496811\n",
-      "130        forest    graveyard                          2.963496                          2.743907\n",
-      "80            cup    substance                          2.738585                          2.703536\n",
-      "\n",
-      "          word 1       word 2  concat_19k_v2_old_cossim (df1)  concat_19k_v2_old_cossim (df2)\n",
-      "247    president        medal                        2.809529                        3.039440\n",
-      "70        credit  information                        2.557827                        2.567752\n",
-      "58   competition        price                        2.699197                        2.854295\n",
-      "117      exhibit  memorabilia                        2.720881                        2.806831\n",
-      "47       century       nation                        3.025727                        3.021246\n",
-      "280        seven       series                        2.801360                        2.821742\n",
-      "193        money       dollar                        2.376377                        2.437513\n",
-      "5       Japanese     American                        2.564544                        2.535274\n",
-      "340       weapon       secret                        2.889109                        2.897323\n",
-      "189        money         bank                        2.261778                        2.170577\n",
-      "\n",
-      "       word 1     word 2  transe_19k_old_cossim (df1)  transe_19k_old_cossim (df2)\n",
-      "36       book      paper                     3.078243                     2.635533\n",
-      "5    Japanese   American                     2.496058                     2.408249\n",
-      "26       baby     mother                     2.292784                     2.155920\n",
-      "248   problem    airport                     3.826307                     3.170909\n",
-      "55      coast       hill                     3.722873                     2.798622\n",
-      "112    energy     crisis                     3.106233                     3.230795\n",
-      "340    weapon     secret                     3.285620                     3.310261\n",
-      "219     opera   industry                     3.296724                     3.553488\n",
-      "169      line  insurance                     3.089979                     2.829222\n",
-      "332      type       kind                     1.000000                     1.000000\n",
-      "\n",
-      "           word 1       word 2  transe_19k_1_new_cossim (df1)  transe_19k_1_new_cossim (df2)\n",
-      "192         money      deposit                       2.837495                       2.837495\n",
-      "232        planet       people                       3.716617                       3.716617\n",
-      "114        energy    secretary                       2.865451                       2.865451\n",
-      "304        street        block                       2.904156                       2.904156\n",
-      "271  registration  arrangement                       1.933719                       1.933719\n",
-      "\n",
-      "       word 1       word 2 category (df1) category (df2)\n",
-      "296     stock           CD              U              M\n",
-      "45       cell        phone              I              M\n",
-      "328      tool    implement              I              M\n",
-      "237  practice  institution              M              U\n",
-      "\n",
-      "         word 1       word 2  concat_probase_19k_v1_old_cossim (df1)  concat_probase_19k_v1_old_cossim (df2)\n",
-      "312  television         film                                3.266487                                2.813985\n",
-      "191       money     currency                                2.401111                                2.298975\n",
-      "243   precedent  information                                3.131530                                3.019516\n",
-      "53       closet      clothes                                2.947983                                2.705879\n",
-      "142  government       crisis                                3.181431                                2.683486\n",
-      "183       media      trading                                3.103774                                2.822458\n",
-      "89        delay       racism                                3.037041                                2.943246\n",
-      "32         bird        crane                                2.323600                                2.007661\n",
-      "126        food      rooster                                3.641608                                3.323421\n",
-      "43          car   automobile                                1.000000                                1.000000\n",
-      "\n",
-      "         word 1     word 2  abstract_firstsent_19k_2_new_cossim (df1)  abstract_firstsent_19k_2_new_cossim (df2)\n",
-      "67      country    citizen                                   2.307399                                   2.307399\n",
-      "218         oil      stock                                   2.875288                                   2.875288\n",
-      "337       vodka        gin                                   1.848263                                   1.848263\n",
-      "152    investor    earning                                   1.998387                                   1.998387\n",
-      "90   deployment  departure                                   1.826386                                   1.826386\n",
-      "208       movie    theater                                   2.390347                                   2.390347\n",
-      "263  psychology       fear                                   2.679016                                   2.679016\n",
-      "96    discovery      space                                   2.952799                                   2.952799\n",
-      "\n",
-      "           word 1         word 2  concat_probase_19k_v2_old_cossim (df1)  concat_probase_19k_v2_old_cossim (df2)\n",
-      "21   architecture        century                                3.188363                                3.081890\n",
-      "117       exhibit    memorabilia                                2.720881                                2.806831\n",
-      "60       computer       keyboard                                2.131908                                2.212525\n",
-      "120     fertility            egg                                2.202871                                2.244311\n",
-      "311     telephone  communication                                2.677514                                2.463340\n",
-      "238     precedent     antecedent                                1.935676                                2.019795\n",
-      "33         bishop          rabbi                                2.277504                                2.458967\n",
-      "203      morality     importance                                2.272562                                2.446757\n",
-      "228        planet     astronomer                                2.470710                                2.152302\n",
-      "144      governor         office                                2.720705                                2.742336\n",
-      "\n",
-      "      word 1        word 2  concat_probase_19k_v1_2_new_cossim (df1)  concat_probase_19k_v1_2_new_cossim (df2)\n",
-      "166     life         death                                  2.384376                                  2.467146\n",
-      "249  problem     challenge                                  2.573163                                  2.851951\n",
-      "216     noon        string                                  2.831571                                  3.404519\n",
-      "214  network      hardware                                  2.797734                                  2.468385\n",
-      "324    tiger        mammal                                  2.608830                                  2.218881\n",
-      "269   reason  hypertension                                  3.043544                                  2.554564\n",
-      "35      book       library                                  3.335751                                  2.560611\n",
-      "169     line     insurance                                  3.121240                                  2.947265\n",
-      "319    tiger     carnivore                                  3.332882                                  3.004569\n",
-      "161      lad       brother                                  2.885448                                  2.868957\n",
-      "\n",
-      "           word 1      word 2  complex_probase_2_new_cossim (df1)  complex_probase_2_new_cossim (df2)\n",
-      "39          bread      butter                            1.926594                            1.926594\n",
-      "102        dollar        buck                            2.733086                            2.733086\n",
-      "193         money      dollar                            2.733086                            2.733086\n",
-      "150  impartiality    interest                            2.323327                            2.323327\n",
-      "1          Arafat      terror                            3.103668                            3.103668\n",
-      "266    psychology  psychiatry                            1.826092                            1.826092\n",
-      "10         Mexico      Brazil                            1.921104                            1.921104\n",
-      "5        Japanese    American                            2.408249                            2.408249\n",
-      "\n",
-      "          word 1       word 2  complex_19k_old_cossim (df1)  complex_19k_old_cossim (df2)\n",
-      "115  environment      ecology                      2.920428                      2.903515\n",
-      "256   psychology        Freud                      3.234018                      2.992413\n",
-      "169         line    insurance                      3.089979                      2.829222\n",
-      "33        bishop        rabbi                      2.300842                      2.845229\n",
-      "220        opera  performance                      3.163513                      3.062105\n",
-      "267   psychology      science                      2.231959                      2.334221\n",
-      "265   psychology         mind                      2.886509                      2.426014\n",
-      "45          cell        phone                      3.622510                      2.693966\n",
-      "26          baby       mother                      2.292784                      2.155920\n",
-      "161          lad      brother                      3.110214                      3.092837\n",
-      "\n",
-      "     word 1        word 2  transe_probase_old_cossim (df1)  transe_probase_old_cossim (df2)\n",
-      "209  murder  manslaughter                         2.249251                         2.257188\n",
-      "141   glass         metal                         3.225510                         2.093335\n",
-      "296   stock            CD                         3.395233                         3.514011\n",
-      "325   tiger      organism                         3.290722                         2.591059\n",
-      "35     book       library                         3.701910                         2.539030\n",
-      "2       FBI   fingerprint                         3.149125                         2.911334\n",
-      "254  profit          loss                         1.653272                         1.573516\n",
-      "334   video       archive                         3.219152                         2.407191\n",
-      "166    life         death                         2.565452                         2.692000\n",
-      "9      Mars     scientist                         3.504530                         3.187455\n",
-      "\n",
-      "        word 1      word 2  text_7props_19k_2_new_cossim (df1)  text_7props_19k_2_new_cossim (df2)\n",
-      "176     luxury         car                            2.942098                            2.942098\n",
-      "293      space       world                            2.109041                            2.109041\n",
-      "309     summer     drought                            2.687325                            2.687325\n",
-      "67     country     citizen                            1.942392                            1.942392\n",
-      "169       line   insurance                            2.691916                            2.691916\n",
-      "302      stock       phone                            2.963663                            2.963663\n",
-      "5     Japanese    American                            2.145108                            2.145108\n",
-      "238  precedent  antecedent                            1.844509                            1.844509\n",
-      "143   governor   interview                            2.651640                            2.651640\n",
-      "152   investor     earning                            1.902240                            1.902240\n",
-      "\n",
-      "           word 1      word 2  text_2props_19k_2_new_cossim (df1)  text_2props_19k_2_new_cossim (df2)\n",
-      "329         train         car                            1.998965                            1.998965\n",
-      "10         Mexico      Brazil                            1.950233                            1.950233\n",
-      "19   announcement  production                            1.866451                            1.866451\n",
-      "121      fighting   defeating                            1.940122                            1.940122\n",
-      "293         space       world                            1.993016                            1.993016\n",
-      "8        Maradona    football                            2.351065                            2.351065\n",
-      "114        energy   secretary                            2.803514                            2.803514\n",
-      "\n",
-      "      word 1       word 2  transe_probase_19k_1_new_cossim (df1)  transe_probase_19k_1_new_cossim (df2)\n",
-      "155  journal  association                               2.736767                               2.736767\n",
-      "123    focus         life                               2.337493                               2.337493\n",
-      "159     king        queen                               1.570639                               1.570639\n",
-      "169     line    insurance                               2.557647                               2.557647\n",
-      "305   street     children                               3.186623                               3.186623\n",
-      "336    vodka       brandy                               1.527815                               1.527815\n",
-      "232   planet       people                               3.593423                               3.593423\n",
-      "130   forest    graveyard                               3.142560                               3.142560\n",
-      "69    credit         card                               2.076337                               2.076337\n",
-      "40   brother         monk                               2.683273                               2.683273\n",
-      "\n",
-      "         word 1      word 2  abstract_19k_old_cossim (df1)  abstract_19k_old_cossim (df2)\n",
-      "285   situation  conclusion                       1.808156                       1.808156\n",
-      "303      street      avenue                       2.281843                       2.281843\n",
-      "94   disability       death                       2.746255                       2.746255\n",
-      "5      Japanese    American                       3.292768                       3.292768\n",
-      "183       media     trading                       3.108796                       3.108796\n",
-      "11         OPEC     country                       3.042327                       3.042327\n",
-      "162         lad      wizard                       3.334772                       3.334772\n",
-      "129    football      tennis                       2.518421                       2.518421\n",
-      "231      planet        moon                       1.920149                       1.920149\n",
-      "\n",
-      "           word 1        word 2  complex_probase_old_cossim (df1)  complex_probase_old_cossim (df2)\n",
-      "63       computer      software                          2.565704                          2.726328\n",
-      "21   architecture       century                          3.418617                          3.099197\n",
-      "179           man         woman                          2.351588                          1.471079\n",
-      "149         image       surface                          3.137178                          3.256807\n",
-      "81            cup     tableware                          2.124012                          1.954043\n",
-      "36           book         paper                          3.078243                          2.635533\n",
-      "194         money    laundering                          2.759570                          2.800958\n",
-      "236   possibility          girl                          3.361988                          2.920025\n",
-      "209        murder  manslaughter                          2.249251                          2.257188\n",
-      "218           oil         stock                          2.577913                          2.625197\n",
-      "\n",
-      "           word 1      word 2  abstract_firstsent_19k_old_cossim (df1)  abstract_firstsent_19k_old_cossim (df2)\n",
-      "246  preservation       world                                 3.234863                                 3.234863\n",
-      "5        Japanese    American                                 2.728319                                 2.728319\n",
-      "285     situation  conclusion                                 1.808156                                 1.808156\n",
-      "244     precedent         law                                 1.878214                                 1.878214\n",
-      "94     disability       death                                 3.016865                                 3.016865\n",
-      "175         lover     quarrel                                 3.157233                                 3.157233\n",
-      "109         drink      mother                                 3.128050                                 3.128050\n",
-      "177      magician      wizard                                 1.909871                                 1.909871\n",
-      "\n",
-      "        word 1   word 2  transe_probase_19k_old_cossim (df1)  transe_probase_19k_old_cossim (df2)\n",
-      "79         cup   object                             2.627446                             2.362561\n",
-      "234     planet     star                             2.586230                             2.320493\n",
-      "176     luxury      car                             3.150901                             2.977136\n",
-      "156    journey      car                             2.971068                             2.457446\n",
-      "324      tiger   mammal                             2.909669                             2.322927\n",
-      "120  fertility      egg                             2.547399                             2.671721\n",
-      "248    problem  airport                             3.826307                             3.170909\n",
-      "16    aluminum    metal                             2.712132                             1.916614\n",
-      "36        book    paper                             3.078243                             2.635533\n",
-      "211      music  project                             3.141641                             3.304875\n",
-      "\n",
-      "           word 1       word 2  complex_probase_19k_old_cossim (df1)  complex_probase_19k_old_cossim (df2)\n",
-      "277       seafood      lobster                              2.240601                              2.113436\n",
-      "27           bank        money                              3.066413                              2.792811\n",
-      "337         vodka          gin                              1.857971                              2.369972\n",
-      "211         music      project                              3.141641                              3.304875\n",
-      "271  registration  arrangement                              2.319711                              2.954145\n",
-      "143      governor    interview                              4.023383                              3.076389\n",
-      "149         image      surface                              3.137178                              3.256807\n",
-      "94     disability        death                              2.565398                              2.939547\n",
-      "166          life        death                              2.565452                              2.692000\n",
-      "226       physics       proton                              3.802075                              2.800635\n",
-      "\n",
-      "         word 1       word 2  concat_19k_v1_old_cossim (df1)  concat_19k_v1_old_cossim (df2)\n",
-      "270      record       number                        2.977163                        2.589607\n",
-      "282      shower        flood                        3.398140                        2.908007\n",
-      "97     dividend  calculation                        2.507181                        2.708939\n",
-      "239   precedent    cognition                        2.724066                        2.793355\n",
-      "125        food  preparation                        2.932974                        2.836360\n",
-      "342        wood       forest                        3.144257                        2.612681\n",
-      "304      street        block                        2.690410                        2.201689\n",
-      "250  production         crew                        2.552550                        2.783363\n",
-      "6     Jerusalem       Israel                        2.978292                        2.383538\n",
-      "210      museum      theater                        3.208882                        2.943593\n",
-      "\n",
-      "           word 1    word 2  transe_probase_1_new_cossim (df1)  transe_probase_1_new_cossim (df2)\n",
-      "108         drink       eat                           2.334188                           2.334188\n",
-      "25        attempt     peace                           2.624155                           2.624155\n",
-      "232        planet    people                           3.392578                           3.392578\n",
-      "204      morality  marriage                           2.173908                           2.173908\n",
-      "267    psychology   science                           1.840069                           1.840069\n",
-      "20   announcement   warning                           1.930855                           1.930855\n",
-      "37         boxing     round                           3.527058                           3.527058\n",
-      "77            cup      food                           2.657233                           2.657233\n",
-      "73            cup  artifact                           2.065621                           2.065621\n",
-      "\n",
-      "         word 1         word 2  text_7props_19k_old_cossim (df1)  text_7props_19k_old_cossim (df2)\n",
-      "311   telephone  communication                          2.269291                          2.269291\n",
-      "205       movie         critic                          2.872865                          2.872865\n",
-      "69       credit           card                          1.834364                          1.834364\n",
-      "334       video        archive                          1.948662                          1.948662\n",
-      "51       change       attitude                          1.894075                          1.894075\n",
-      "260  psychology     depression                          1.948810                          1.948810\n",
-      "63     computer       software                          1.918968                          1.918968\n",
-      "\n",
-      "          word 1        word 2  abstract_19k_2_new_cossim (df1)  abstract_19k_2_new_cossim (df2)\n",
-      "90    deployment     departure                         1.826386                         1.826386\n",
-      "14     admission        ticket                         2.890691                         2.890691\n",
-      "84           day        summer                         1.820569                         1.820569\n",
-      "217  observation  architecture                         2.541219                         2.541219\n",
-      "243    precedent   information                         2.768309                         2.768309\n",
-      "253    professor        doctor                         2.462181                         2.462181\n",
-      "60      computer      keyboard                         1.977226                         1.977226\n",
-      "95      disaster          area                         3.350219                         3.350219\n",
-      "\n",
-      "        word 1     word 2  concat_probase_19k_v2_2_new_cossim (df1)  concat_probase_19k_v2_2_new_cossim (df2)\n",
-      "208      movie    theater                                  2.614114                                  2.456455\n",
-      "177   magician     wizard                                  2.146197                                  2.011585\n",
-      "86       death        row                                  2.990100                                  2.809539\n",
-      "293      space      world                                  2.481352                                  2.412213\n",
-      "123      focus       life                                  2.908816                                  2.990622\n",
-      "316  territory    surface                                  2.778507                                  2.489232\n",
-      "289      smart    student                                  2.754242                                  2.578396\n",
-      "333     victim  emergency                                  2.380390                                  2.445431\n",
-      "55       coast       hill                                  2.826077                                  2.516979\n",
-      "78         cup     liquid                                  2.365327                                  2.264276\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(compare.report())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "common-vietnam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word 1</th>\n",
-       "      <th>word 2</th>\n",
-       "      <th>id</th>\n",
-       "      <th>h_sim</th>\n",
-       "      <th>h_dim</th>\n",
-       "      <th>f_sim</th>\n",
-       "      <th>f_dim</th>\n",
-       "      <th>n_sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cossim</th>\n",
-       "      <th>concat_19k_v2_2_new_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cossim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cossim</th>\n",
-       "      <th>avg_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q106106</td>\n",
-       "      <td>Q11460</td>\n",
-       "      <td>closet</td>\n",
-       "      <td>clothes</td>\n",
-       "      <td>42</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.0014151543049584102, 0.003161483781535701, ...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.652559</td>\n",
-       "      <td>[0.013902607489678993, 0.013241494756981564, -...</td>\n",
-       "      <td>[0.0014224480432097992, 0.0031547764732941653,...</td>\n",
-       "      <td>[0.01371531560905985, 0.013126875633706597, -0...</td>\n",
-       "      <td>[0.0014206775968733409, 0.0031581452887393872,...</td>\n",
-       "      <td>2.697022</td>\n",
-       "      <td>2.683318</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q16502</td>\n",
-       "      <td>space</td>\n",
-       "      <td>world</td>\n",
-       "      <td>293</td>\n",
-       "      <td>3</td>\n",
-       "      <td>L</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.0031782974877087693, 0.003973805776938075,...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.347308</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[-0.0032792710227188605, 0.0036472880832864947...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[-0.0032450374860671267, 0.0036673577053825254...</td>\n",
-       "      <td>2.485859</td>\n",
-       "      <td>2.481352</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q107</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>space</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>292</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.015268952851071075, -0.0024137893437008715,...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.782963</td>\n",
-       "      <td>[0.00027019944749503126, 0.005353652289998999,...</td>\n",
-       "      <td>[0.015322423107720673, -0.0024500099720118624,...</td>\n",
-       "      <td>[0.00027492382990721006, 0.0053545622809597, -...</td>\n",
-       "      <td>[0.015281665873653082, -0.002421165402091456, ...</td>\n",
-       "      <td>2.798788</td>\n",
-       "      <td>2.795047</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q1074</td>\n",
-       "      <td>Q7364</td>\n",
-       "      <td>skin</td>\n",
-       "      <td>eye</td>\n",
-       "      <td>288</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.002992664951436964, 0.013922942027531155, -...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.314417</td>\n",
-       "      <td>[-0.0026976204618851985, 0.001541600665881322,...</td>\n",
-       "      <td>[0.0033640181667058486, 0.014044398230441645, ...</td>\n",
-       "      <td>[-0.002678224125439186, 0.0015598357892553907,...</td>\n",
-       "      <td>[0.0033484286344994184, 0.014002864550517797, ...</td>\n",
-       "      <td>2.355813</td>\n",
-       "      <td>2.350603</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1079023</td>\n",
-       "      <td>Q500834</td>\n",
-       "      <td>championship</td>\n",
-       "      <td>tournament</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.00920825730356781, 0.011965374832474578, 0....</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.865663</td>\n",
-       "      <td>[-0.0008601077391385219, 0.005201185839222482,...</td>\n",
-       "      <td>[0.00923153244996602, 0.011960544533439752, 0....</td>\n",
-       "      <td>[-0.0008458279784483333, 0.005209858282773185,...</td>\n",
-       "      <td>[0.009207264410626997, 0.011951895965764294, 0...</td>\n",
-       "      <td>1.868699</td>\n",
-       "      <td>1.866785</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 105 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id        word 1      word 2   id  h_sim h_dim  f_sim  \\\n",
-       "0     Q106106      Q11460        closet     clothes   42      3     H      3   \n",
-       "1        Q107      Q16502         space       world  293      3     L      3   \n",
-       "2        Q107       Q2329         space   chemistry  292      4   NaN      4   \n",
-       "3       Q1074       Q7364          skin         eye  288      3     H      3   \n",
-       "4    Q1079023     Q500834  championship  tournament   38      1   NaN      1   \n",
-       "\n",
-       "  f_dim  n_sim  ...                            concat_19k_v2_word2_new  \\\n",
-       "0     H      3  ...  [0.0014151543049584102, 0.003161483781535701, ...   \n",
-       "1     S      3  ...  [-0.0031782974877087693, 0.003973805776938075,...   \n",
-       "2   NaN      4  ...  [0.015268952851071075, -0.0024137893437008715,...   \n",
-       "3     H      3  ...  [0.002992664951436964, 0.013922942027531155, -...   \n",
-       "4   NaN      2  ...  [0.00920825730356781, 0.011965374832474578, 0....   \n",
-       "\n",
-       "   concat_19k_v2_old_cossim concat_19k_v2_2_new_cossim  \\\n",
-       "0                  2.697022                   2.652559   \n",
-       "1                  2.485859                   2.347308   \n",
-       "2                  2.798788                   2.782963   \n",
-       "3                  2.355813                   2.314417   \n",
-       "4                  1.868699                   1.865663   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.013902607489678993, 0.013241494756981564, -...   \n",
-       "1  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "2  [0.00027019944749503126, 0.005353652289998999,...   \n",
-       "3  [-0.0026976204618851985, 0.001541600665881322,...   \n",
-       "4  [-0.0008601077391385219, 0.005201185839222482,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [0.0014224480432097992, 0.0031547764732941653,...   \n",
-       "1  [-0.0032792710227188605, 0.0036472880832864947...   \n",
-       "2  [0.015322423107720673, -0.0024500099720118624,...   \n",
-       "3  [0.0033640181667058486, 0.014044398230441645, ...   \n",
-       "4  [0.00923153244996602, 0.011960544533439752, 0....   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.01371531560905985, 0.013126875633706597, -0...   \n",
-       "1  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "2  [0.00027492382990721006, 0.0053545622809597, -...   \n",
-       "3  [-0.002678224125439186, 0.0015598357892553907,...   \n",
-       "4  [-0.0008458279784483333, 0.005209858282773185,...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [0.0014206775968733409, 0.0031581452887393872,...   \n",
-       "1  [-0.0032450374860671267, 0.0036673577053825254...   \n",
-       "2  [0.015281665873653082, -0.002421165402091456, ...   \n",
-       "3  [0.0033484286344994184, 0.014002864550517797, ...   \n",
-       "4  [0.009207264410626997, 0.011951895965764294, 0...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cossim  concat_probase_19k_v2_2_new_cossim  \\\n",
-       "0                          2.697022                            2.683318   \n",
-       "1                          2.485859                            2.481352   \n",
-       "2                          2.798788                            2.795047   \n",
-       "3                          2.355813                            2.350603   \n",
-       "4                          1.868699                            1.866785   \n",
-       "\n",
-       "  avg_old  \n",
-       "0     3.0  \n",
-       "1     3.2  \n",
-       "2     4.0  \n",
-       "3     3.0  \n",
-       "4     1.2  \n",
-       "\n",
-       "[5 rows x 105 columns]"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "rising-platform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.20519776]])"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "governing-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Word 1', 'Word 2', 'ID', 'H_Sim', 'H_Dim', 'F_Sim', 'F_Dim', 'N_Sim',\n",
-       "       'N_Dim', 'D_Sim',\n",
-       "       ...\n",
-       "       'concat_19k_v2_word1_new', 'concat_19k_v2_word2_new',\n",
-       "       'concat_19k_v2_old_cosSim', 'concat_19k_v2_2_new_cosSim',\n",
-       "       'concat_probase_19k_v2_word1_old', 'concat_probase_19k_v2_word2_old',\n",
-       "       'concat_probase_19k_v2_word1_new', 'concat_probase_19k_v2_word2_new',\n",
-       "       'concat_probase_19k_v2_old_cosSim',\n",
-       "       'concat_probase_19k_v2_2_new_cosSim'],\n",
-       "      dtype='object', length=104)"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "rough-february",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_2_new_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_2_new_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "hired-center",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "extraordinary-value",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_pairs = [('text_7props_19k', 'text_7props_19k_2'),\n",
-    "             ('text_2props_19k', 'text_2props_19k_2'),\n",
-    "             ('complex_19k', 'complex_19k_2'),\n",
-    "             ('transe_19k', 'transe_19k_1'),\n",
-    "             ('abstract_19k', 'abstract_19k_2'),\n",
-    "             ('abstract_firstSent_19k', 'abstract_firstSent_19k_2'),\n",
-    "             ('complex_probase', 'complex_probase_2'),\n",
-    "             ('transe_probase', 'transe_probase_1'),\n",
-    "             ('complex_probase_19k', 'complex_probase_19k_2'),\n",
-    "             ('transe_probase_19k', 'transe_probase_19k_1'),\n",
-    "             ('concat_19k_v1', 'concat_19k_v1_2'),\n",
-    "             ('concat_probase_19k_v1', 'concat_probase_19k_v1_2'),\n",
-    "             ('concat_19k_v2', 'concat_19k_v2_2'),\n",
-    "             ('concat_probase_19k_v2', 'concat_probase_19k_v2_2')]\n",
-    "colMappers = {p[0]+'_old_cosSim': p[1]+'_new_cosSim' for p in emb_pairs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "political-battle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oldColList = list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "flexible-booking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "radio-outline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_with_r3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "recent-portal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# oldColList, [colMappers[col] for col in oldColList]\n",
-    "t1234 = []\n",
-    "t4567 = []\n",
-    "for col in oldColList:\n",
-    "    t1234.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(label_samples), wordSim353AnnotDF_New_Merged_DF[col].apply(label_samples)))\n",
-    "    t4567.append(accuracy_score(wordSim353AnnotDF_New_Merged_DF.Avg.apply(label_samples), wordSim353AnnotDF_New_Merged_DF[colMappers[col]].apply(label_samples)))\n",
-    "                 \n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "id": "opening-inside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = list(zip(indNames,t1234, t4567))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "id": "settled-overhead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1234 = pd.DataFrame(t1234, columns=['Type', 'Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "id": "young-heath",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.686047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>0.680233</td>\n",
-       "      <td>0.680233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.662791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.659884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.656977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.654070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.645349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>0.598837</td>\n",
-       "      <td>0.642442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>0.627907</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.627907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>0.572674</td>\n",
-       "      <td>0.569767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>0.566860</td>\n",
-       "      <td>0.555233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         Type  \\\n",
-       "10          Complex-Transe-AbsFirstSent - 19k   \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "8                       Complex - Probase-19k   \n",
-       "2                               Complex - 19k   \n",
-       "6                           Complex - Probase   \n",
-       "3                                Transe - 19k   \n",
-       "5               Abstract First Sentence - 19k   \n",
-       "7                            Transe - Probase   \n",
-       "9                        Transe - Probase-19k   \n",
-       "4                              Abstract - 19k   \n",
-       "13                 6 embeddings - Probase-19k   \n",
-       "12                         6 embeddings - 19k   \n",
-       "0                          Text 7 props - 19k   \n",
-       "1                          Text 2 props - 19k   \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "10                                           0.680233                  \n",
-       "11                                           0.680233                  \n",
-       "8                                            0.598837                  \n",
-       "2                                            0.598837                  \n",
-       "6                                            0.598837                  \n",
-       "3                                            0.598837                  \n",
-       "5                                            0.651163                  \n",
-       "7                                            0.598837                  \n",
-       "9                                            0.598837                  \n",
-       "4                                            0.627907                  \n",
-       "13                                           0.625000                  \n",
-       "12                                           0.625000                  \n",
-       "0                                            0.572674                  \n",
-       "1                                            0.566860                  \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \n",
-       "10                                           0.686047                 \n",
-       "11                                           0.680233                 \n",
-       "8                                            0.662791                 \n",
-       "2                                            0.659884                 \n",
-       "6                                            0.656977                 \n",
-       "3                                            0.654070                 \n",
-       "5                                            0.654070                 \n",
-       "7                                            0.645349                 \n",
-       "9                                            0.642442                 \n",
-       "4                                            0.627907                 \n",
-       "13                                           0.627907                 \n",
-       "12                                           0.625000                 \n",
-       "0                                            0.569767                 \n",
-       "1                                            0.555233                 "
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t1234.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "beneficial-continuity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "    for col in colList:\n",
-    "        preds = []\n",
-    "        for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "            clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "            clf1.fit(X_train1[[col]], Y_train1)\n",
-    "            preds.append(clf1.predict(X_test1[[col]]))\n",
-    "        acc = 0\n",
-    "        for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "            acc += accuracy_score(pred, Y_test1)\n",
-    "        tempVals.append(acc/N_SPLITS)\n",
-    "    return tempVals\n",
-    "\n",
-    "def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "    tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "    tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "    summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']) * 100)\n",
-    "    summ['Increase'] = summ['Accuracy (in %) of new embeddings compared to annotated category'] - summ['Accuracy (in %) of old embeddings compared to annotated category']\n",
-    "    return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "intensive-platform",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "id": "significant-season",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "fitting-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text 2 props - 19k</th>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>1.159664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Complex - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>69.168067</td>\n",
-       "      <td>0.268908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transe - 19k</th>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract - 19k</th>\n",
-       "      <td>66.546218</td>\n",
-       "      <td>65.983193</td>\n",
-       "      <td>-0.563025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text 7 props - 19k</th>\n",
-       "      <td>67.747899</td>\n",
-       "      <td>67.159664</td>\n",
-       "      <td>-0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abstract First Sentence - 19k</th>\n",
-       "      <td>73.218487</td>\n",
-       "      <td>71.504202</td>\n",
-       "      <td>-1.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     66.000000                  \n",
-       "Complex - 19k                                                          66.865546                  \n",
-       "Combined                                                               68.899160                  \n",
-       "Transe - 19k                                                           66.865546                  \n",
-       "Abstract - 19k                                                         66.546218                  \n",
-       "Text 7 props - 19k                                                     67.747899                  \n",
-       "Abstract First Sentence - 19k                                          73.218487                  \n",
-       "\n",
-       "                               Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Text 2 props - 19k                                                     67.159664                  \n",
-       "Complex - 19k                                                          67.159664                  \n",
-       "Combined                                                               69.168067                  \n",
-       "Transe - 19k                                                           66.571429                  \n",
-       "Abstract - 19k                                                         65.983193                  \n",
-       "Text 7 props - 19k                                                     67.159664                  \n",
-       "Abstract First Sentence - 19k                                          71.504202                  \n",
-       "\n",
-       "                               Increase  \n",
-       "Text 2 props - 19k             1.159664  \n",
-       "Complex - 19k                  0.294118  \n",
-       "Combined                       0.268908  \n",
-       "Transe - 19k                  -0.294118  \n",
-       "Abstract - 19k                -0.563025  \n",
-       "Text 7 props - 19k            -0.588235  \n",
-       "Abstract First Sentence - 19k -1.714286  "
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    "'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', \n",
-    "            'Transe - 19k', 'Abstract - 19k', 'Abstract First Sentence - 19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "loose-cuisine",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Complex + Transe + Abstract FirstSent 19k</th>\n",
-       "      <td>66.176471</td>\n",
-       "      <td>66.462185</td>\n",
-       "      <td>0.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Combined</th>\n",
-       "      <td>68.756303</td>\n",
-       "      <td>69.033613</td>\n",
-       "      <td>0.277311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent</th>\n",
-       "      <td>69.630252</td>\n",
-       "      <td>69.327731</td>\n",
-       "      <td>-0.302521</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.176471                  \n",
-       "Combined                                                                                    68.756303                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.630252                  \n",
-       "\n",
-       "                                                    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "Complex + Transe + Abstract FirstSent 19k                                                   66.462185                  \n",
-       "Combined                                                                                    69.033613                  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra...                                          69.327731                  \n",
-       "\n",
-       "                                                    Increase  \n",
-       "Complex + Transe + Abstract FirstSent 19k           0.285714  \n",
-       "Combined                                            0.277311  \n",
-       "Text7, Text2, Complex, Transe, Abstract, Abstra... -0.302521  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oldColList = ['concat_19k_v1_old_cosSim',\n",
-    "'concat_19k_v2_old_cosSim']\n",
-    "indNames = ['Complex + Transe + Abstract FirstSent 19k', 'Text7, Text2, Complex, Transe, Abstract, Abstract FirstSent']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "second-lightning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text_7props_19k_old_cosSim',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_old_cosSim']"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'old_cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "id": "institutional-gamma",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, [colMappers[col] for col in oldColList], indNames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seven-edinburgh",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "individual-store",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "processed-reynolds",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "oldColList = ['text_7props_19k_old_cosSim',\n",
-    " 'text_2props_19k_old_cosSim',\n",
-    " 'complex_19k_old_cosSim',\n",
-    " 'transe_19k_old_cosSim',\n",
-    " 'abstract_19k_old_cosSim',\n",
-    " 'abstract_firstSent_19k_old_cosSim',\n",
-    " 'complex_probase_old_cosSim',\n",
-    " 'transe_probase_old_cosSim',\n",
-    " 'complex_probase_19k_old_cosSim',\n",
-    " 'transe_probase_19k_old_cosSim',\n",
-    " 'concat_19k_v1_old_cosSim',\n",
-    " 'concat_probase_19k_v1_old_cosSim',\n",
-    " 'concat_19k_v2_old_cosSim',\n",
-    " 'concat_probase_19k_v2_old_cosSim']\n",
-    "indNames = ['Text 7 props - 19k', 'Text 2 props - 19k', 'Complex - 19k', 'Transe - 19k', \n",
-    "            'Abstract - 19k', 'Abstract First Sentence - 19k', 'Complex - Probase', 'Transe - Probase', \n",
-    "            'Complex - Probase-19k', 'Transe - Probase-19k', 'Complex-Transe-AbsFirstSent - 19k',\n",
-    "            'Complex-Transe-AbsFirstSent - Probase-19k', '6 embeddings - 19k',\n",
-    "            '6 embeddings - Probase-19k']\n",
-    "oldColPairs = list(zip(oldColList, indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "forced-dining",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Text 7 props - 19k\n",
-      "Text 2 props - 19k\n",
-      "Complex - 19k\n",
-      "Transe - 19k\n",
-      "Abstract - 19k\n",
-      "Abstract First Sentence - 19k\n",
-      "Complex - Probase\n",
-      "Transe - Probase\n",
-      "Complex - Probase-19k\n",
-      "Transe - Probase-19k\n",
-      "Complex-Transe-AbsFirstSent - 19k\n",
-      "Complex-Transe-AbsFirstSent - Probase-19k\n",
-      "6 embeddings - 19k\n",
-      "6 embeddings - Probase-19k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(indNames))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "permanent-comfort",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2002"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comb(len(oldColPairs), 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quantitative-japanese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16384"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**(len(oldColPairs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "flexible-preparation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accs = []\n",
-    "# for r in tqdm(range(1,len(oldColPairs)+1)):\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         print(len(comb))\n",
-    "# #         oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "# #         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         print(\" & \".join([col[1] for col in comb]).count(\"&\"))\n",
-    "# #         accs.append((indName, oldAcc, newAcc))\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "id": "fatal-publicity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77739ea8b6bf4cfb933f406e6e9699a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "documentary-production",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "necessary-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "id": "divided-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results_flat == results_flat1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "id": "large-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "moved-wheat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "id": "biological-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "artificial-incident",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "restricted-balloon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "id": "palestinian-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.428571</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>64.554622</td>\n",
-       "      <td>63.680672</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2       Complex - 19k   \n",
-       "3        Transe - 19k   \n",
-       "4      Abstract - 19k   \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0                                          65.428571                  \n",
-       "1                                          64.554622                  \n",
-       "2                                          64.579832                  \n",
-       "3                                          64.579832                  \n",
-       "4                                          67.739496                  \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0                                          65.126050                -0.302521   \n",
-       "1                                          63.680672                -0.873950   \n",
-       "2                                          65.445378                 0.865546   \n",
-       "3                                          66.890756                 2.310924   \n",
-       "4                                          68.050420                 0.310924   \n",
-       "\n",
-       "   count  \n",
-       "0      1  \n",
-       "1      1  \n",
-       "2      1  \n",
-       "3      1  \n",
-       "4      1  "
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "id": "congressional-genre",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "id": "single-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "sharp-tunisia",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.873950</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.462185</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>71.243697</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12350</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.504202</td>\n",
-       "      <td>-1.470588</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12329</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-0.899160</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12154</th>\n",
-       "      <td>Text 2 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>-0.890756</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11944</th>\n",
-       "      <td>Text 2 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.781513</td>\n",
-       "      <td>-1.193277</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12349</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>-1.764706</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                         Combination  \\\n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                                                   \n",
-       "30     Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "204    Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                             \n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                                              \n",
-       "60     Abstract - 19k & Abstract First Sentence - 19k                                                                                                                                                                                  \n",
-       "12350  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k    \n",
-       "12329  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k       \n",
-       "12154  Text 2 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k            \n",
-       "11944  Text 2 props - 19k & Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k           \n",
-       "12349  Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5      72.705882                                                          \n",
-       "30     71.840336                                                          \n",
-       "204    71.840336                                                          \n",
-       "18     71.840336                                                          \n",
-       "60     71.546218                                                          \n",
-       "12350  70.974790                                                          \n",
-       "12329  70.974790                                                          \n",
-       "12154  70.974790                                                          \n",
-       "11944  70.974790                                                          \n",
-       "12349  70.974790                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5      71.537815                                                          \n",
-       "30     70.966387                                                          \n",
-       "204    70.378151                                                          \n",
-       "18     71.848739                                                          \n",
-       "60     71.243697                                                          \n",
-       "12350  69.504202                                                          \n",
-       "12329  70.075630                                                          \n",
-       "12154  70.084034                                                          \n",
-       "11944  69.781513                                                          \n",
-       "12349  69.210084                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "5     -1.168067  1      \n",
-       "30    -0.873950  2      \n",
-       "204   -1.462185  3      \n",
-       "18     0.008403  2      \n",
-       "60    -0.302521  2      \n",
-       "12350 -1.470588  8      \n",
-       "12329 -0.899160  8      \n",
-       "12154 -0.890756  8      \n",
-       "11944 -1.193277  8      \n",
-       "12349 -1.764706  8      "
-      ]
-     },
-     "execution_count": 328,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "id": "federal-timing",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>71.840336</td>\n",
-       "      <td>71.848739</td>\n",
-       "      <td>0.008403</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5318</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.394958</td>\n",
-       "      <td>71.546218</td>\n",
-       "      <td>1.151261</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>72.705882</td>\n",
-       "      <td>71.537815</td>\n",
-       "      <td>-1.168067</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>68.630252</td>\n",
-       "      <td>71.521008</td>\n",
-       "      <td>2.890756</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>2.042017</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>694</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>70.084034</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.168067</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8035</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.092437</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.159664</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7049</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.386555</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1916</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.453782</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11327</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>70.100840</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.151261</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                Combination  \\\n",
-       "18     Text 7 props - 19k & Abstract First Sentence - 19k                                                                                                                                                     \n",
-       "5318   Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                                       \n",
-       "5      Abstract First Sentence - 19k                                                                                                                                                                          \n",
-       "51     Transe - 19k & Abstract First Sentence - 19k                                                                                                                                                           \n",
-       "108    Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k                                                                                                                                \n",
-       "694    Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                                                                                            \n",
-       "8035   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                   \n",
-       "7049   Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k                   \n",
-       "1916   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                                                     \n",
-       "11327  Text 7 props - 19k & Transe - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "18     71.840336                                                          \n",
-       "5318   70.394958                                                          \n",
-       "5      72.705882                                                          \n",
-       "51     68.630252                                                          \n",
-       "108    69.210084                                                          \n",
-       "694    70.084034                                                          \n",
-       "8035   70.092437                                                          \n",
-       "7049   70.386555                                                          \n",
-       "1916   69.798319                                                          \n",
-       "11327  70.100840                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "18     71.848739                                                          \n",
-       "5318   71.546218                                                          \n",
-       "5      71.537815                                                          \n",
-       "51     71.521008                                                          \n",
-       "108    71.252101                                                          \n",
-       "694    71.252101                                                          \n",
-       "8035   71.252101                                                          \n",
-       "7049   71.252101                                                          \n",
-       "1916   71.252101                                                          \n",
-       "11327  71.252101                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "18     0.008403  2      \n",
-       "5318   1.151261  6      \n",
-       "5     -1.168067  1      \n",
-       "51     2.890756  2      \n",
-       "108    2.042017  3      \n",
-       "694    1.168067  4      \n",
-       "8035   1.159664  7      \n",
-       "7049   0.865546  7      \n",
-       "1916   1.453782  5      \n",
-       "11327  1.151261  8      "
-      ]
-     },
-     "execution_count": 329,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "id": "known-basement",
-   "metadata": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
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-       "      <th>count</th>\n",
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-       "    <tr>\n",
-       "      <th>1041</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>69.193277</td>\n",
-       "      <td>2.907563</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>2828</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k</td>\n",
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-       "      <th>51</th>\n",
-       "      <td>Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "    <tr>\n",
-       "      <th>9440</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>66.563025</td>\n",
-       "      <td>69.184874</td>\n",
-       "      <td>2.621849</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>2701</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>1035</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase-19k</td>\n",
-       "      <td>66.285714</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>2.613445</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>2689</th>\n",
-       "      <td>Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2834</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k</td>\n",
-       "      <td>65.714286</td>\n",
-       "      <td>68.319328</td>\n",
-       "      <td>2.605042</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>1026</th>\n",
-       "      <td>Complex - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.647059</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>2.579832</td>\n",
-       "      <td>4</td>\n",
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-      "text/plain": [
-       "                                                                                                                                            Combination  \\\n",
-       "1041  Complex - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                                       \n",
-       "2828  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k                                                       \n",
-       "51    Transe - 19k & Abstract First Sentence - 19k                                                                                                        \n",
-       "9440  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "2701  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & Transe - Probase-19k                                                        \n",
-       "1035  Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k                                                                           \n",
-       "1030  Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase-19k                                                                           \n",
-       "2689  Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k                                                           \n",
-       "2834  Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k                                                   \n",
-       "1026  Complex - 19k & Abstract - 19k & Abstract First Sentence - 19k & 6 embeddings - 19k                                                                 \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "1041  66.285714                                                          \n",
-       "2828  65.714286                                                          \n",
-       "51    68.630252                                                          \n",
-       "9440  66.563025                                                          \n",
-       "2701  65.714286                                                          \n",
-       "1035  66.285714                                                          \n",
-       "1030  66.285714                                                          \n",
-       "2689  65.714286                                                          \n",
-       "2834  65.714286                                                          \n",
-       "1026  68.647059                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "1041  69.193277                                                          \n",
-       "2828  68.613445                                                          \n",
-       "51    71.521008                                                          \n",
-       "9440  69.184874                                                          \n",
-       "2701  68.327731                                                          \n",
-       "1035  68.899160                                                          \n",
-       "1030  68.899160                                                          \n",
-       "2689  68.319328                                                          \n",
-       "2834  68.319328                                                          \n",
-       "1026  71.226891                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "1041  2.907563  4      \n",
-       "2828  2.899160  5      \n",
-       "51    2.890756  2      \n",
-       "9440  2.621849  7      \n",
-       "2701  2.613445  5      \n",
-       "1035  2.613445  4      \n",
-       "1030  2.613445  4      \n",
-       "2689  2.605042  5      \n",
-       "2834  2.605042  5      \n",
-       "1026  2.579832  4      "
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "id": "private-conditions",
-   "metadata": {
-    "scrolled": false
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-   "outputs": [
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-     "data": {
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-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
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-       "      <td>-1.168067</td>\n",
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-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>70.092437</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>1.159664</td>\n",
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-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
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-       "      <td>70.672269</td>\n",
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-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>67.739496</td>\n",
-       "      <td>68.050420</td>\n",
-       "      <td>0.310924</td>\n",
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-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
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-       "      <td>67.176471</td>\n",
-       "      <td>-0.294118</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>67.470588</td>\n",
-       "      <td>66.899160</td>\n",
-       "      <td>-0.571429</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.890756</td>\n",
-       "      <td>2.310924</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>2.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Complex - Probase-19k</td>\n",
-       "      <td>64.579832</td>\n",
-       "      <td>65.445378</td>\n",
-       "      <td>0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "5   Abstract First Sentence - 19k               \n",
-       "12  6 embeddings - 19k                          \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "4   Abstract - 19k                              \n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "7   Transe - Probase                            \n",
-       "8   Complex - Probase-19k                       \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5   72.705882                                                          \n",
-       "12  70.092437                                                          \n",
-       "13  70.092437                                                          \n",
-       "4   67.739496                                                          \n",
-       "11  67.470588                                                          \n",
-       "10  67.470588                                                          \n",
-       "3   64.579832                                                          \n",
-       "9   64.579832                                                          \n",
-       "7   64.579832                                                          \n",
-       "8   64.579832                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5   71.537815                                                          \n",
-       "12  71.252101                                                          \n",
-       "13  70.672269                                                          \n",
-       "4   68.050420                                                          \n",
-       "11  67.176471                                                          \n",
-       "10  66.899160                                                          \n",
-       "3   66.890756                                                          \n",
-       "9   66.890756                                                          \n",
-       "7   66.596639                                                          \n",
-       "8   65.445378                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "5  -1.168067  1      \n",
-       "12  1.159664  1      \n",
-       "13  0.579832  1      \n",
-       "4   0.310924  1      \n",
-       "11 -0.294118  1      \n",
-       "10 -0.571429  1      \n",
-       "3   2.310924  1      \n",
-       "9   2.310924  1      \n",
-       "7   2.016807  1      \n",
-       "8   0.865546  1      "
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heard-think",
-   "metadata": {},
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "trying-liabilities",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "competitive-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "accredited-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "specialized-lucas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "published-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "nominated-sweet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "alleged-fruit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-indonesian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "english-brother",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "fantastic-narrow",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.117647</td>\n",
-       "      <td>68.327731</td>\n",
-       "      <td>-3.789916</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>72.100840</td>\n",
-       "      <td>70.361345</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.789916</td>\n",
-       "      <td>-2.025210</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.798319</td>\n",
-       "      <td>-2.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
-       "60   -3.789916  2      \n",
-       "138  -1.739496  3      \n",
-       "151  -1.453782  3      \n",
-       "152  -0.579832  3      \n",
-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of old embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "brown-punishment",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>681</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>71.260504</td>\n",
-       "      <td>0.596639</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.025210</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1575</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>0.016807</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3884</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>68.310924</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>2.924370</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k</td>\n",
-       "      <td>69.764706</td>\n",
-       "      <td>71.235294</td>\n",
-       "      <td>1.470588</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
-       "      <td>-0.252101</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>0.302521</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "681   0.596639  4      \n",
-       "218   0.025210  3      \n",
-       "1575  0.016807  5      \n",
-       "152  -0.579832  3      \n",
-       "3884  2.924370  6      \n",
-       "129   1.470588  3      \n",
-       "217  -0.252101  3      \n",
-       "604  -0.563025  4      \n",
-       "7207  2.361345  7      \n",
-       "690   0.302521  4      "
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "considerable-syndication",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13048</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>70.352941</td>\n",
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-       "      <th>13084</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>69.210084</td>\n",
-       "      <td>4.957983</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>14022</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>64.243697</td>\n",
-       "      <td>68.890756</td>\n",
-       "      <td>4.647059</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>7551</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.722689</td>\n",
-       "      <td>4.630252</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.436975</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>7570</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.428571</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
-       "7551   4.630252  7      \n",
-       "13058  4.352941  9      \n",
-       "6500   4.344538  7      \n",
-       "7537   4.344538  7      \n",
-       "7570   4.336134  7      \n",
-       "6499   4.336134  7      "
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "id": "binary-committee",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>70.630252</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
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-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "approximate-event",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "308px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/SVM Hans.ipynb b/SVM Hans.ipynb
deleted file mode 100644
index 9eb4cd6..0000000
--- a/SVM Hans.ipynb	
+++ /dev/null
@@ -1,3513 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dynamic-generator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "million-analyst",
-   "metadata": {},
-   "source": [
-    "# Determine Differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "operating-launch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_annotation_task_qnodes_kartik_20210629+IDs+jiang_conrath_sim.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "regional-denmark",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimLat = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "functional-chosen",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF.set_index(['Word 1', 'Word 2']).join(wordsimLat.set_index(['Word 1', 'Word 2']),rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "coral-puppy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "alone-rendering",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Yasser Arafat</td>\n",
-       "      <td>former Palestinian President</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>state of harmony characterized by lack of viol...</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Yasser Arafat</td>\n",
-       "      <td>former Palestinian President</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>policy of political repression and violence</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Federal Bureau of Investigation</td>\n",
-       "      <td>United States law enforcement agency</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>biometric identifier</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Federal Bureau of Investigation</td>\n",
-       "      <td>United States law enforcement agency</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>inquiry</td>\n",
-       "      <td>process that has the aim of augmenting knowled...</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.013016090623183913, 0.01114268771757639, -0...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.01327496060589727, 0.010965634890657862, -0...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Harvard University</td>\n",
-       "      <td>private research university in Cambridge, Mass...</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>Yale University</td>\n",
-       "      <td>private research university in New Haven, Conn...</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626159341165, 0.005047111630696947, -0...</td>\n",
-       "      <td>[0.010410829393995813, 0.014776997996557633, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 117 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID word1_kg_id                   word1_kg_label  \\\n",
-       "0   Arafat          peace    8      Q34211                    Yasser Arafat   \n",
-       "1   Arafat         terror    9      Q34211                    Yasser Arafat   \n",
-       "2      FBI    fingerprint  109       Q8333  Federal Bureau of Investigation   \n",
-       "3      FBI  investigation  110       Q8333  Federal Bureau of Investigation   \n",
-       "4  Harvard           Yale  137      Q13371               Harvard University   \n",
-       "\n",
-       "                                word1_kg_description word2_kg_id  \\\n",
-       "0                       former Palestinian President        Q454   \n",
-       "1                       former Palestinian President   Q13648784   \n",
-       "2               United States law enforcement agency     Q178022   \n",
-       "3               United States law enforcement agency   Q21004260   \n",
-       "4  private research university in Cambridge, Mass...      Q49112   \n",
-       "\n",
-       "    word2_kg_label                               word2_kg_description  \\\n",
-       "0            peace  state of harmony characterized by lack of viol...   \n",
-       "1           terror        policy of political repression and violence   \n",
-       "2      fingerprint                               biometric identifier   \n",
-       "3          inquiry  process that has the aim of augmenting knowled...   \n",
-       "4  Yale University  private research university in New Haven, Conn...   \n",
-       "\n",
-       "   Human (Mean)  ...                            concat_19k_v2_word1_new  \\\n",
-       "0        2.1250  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1        3.0625  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2        4.0625  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3        5.0625  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4        4.8750  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...   \n",
-       "\n",
-       "   concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "0                  3.244539                    3.175157   \n",
-       "1                  3.085083                    3.013958   \n",
-       "2                  2.823093                    2.745230   \n",
-       "3                  2.920986                    2.886161   \n",
-       "4                  1.707655                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                          3.244539                           3.240270  \n",
-       "1                          3.085083                           3.013958  \n",
-       "2                          2.823093                           2.816176  \n",
-       "3                          2.920986                           2.919120  \n",
-       "4                          1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 117 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "documented-civilian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 1, 19, 4, 0]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[wordSim353AnnotDF_New_Merged_DF[col].isna().sum() for col in ['Human (Mean)', 'complex', 'transe', 'text', 'class', 'jc_sim']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "hollywood-trade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['complex', 'transe', 'text', 'class', 'jc_sim']:\n",
-    "    wordSim353AnnotDF_New_Merged_DF[col] = wordSim353AnnotDF_New_Merged_DF[col].fillna(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dangerous-syndicate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def determineFarNessOfOld(val1):\n",
-    "#     if 0.000000 <= val1 < 0.496332:\n",
-    "#         return 0\n",
-    "#     else:\n",
-    "#         return 1\n",
-    "# def determineFarNessOfNew(val1):\n",
-    "#     if 0 <= val1 < 0.570044:\n",
-    "#         return 0\n",
-    "#     else:\n",
-    "#         return 1\n",
-    "\n",
-    "# wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff'].apply(determineFarNessOfOld)\n",
-    "# wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] = wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff'].apply(determineFarNessOfNew)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "monthly-application",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[['Word 1', 'Word 2', 'Avg', 'transe_19k_old_cosSim', 'transe_19k_1_new_cosSim', 'transe_19k_old_cosSim_Diff', 'transe_19k_1_new_cosSim_Diff', 'transe_19k_old_cosSim_Diff_Cat', 'transe_19k_1_new_cosSim_Diff_Cat']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ultimate-stuff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "timely-jefferson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# len(wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "miniature-problem",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New_Merged_DF[]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "incorporate-intermediate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/good+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "american-february",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 0) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/good+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "herbal-kitty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 0)].to_csv('../data/transEmb/bad+good.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "outside-korean",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSim353AnnotDF_New_Merged_DF[(wordSim353AnnotDF_New_Merged_DF['transe_19k_old_cosSim_Diff_Cat'] == 1) & (wordSim353AnnotDF_New_Merged_DF['transe_19k_1_new_cosSim_Diff_Cat'] == 1)].to_csv('../data/transEmb/bad+bad.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "steady-alliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "thousand-multiple",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nominated-variance",
-   "metadata": {},
-   "source": [
-    "# SVM Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "perceived-dutch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_annotation_task_qnodes_kartik_20210629+IDs+jiang_conrath_sim.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "choice-worse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Yasser Arafat</td>\n",
-       "      <td>former Palestinian President</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>state of harmony characterized by lack of viol...</td>\n",
-       "      <td>2.1250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Yasser Arafat</td>\n",
-       "      <td>former Palestinian President</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>policy of political repression and violence</td>\n",
-       "      <td>3.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Federal Bureau of Investigation</td>\n",
-       "      <td>United States law enforcement agency</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>biometric identifier</td>\n",
-       "      <td>4.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Federal Bureau of Investigation</td>\n",
-       "      <td>United States law enforcement agency</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>inquiry</td>\n",
-       "      <td>process that has the aim of augmenting knowled...</td>\n",
-       "      <td>5.0625</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.013016090623183913, 0.01114268771757639, -0...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.01327496060589727, 0.010965634890657862, -0...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Harvard University</td>\n",
-       "      <td>private research university in Cambridge, Mass...</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>Yale University</td>\n",
-       "      <td>private research university in New Haven, Conn...</td>\n",
-       "      <td>4.8750</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626159341165, 0.005047111630696947, -0...</td>\n",
-       "      <td>[0.010410829393995813, 0.014776997996557633, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 117 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID word1_kg_id                   word1_kg_label  \\\n",
-       "0   Arafat          peace    8      Q34211                    Yasser Arafat   \n",
-       "1   Arafat         terror    9      Q34211                    Yasser Arafat   \n",
-       "2      FBI    fingerprint  109       Q8333  Federal Bureau of Investigation   \n",
-       "3      FBI  investigation  110       Q8333  Federal Bureau of Investigation   \n",
-       "4  Harvard           Yale  137      Q13371               Harvard University   \n",
-       "\n",
-       "                                word1_kg_description word2_kg_id  \\\n",
-       "0                       former Palestinian President        Q454   \n",
-       "1                       former Palestinian President   Q13648784   \n",
-       "2               United States law enforcement agency     Q178022   \n",
-       "3               United States law enforcement agency   Q21004260   \n",
-       "4  private research university in Cambridge, Mass...      Q49112   \n",
-       "\n",
-       "    word2_kg_label                               word2_kg_description  \\\n",
-       "0            peace  state of harmony characterized by lack of viol...   \n",
-       "1           terror        policy of political repression and violence   \n",
-       "2      fingerprint                               biometric identifier   \n",
-       "3          inquiry  process that has the aim of augmenting knowled...   \n",
-       "4  Yale University  private research university in New Haven, Conn...   \n",
-       "\n",
-       "   Human (Mean)  ...                            concat_19k_v2_word1_new  \\\n",
-       "0        2.1250  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1        3.0625  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2        4.0625  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3        5.0625  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4        4.8750  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...   \n",
-       "\n",
-       "   concat_19k_v2_old_cosSim  concat_19k_v2_2_new_cosSim  \\\n",
-       "0                  3.244539                    3.175157   \n",
-       "1                  3.085083                    3.013958   \n",
-       "2                  2.823093                    2.745230   \n",
-       "3                  2.920986                    2.886161   \n",
-       "4                  1.707655                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "   concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                          3.244539                           3.240270  \n",
-       "1                          3.085083                           3.013958  \n",
-       "2                          2.823093                           2.816176  \n",
-       "3                          2.920986                           2.919120  \n",
-       "4                          1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 117 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "continuing-fraud",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Word 1',\n",
-       " 'Word 2',\n",
-       " 'ID',\n",
-       " 'word1_kg_id',\n",
-       " 'word1_kg_label',\n",
-       " 'word1_kg_description',\n",
-       " 'word2_kg_id',\n",
-       " 'word2_kg_label',\n",
-       " 'word2_kg_description',\n",
-       " 'Human (Mean)',\n",
-       " 'complex',\n",
-       " 'transe',\n",
-       " 'text',\n",
-       " 'class',\n",
-       " 'jc_sim',\n",
-       " 'ID_right',\n",
-       " 'H_Sim',\n",
-       " 'H_Dim',\n",
-       " 'F_Sim',\n",
-       " 'F_Dim',\n",
-       " 'N_Sim',\n",
-       " 'N_Dim',\n",
-       " 'D_Sim',\n",
-       " 'D_Dim',\n",
-       " 'P_Sim',\n",
-       " 'P_Dim',\n",
-       " 'Avg',\n",
-       " 'Stdev',\n",
-       " 'H_orig',\n",
-       " 'H_reversed',\n",
-       " 'word1_kg_id_right',\n",
-       " 'word2_kg_id_right',\n",
-       " 'category',\n",
-       " 'text_7props_19k_word1_old',\n",
-       " 'text_7props_19k_word2_old',\n",
-       " 'text_7props_19k_word1_new',\n",
-       " 'text_7props_19k_word2_new',\n",
-       " 'text_7props_19k_old_cosSim',\n",
-       " 'text_7props_19k_2_new_cosSim',\n",
-       " 'text_2props_19k_word1_old',\n",
-       " 'text_2props_19k_word2_old',\n",
-       " 'text_2props_19k_word1_new',\n",
-       " 'text_2props_19k_word2_new',\n",
-       " 'text_2props_19k_old_cosSim',\n",
-       " 'text_2props_19k_2_new_cosSim',\n",
-       " 'complex_19k_word1_old',\n",
-       " 'complex_19k_word2_old',\n",
-       " 'complex_19k_word1_new',\n",
-       " 'complex_19k_word2_new',\n",
-       " 'complex_19k_old_cosSim',\n",
-       " 'complex_19k_1_new_cosSim',\n",
-       " 'transe_19k_word1_old',\n",
-       " 'transe_19k_word2_old',\n",
-       " 'transe_19k_word1_new',\n",
-       " 'transe_19k_word2_new',\n",
-       " 'transe_19k_old_cosSim',\n",
-       " 'transe_19k_1_new_cosSim',\n",
-       " 'abstract_19k_word1_old',\n",
-       " 'abstract_19k_word2_old',\n",
-       " 'abstract_19k_word1_new',\n",
-       " 'abstract_19k_word2_new',\n",
-       " 'abstract_19k_old_cosSim',\n",
-       " 'abstract_19k_2_new_cosSim',\n",
-       " 'abstract_firstSent_19k_word1_old',\n",
-       " 'abstract_firstSent_19k_word2_old',\n",
-       " 'abstract_firstSent_19k_word1_new',\n",
-       " 'abstract_firstSent_19k_word2_new',\n",
-       " 'abstract_firstSent_19k_old_cosSim',\n",
-       " 'abstract_firstSent_19k_2_new_cosSim',\n",
-       " 'complex_probase_word1_old',\n",
-       " 'complex_probase_word2_old',\n",
-       " 'complex_probase_word1_new',\n",
-       " 'complex_probase_word2_new',\n",
-       " 'complex_probase_old_cosSim',\n",
-       " 'complex_probase_2_new_cosSim',\n",
-       " 'transe_probase_word1_old',\n",
-       " 'transe_probase_word2_old',\n",
-       " 'transe_probase_word1_new',\n",
-       " 'transe_probase_word2_new',\n",
-       " 'transe_probase_old_cosSim',\n",
-       " 'transe_probase_1_new_cosSim',\n",
-       " 'complex_probase_19k_word1_old',\n",
-       " 'complex_probase_19k_word2_old',\n",
-       " 'complex_probase_19k_word1_new',\n",
-       " 'complex_probase_19k_word2_new',\n",
-       " 'complex_probase_19k_old_cosSim',\n",
-       " 'complex_probase_19k_1_new_cosSim',\n",
-       " 'transe_probase_19k_word1_old',\n",
-       " 'transe_probase_19k_word2_old',\n",
-       " 'transe_probase_19k_word1_new',\n",
-       " 'transe_probase_19k_word2_new',\n",
-       " 'transe_probase_19k_old_cosSim',\n",
-       " 'transe_probase_19k_1_new_cosSim',\n",
-       " 'concat_19k_v1_word1_old',\n",
-       " 'concat_19k_v1_word2_old',\n",
-       " 'concat_19k_v1_word1_new',\n",
-       " 'concat_19k_v1_word2_new',\n",
-       " 'concat_19k_v1_old_cosSim',\n",
-       " 'concat_19k_v1_2_new_cosSim',\n",
-       " 'concat_probase_19k_v1_word1_old',\n",
-       " 'concat_probase_19k_v1_word2_old',\n",
-       " 'concat_probase_19k_v1_word1_new',\n",
-       " 'concat_probase_19k_v1_word2_new',\n",
-       " 'concat_probase_19k_v1_old_cosSim',\n",
-       " 'concat_probase_19k_v1_2_new_cosSim',\n",
-       " 'concat_19k_v2_word1_old',\n",
-       " 'concat_19k_v2_word2_old',\n",
-       " 'concat_19k_v2_word1_new',\n",
-       " 'concat_19k_v2_word2_new',\n",
-       " 'concat_19k_v2_old_cosSim',\n",
-       " 'concat_19k_v2_2_new_cosSim',\n",
-       " 'concat_probase_19k_v2_word1_old',\n",
-       " 'concat_probase_19k_v2_word2_old',\n",
-       " 'concat_probase_19k_v2_word1_new',\n",
-       " 'concat_probase_19k_v2_word2_new',\n",
-       " 'concat_probase_19k_v2_old_cosSim',\n",
-       " 'concat_probase_19k_v2_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "conservative-bunch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cosine_similarity(np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word1_new'][1:-1].split(\",\")]).reshape(1,-1), np.array([float(val1) for val1 in wordSim353AnnotDF_New_Merged_DF.iloc[0]['concat_19k_v2_word2_new'][1:-1].split(\",\")]).reshape(1,-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "middle-tower",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['ID', 'Word 1', 'word1_kg_id', 'word1_kg_label', 'word1_kg_description',\n",
-       "       'Word 2', 'word2_kg_id', 'word2_kg_label', 'word2_kg_description',\n",
-       "       'Human (Mean)', 'complex', 'transe', 'text', 'class', 'jc_sim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "growing-edgar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "# from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "respiratory-washer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "#     Y = wordSim353AnnotDF_New_Merged_DF['Avg']\n",
-    "\n",
-    "#     N_SPLITS = 10\n",
-    "\n",
-    "#     skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "#     X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "#     for train_index, test_index in skf.split(X, Y):\n",
-    "#         X_train_splits.append(X.iloc[train_index])\n",
-    "#         X_test_splits.append(X.iloc[test_index])\n",
-    "#         Y_train_splits.append(Y.iloc[train_index])\n",
-    "#         Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "#     preds = []\n",
-    "#     for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "#         clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "#         clf.fit(X_train1, Y_train1)\n",
-    "#         preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "#     tempVals = []å\n",
-    "\n",
-    "#     acc = 0\n",
-    "#     for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "#         acc += mean_squared_error(pred, Y_test1)\n",
-    "\n",
-    "#     tempVals.append(acc/N_SPLITS)\n",
-    "\n",
-    "#     for col in colList:\n",
-    "#         preds = []\n",
-    "#         for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "#             clf1 = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "#             clf1.fit(X_train1[[col]], Y_train1)\n",
-    "#             preds.append(clf1.predict(X_test1[[col]]))\n",
-    "#         acc = 0\n",
-    "#         for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "#             acc += mean_squared_error(pred, Y_test1)\n",
-    "#         tempVals.append(acc/N_SPLITS)\n",
-    "#     return tempVals\n",
-    "\n",
-    "# def compareEmbeddings(wordSim353AnnotDF_New_Merged_DF, oldColList, newColList, indNames):\n",
-    "#     tempVals1 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, oldColList)\n",
-    "#     tempVals2 = trainAndGenerateAccuracies(wordSim353AnnotDF_New_Merged_DF, newColList)\n",
-    "#     summ = (pd.DataFrame(list(zip(tempVals1, tempVals2)), index = ['Combined'] + indNames, columns = ['MSE of old embeddings compared to annotated category', 'MSE of new embeddings compared to annotated category']) * 100)\n",
-    "#     summ['Increase'] = summ['MSE of new embeddings compared to annotated category'] - summ['MSE of old embeddings compared to annotated category']\n",
-    "#     return summ.sort_values(by=['Increase'], ascending=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "sustained-excitement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['Avg']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = KFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVR(gamma='auto'))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += mean_squared_error(pred, Y_test1)\n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "polish-slovakia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "349"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSim353AnnotDF_New_Merged_DF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "answering-island",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def trainAndFindAccuracyClassification(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "#     Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "#     N_SPLITS = 10\n",
-    "\n",
-    "#     skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "#     X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "#     for train_index, test_index in skf.split(X, Y):\n",
-    "#         X_train_splits.append(X.iloc[train_index])\n",
-    "#         X_test_splits.append(X.iloc[test_index])\n",
-    "#         Y_train_splits.append(Y.iloc[train_index])\n",
-    "#         Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "#     preds = []\n",
-    "#     for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "#         clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "#         clf.fit(X_train1, Y_train1)\n",
-    "#         preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "#     tempVals = []\n",
-    "\n",
-    "#     acc = 0\n",
-    "#     for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "#         acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "#     return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "suspected-protein",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "#     X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "#     Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "#     N_SPLITS = 10\n",
-    "\n",
-    "#     skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "#     X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "#     for train_index, test_index in skf.split(X, Y):\n",
-    "#         X_train_splits.append(X.iloc[train_index])\n",
-    "#         X_test_splits.append(X.iloc[test_index])\n",
-    "#         Y_train_splits.append(Y.iloc[train_index])\n",
-    "#         Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "#     preds = []\n",
-    "#     for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "#         clf = RandomForestClassifier(max_depth=max(2 * len(colList) // 3,3), random_state=100)\n",
-    "#         clf.fit(X_train1, Y_train1)\n",
-    "#         preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "#     tempVals = []\n",
-    "\n",
-    "#     acc = 0\n",
-    "#     for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "#         acc += accuracy_score(pred, Y_test1)\n",
-    "\n",
-    "#     return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "intelligent-right",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.016446209419554415"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "colList = ['complex', 'transe', 'text', 'class', 'jc_sim']\n",
-    "indNames = ['Complex', 'Transe', 'Text', 'Class', 'JC_Sim']\n",
-    "colPairs = list(zip(colList, indNames))\n",
-    "trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "collect-learning",
-   "metadata": {},
-   "source": [
-    "## Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "awful-glasgow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['Avg1'] = wordSim353AnnotDF_New_Merged_DF['Avg']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "built-intelligence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['Avg'] = wordSim353AnnotDF_New_Merged_DF['Avg'].apply(lambda p: 1-(p - 1)/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "continuous-mileage",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    349.000000\n",
-       "mean     0.490867  \n",
-       "std      0.189153  \n",
-       "min      0.250000  \n",
-       "25%      0.350000  \n",
-       "50%      0.500000  \n",
-       "75%      0.600000  \n",
-       "max      1.000000  \n",
-       "Name: Avg, dtype: float64"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['Avg'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "opened-morning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21fcb1c963234c3985a2963716e5f090",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[('Complex', 0.02321940095771151), ('Transe', 0.026212630341633864), ('Text', 0.02245616090133273), ('Class', 0.01936172956841233), ('JC_Sim', 0.01933705421406063)], [('Complex & Transe', 0.023498903093071868), ('Complex & Text', 0.020288804874437343), ('Complex & Class', 0.019280969582380746), ('Complex & JC_Sim', 0.018391197762239037), ('Transe & Text', 0.02164415251389583), ('Transe & Class', 0.019243961232510827), ('Transe & JC_Sim', 0.01915285516489467), ('Text & Class', 0.017471550608673957), ('Text & JC_Sim', 0.017272773472134774), ('Class & JC_Sim', 0.018168085523900198)], [('Complex & Transe & Text', 0.020668807932076618), ('Complex & Transe & Class', 0.018976450797180543), ('Complex & Transe & JC_Sim', 0.01856637416827929), ('Complex & Text & Class', 0.016934178158318845), ('Complex & Text & JC_Sim', 0.017271869864320485), ('Complex & Class & JC_Sim', 0.018330482575486425), ('Transe & Text & Class', 0.016909041915910205), ('Transe & Text & JC_Sim', 0.017556257532620998), ('Transe & Class & JC_Sim', 0.018350808562520037), ('Text & Class & JC_Sim', 0.016925180056652625)], [('Complex & Transe & Text & Class', 0.01678190662575939), ('Complex & Transe & Text & JC_Sim', 0.017471885532555027), ('Complex & Transe & Class & JC_Sim', 0.018200759073008162), ('Complex & Text & Class & JC_Sim', 0.016801512232522433), ('Transe & Text & Class & JC_Sim', 0.01651721526582666)], [('Complex & Transe & Text & Class & JC_Sim', 0.016446209419554415)]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(colPairs, i) for i in tqdm(range(1,len(colPairs)+1)))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "comparable-queens",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# from joblib import Parallel, delayed\n",
-    "# def generateCombAccuracies(oldColPairs, r):\n",
-    "#     accs = []\n",
-    "#     for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "#         assert len(comb) == r\n",
-    "#         oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "#         indName = \" & \".join([col[1] for col in comb])\n",
-    "#         accs.append((indName, oldAcc, newAcc))\n",
-    "#     return accs\n",
-    "    \n",
-    "# results = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "# print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "scenic-acoustic",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist[-1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "statistical-nothing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.Series(results_flat).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "knowing-granny",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = [item for sublist in results for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "historic-packing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs = (pd.DataFrame(results_flat, columns = ['Combination','MSE']).set_index('Combination')).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "surrounded-declaration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "satisfied-owner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allCombs['count'] = allCombs['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "radio-requirement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "fifteen-rough",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "stable-lighting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Complex</td>\n",
-       "      <td>0.023219</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Transe</td>\n",
-       "      <td>0.026213</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Text</td>\n",
-       "      <td>0.022456</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Class</td>\n",
-       "      <td>0.019362</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>JC_Sim</td>\n",
-       "      <td>0.019337</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Combination       MSE  count\n",
-       "0  Complex     0.023219  1    \n",
-       "1  Transe      0.026213  1    \n",
-       "2  Text        0.022456  1    \n",
-       "3  Class       0.019362  1    \n",
-       "4  JC_Sim      0.019337  1    "
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "lucky-absorption",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 31 combinations of embedding scores based on the length 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs)} combinations of embedding scores based on the length {len(colPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "rising-explosion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "passive-nowhere",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Complex &amp; Transe &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>0.016446</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Transe &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>0.016517</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Complex &amp; Transe &amp; Text &amp; Class</td>\n",
-       "      <td>0.016782</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Complex &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>0.016802</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Transe &amp; Text &amp; Class</td>\n",
-       "      <td>0.016909</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>0.016925</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Complex &amp; Text &amp; Class</td>\n",
-       "      <td>0.016934</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Complex &amp; Text &amp; JC_Sim</td>\n",
-       "      <td>0.017272</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Text &amp; JC_Sim</td>\n",
-       "      <td>0.017273</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Text &amp; Class</td>\n",
-       "      <td>0.017472</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Combination       MSE  count\n",
-       "30  Complex & Transe & Text & Class & JC_Sim  0.016446  5    \n",
-       "29  Transe & Text & Class & JC_Sim            0.016517  4    \n",
-       "25  Complex & Transe & Text & Class           0.016782  4    \n",
-       "28  Complex & Text & Class & JC_Sim           0.016802  4    \n",
-       "21  Transe & Text & Class                     0.016909  3    \n",
-       "24  Text & Class & JC_Sim                     0.016925  3    \n",
-       "18  Complex & Text & Class                    0.016934  3    \n",
-       "19  Complex & Text & JC_Sim                   0.017272  3    \n",
-       "13  Text & JC_Sim                             0.017273  2    \n",
-       "12  Text & Class                              0.017472  2    "
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs.sort_values(by=['MSE']).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "offensive-spirituality",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "virgin-mountain",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "quick-warner",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>JC_Sim</td>\n",
-       "      <td>0.019337</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Class</td>\n",
-       "      <td>0.019362</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Text</td>\n",
-       "      <td>0.022456</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Complex</td>\n",
-       "      <td>0.023219</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Transe</td>\n",
-       "      <td>0.026213</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Combination       MSE  count\n",
-       "4  JC_Sim      0.019337  1    \n",
-       "3  Class       0.019362  1    \n",
-       "2  Text        0.022456  1    \n",
-       "0  Complex     0.023219  1    \n",
-       "1  Transe      0.026213  1    "
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs[allCombs['count'] == 1].sort_values(by=['MSE']).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "relative-while",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "forced-dominant",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "corresponding-measure",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF['category'] = wordSim353AnnotDF_New_Merged_DF['Avg']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "devoted-thousand",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bb7cc3195524ea588210ba32ad87c4a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'category'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3080, in get_loc\n    return self._engine.get_loc(casted_key)\n  File \"pandas/_libs/index.pyx\", line 70, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 101, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'category'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 431, in _process_worker\n    r = call_item()\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 285, in __call__\n    return self.fn(*self.args, **self.kwargs)\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/_parallel_backends.py\", line 595, in __call__\n    return self.func(*args, **kwargs)\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\", line 262, in __call__\n    return [func(*args, **kwargs)\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"<ipython-input-88-9f71ec8e69c0>\", line 6, in generateCombAccuraciesClassn\n  File \"<ipython-input-86-735432da042b>\", line 3, in trainAndFindAccuracyClassification\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\", line 3024, in __getitem__\n    indexer = self.columns.get_loc(key)\n  File \"/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3082, in get_loc\n    raise KeyError(key) from err\nKeyError: 'category'\n\"\"\"",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-88-9f71ec8e69c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0maccs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mresultsClassn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerateCombAccuraciesClassn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolPairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolPairs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresultsClassn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1055\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    931\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'category'"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuraciesClassn(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyClassification(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "#         newAcc = trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "resultsClassn = Parallel(n_jobs=14)(delayed(generateCombAccuraciesClassn)(colPairs, i) for i in tqdm(range(1,len(colPairs)+1)))\n",
-    "print(resultsClassn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "organized-campbell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "results_flat_classn = [item for sublist in resultsClassn for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confidential-nicaragua",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "allCombs_classn = (pd.DataFrame(results_flat_classn, columns = ['Combination','MSE']).set_index('Combination')).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outstanding-bracelet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs['Increase'] = allCombs['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "transparent-possession",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "allCombs_classn['count'] = allCombs_classn['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "peripheral-sucking",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs.to_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "greek-sleeve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs = pd.read_csv('../data/wordsim353_all_combinations_SVM_accuracies.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "attended-helen",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Complex</td>\n",
-       "      <td>3.064231</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Transe</td>\n",
-       "      <td>3.393020</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Text</td>\n",
-       "      <td>2.593313</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Class</td>\n",
-       "      <td>2.863816</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>JC_Sim</td>\n",
-       "      <td>2.962828</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Combination       MSE  count\n",
-       "0  Complex     3.064231  1    \n",
-       "1  Transe      3.393020  1    \n",
-       "2  Text        2.593313  1    \n",
-       "3  Class       2.863816  1    \n",
-       "4  JC_Sim      2.962828  1    "
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs_classn.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "local-chuck",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 31 combinations of embedding scores based on the length 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs_classn)} combinations of embedding scores based on the length {len(colPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "atomic-screw",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.options.display.max_colwidth = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "rental-scratch",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>2.337720</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Text &amp; Class</td>\n",
-       "      <td>2.339691</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Text &amp; JC_Sim</td>\n",
-       "      <td>2.369431</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Transe &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>2.411251</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Complex &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>2.415421</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Complex &amp; Text &amp; Class</td>\n",
-       "      <td>2.439534</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Transe &amp; Text &amp; Class</td>\n",
-       "      <td>2.473388</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>Complex &amp; Text &amp; JC_Sim</td>\n",
-       "      <td>2.475702</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Transe &amp; Text &amp; JC_Sim</td>\n",
-       "      <td>2.491334</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Complex &amp; Transe &amp; Text &amp; Class &amp; JC_Sim</td>\n",
-       "      <td>2.497628</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Combination       MSE  count\n",
-       "24  Text & Class & JC_Sim                     2.337720  3    \n",
-       "12  Text & Class                              2.339691  2    \n",
-       "13  Text & JC_Sim                             2.369431  2    \n",
-       "29  Transe & Text & Class & JC_Sim            2.411251  4    \n",
-       "28  Complex & Text & Class & JC_Sim           2.415421  4    \n",
-       "18  Complex & Text & Class                    2.439534  3    \n",
-       "21  Transe & Text & Class                     2.473388  3    \n",
-       "19  Complex & Text & JC_Sim                   2.475702  3    \n",
-       "22  Transe & Text & JC_Sim                    2.491334  3    \n",
-       "30  Complex & Transe & Text & Class & JC_Sim  2.497628  5    "
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs_classn.sort_values(by=['MSE']).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "mexican-detective",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs.sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "vocal-morning",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# allCombs.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "yellow-replacement",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>MSE</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Text</td>\n",
-       "      <td>2.593313</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Class</td>\n",
-       "      <td>2.863816</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>JC_Sim</td>\n",
-       "      <td>2.962828</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Complex</td>\n",
-       "      <td>3.064231</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Transe</td>\n",
-       "      <td>3.393020</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Combination       MSE  count\n",
-       "2  Text        2.593313  1    \n",
-       "3  Class       2.863816  1    \n",
-       "4  JC_Sim      2.962828  1    \n",
-       "0  Complex     3.064231  1    \n",
-       "1  Transe      3.393020  1    "
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs_classn[allCombs_classn['count'] == 1].sort_values(by=['MSE']).head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flying-boxing",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "unlikely-divorce",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "123c20d4e60b4e3e8b09c0e0e46efc1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/14 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub data rate exceeded.\n",
-      "The notebook server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--NotebookApp.iopub_data_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
-      "NotebookApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "def generateCombAccuracies(oldColPairs, r):\n",
-    "    accs = []\n",
-    "    for comb in tqdm(combinations(oldColPairs, r)):\n",
-    "        assert len(comb) == r\n",
-    "        oldAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [col[0] for col in comb])\n",
-    "        newAcc = trainAndFindAccuracyRF(wordSim353AnnotDF_New_Merged_DF, [colMappers[col[0]] for col in comb])\n",
-    "        indName = \" & \".join([col[1] for col in comb])\n",
-    "        accs.append((indName, oldAcc, newAcc))\n",
-    "    return accs\n",
-    "    \n",
-    "results1 = Parallel(n_jobs=14)(delayed(generateCombAccuracies)(oldColPairs, i) for i in tqdm(range(1,len(oldColPairs)+1)))\n",
-    "print(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "id": "progressive-width",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results == results1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "id": "quality-listing",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "results_flat1 = [item for sublist in results1 for item in sublist]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "after-prospect",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "allCombs1 = (pd.DataFrame(results_flat1, columns = ['Combination','Accuracy (in %) of old embeddings compared to annotated category', 'Accuracy (in %) of new embeddings compared to annotated category']).set_index('Combination') * 100).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "dated-musician",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "allCombs1['Increase'] = allCombs1['Accuracy (in %) of new embeddings compared to annotated category'] - allCombs['Accuracy (in %) of old embeddings compared to annotated category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "id": "descending-rebate",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "allCombs1['count'] = allCombs1['Combination'].apply(lambda p: p.count(\"&\")+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "union-forty",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Text 7 props - 19k</td>\n",
-       "      <td>65.126050</td>\n",
-       "      <td>63.672269</td>\n",
-       "      <td>-1.453782</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Text 2 props - 19k</td>\n",
-       "      <td>61.899160</td>\n",
-       "      <td>63.344538</td>\n",
-       "      <td>1.445378</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Complex - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>64.277311</td>\n",
-       "      <td>-2.336134</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Combination  \\\n",
-       "0  Text 7 props - 19k   \n",
-       "1  Text 2 props - 19k   \n",
-       "2  Complex - 19k        \n",
-       "3  Transe - 19k         \n",
-       "4  Abstract - 19k       \n",
-       "\n",
-       "   Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "0  65.126050                                                          \n",
-       "1  61.899160                                                          \n",
-       "2  66.613445                                                          \n",
-       "3  66.613445                                                          \n",
-       "4  68.899160                                                          \n",
-       "\n",
-       "   Accuracy (in %) of new embeddings compared to annotated category  Increase  \\\n",
-       "0  63.672269                                                        -1.453782   \n",
-       "1  63.344538                                                         1.445378   \n",
-       "2  64.277311                                                        -2.336134   \n",
-       "3  66.310924                                                        -0.302521   \n",
-       "4  66.008403                                                        -2.890756   \n",
-       "\n",
-       "   count  \n",
-       "0  1      \n",
-       "1  1      \n",
-       "2  1      \n",
-       "3  1      \n",
-       "4  1      "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "increased-dakota",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Determined accuracies of 16383 combinations of embedding scores based on the length 14\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Determined accuracies of {len(allCombs1)} combinations of embedding scores based on the length {len(oldColPairs)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "indirect-baltimore",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
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-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
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-       "      <td>Abstract First Sentence - 19k</td>\n",
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-       "      <th>138</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k</td>\n",
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-       "      <td>70.361345</td>\n",
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-       "      <th>151</th>\n",
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-       "      <td>3</td>\n",
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-       "      <th>152</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.831933</td>\n",
-       "      <td>71.252101</td>\n",
-       "      <td>-0.579832</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>2114</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
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-       "      <td>69.798319</td>\n",
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-       "      <th>2000</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>70.378151</td>\n",
-       "      <td>-1.436975</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.495798</td>\n",
-       "      <td>-2.319328</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>71.815126</td>\n",
-       "      <td>69.487395</td>\n",
-       "      <td>-2.327731</td>\n",
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-       "                                                                                                                                           Combination  \\\n",
-       "5     Abstract First Sentence - 19k                                                                                                                      \n",
-       "60    Abstract - 19k & Abstract First Sentence - 19k                                                                                                     \n",
-       "138   Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k                                                                                \n",
-       "151   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                             \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                     \n",
-       "2114  Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "2001  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k       \n",
-       "2000  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k               \n",
-       "1999  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - Probase-19k               \n",
-       "1998  Text 7 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k & 6 embeddings - 19k                       \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "5     73.285714                                                          \n",
-       "60    72.117647                                                          \n",
-       "138   72.100840                                                          \n",
-       "151   71.831933                                                          \n",
-       "152   71.831933                                                          \n",
-       "2114  71.815126                                                          \n",
-       "2001  71.815126                                                          \n",
-       "2000  71.815126                                                          \n",
-       "1999  71.815126                                                          \n",
-       "1998  71.815126                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "5     70.075630                                                          \n",
-       "60    68.327731                                                          \n",
-       "138   70.361345                                                          \n",
-       "151   70.378151                                                          \n",
-       "152   71.252101                                                          \n",
-       "2114  69.789916                                                          \n",
-       "2001  69.798319                                                          \n",
-       "2000  70.378151                                                          \n",
-       "1999  69.495798                                                          \n",
-       "1998  69.487395                                                          \n",
-       "\n",
-       "      Increase  count  \n",
-       "5    -3.210084  1      \n",
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-       "2114 -2.025210  5      \n",
-       "2001 -2.016807  5      \n",
-       "2000 -1.436975  5      \n",
-       "1999 -2.319328  5      \n",
-       "1998 -2.327731  5      "
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-   "execution_count": 259,
-   "id": "dutch-offense",
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-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
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-       "      <th>129</th>\n",
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-       "      <td>71.226891</td>\n",
-       "      <td>70.974790</td>\n",
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-       "      <td>3</td>\n",
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-       "      <th>604</th>\n",
-       "      <td>Text 7 props - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>71.529412</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>-0.563025</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>7207</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - 19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>70.966387</td>\n",
-       "      <td>2.361345</td>\n",
-       "      <td>7</td>\n",
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-       "      <th>690</th>\n",
-       "      <td>Text 7 props - 19k &amp; Abstract First Sentence - 19k &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.663866</td>\n",
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-       "                                                                                                                                                                                                      Combination  \\\n",
-       "681   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k                                                                                             \n",
-       "218   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "1575  Text 7 props - 19k & Text 2 props - 19k & Abstract - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                          \n",
-       "152   Text 7 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                                \n",
-       "3884  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                              \n",
-       "129   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k                                                                                                                                             \n",
-       "217   Text 2 props - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - 19k                                                                                                                        \n",
-       "604   Text 7 props - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                                 \n",
-       "7207  Text 7 props - 19k & Text 2 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - Probase-19k   \n",
-       "690   Text 7 props - 19k & Abstract First Sentence - 19k & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k                                                                                         \n",
-       "\n",
-       "      Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "681   70.663866                                                          \n",
-       "218   71.226891                                                          \n",
-       "1575  71.235294                                                          \n",
-       "152   71.831933                                                          \n",
-       "3884  68.310924                                                          \n",
-       "129   69.764706                                                          \n",
-       "217   71.226891                                                          \n",
-       "604   71.529412                                                          \n",
-       "7207  68.605042                                                          \n",
-       "690   70.663866                                                          \n",
-       "\n",
-       "      Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "681   71.260504                                                          \n",
-       "218   71.252101                                                          \n",
-       "1575  71.252101                                                          \n",
-       "152   71.252101                                                          \n",
-       "3884  71.235294                                                          \n",
-       "129   71.235294                                                          \n",
-       "217   70.974790                                                          \n",
-       "604   70.966387                                                          \n",
-       "7207  70.966387                                                          \n",
-       "690   70.966387                                                          \n",
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-       "      <th>13861</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Transe - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>9</td>\n",
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-       "      <th>7551</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
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-       "      <td>4.630252</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract First Sentence - 19k &amp; Complex - Probase &amp; Complex - Probase-19k &amp; Complex-Transe-AbsFirstSent - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>64.252101</td>\n",
-       "      <td>68.605042</td>\n",
-       "      <td>4.352941</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6500</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.033613</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7537</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Complex - Probase &amp; Transe - Probase &amp; Complex - Probase-19k &amp; 6 embeddings - Probase-19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.436975</td>\n",
-       "      <td>4.344538</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7570</th>\n",
-       "      <td>Text 7 props - 19k &amp; Complex - 19k &amp; Abstract - 19k &amp; Transe - Probase &amp; Complex - Probase-19k &amp; Transe - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.092437</td>\n",
-       "      <td>67.428571</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6499</th>\n",
-       "      <td>Text 7 props - 19k &amp; Text 2 props - 19k &amp; Complex - 19k &amp; Transe - 19k &amp; Abstract - 19k &amp; Complex - Probase-19k &amp; 6 embeddings - 19k</td>\n",
-       "      <td>63.689076</td>\n",
-       "      <td>68.025210</td>\n",
-       "      <td>4.336134</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                                                                                                                                                                          Combination  \\\n",
-       "13048  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                   \n",
-       "13084  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                                \n",
-       "14022  Text 7 props - 19k & Complex - 19k & Abstract First Sentence - 19k & Transe - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - 19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k   \n",
-       "13861  Text 7 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Transe - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k & 6 embeddings - Probase-19k                       \n",
-       "7551   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - Probase-19k                                                                                              \n",
-       "13058  Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract First Sentence - 19k & Complex - Probase & Complex - Probase-19k & Complex-Transe-AbsFirstSent - Probase-19k & 6 embeddings - 19k                              \n",
-       "6500   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                     \n",
-       "7537   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Complex - Probase & Transe - Probase & Complex - Probase-19k & 6 embeddings - Probase-19k                                                                                                  \n",
-       "7570   Text 7 props - 19k & Complex - 19k & Abstract - 19k & Transe - Probase & Complex - Probase-19k & Transe - Probase-19k & 6 embeddings - 19k                                                                                                       \n",
-       "6499   Text 7 props - 19k & Text 2 props - 19k & Complex - 19k & Transe - 19k & Abstract - 19k & Complex - Probase-19k & 6 embeddings - 19k                                                                                                             \n",
-       "\n",
-       "       Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "13048  64.252101                                                          \n",
-       "13084  64.252101                                                          \n",
-       "14022  64.529412                                                          \n",
-       "13861  64.243697                                                          \n",
-       "7551   63.092437                                                          \n",
-       "13058  64.252101                                                          \n",
-       "6500   63.689076                                                          \n",
-       "7537   63.092437                                                          \n",
-       "7570   63.092437                                                          \n",
-       "6499   63.689076                                                          \n",
-       "\n",
-       "       Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "13048  70.352941                                                          \n",
-       "13084  69.210084                                                          \n",
-       "14022  69.176471                                                          \n",
-       "13861  68.890756                                                          \n",
-       "7551   67.722689                                                          \n",
-       "13058  68.605042                                                          \n",
-       "6500   68.033613                                                          \n",
-       "7537   67.436975                                                          \n",
-       "7570   67.428571                                                          \n",
-       "6499   68.025210                                                          \n",
-       "\n",
-       "       Increase  count  \n",
-       "13048  6.100840  9      \n",
-       "13084  4.957983  9      \n",
-       "14022  4.647059  9      \n",
-       "13861  4.647059  9      \n",
-       "7551   4.630252  7      \n",
-       "13058  4.352941  9      \n",
-       "6500   4.344538  7      \n",
-       "7537   4.344538  7      \n",
-       "7570   4.336134  7      \n",
-       "6499   4.336134  7      "
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1.sort_values(by=['Increase'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "id": "confidential-sandwich",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Combination</th>\n",
-       "      <th>Accuracy (in %) of old embeddings compared to annotated category</th>\n",
-       "      <th>Accuracy (in %) of new embeddings compared to annotated category</th>\n",
-       "      <th>Increase</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - Probase-19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>70.630252</td>\n",
-       "      <td>0.588235</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Abstract First Sentence - 19k</td>\n",
-       "      <td>73.285714</td>\n",
-       "      <td>70.075630</td>\n",
-       "      <td>-3.210084</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Complex-Transe-AbsFirstSent - 19k</td>\n",
-       "      <td>70.042017</td>\n",
-       "      <td>68.302521</td>\n",
-       "      <td>-1.739496</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>6 embeddings - Probase-19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>68.042017</td>\n",
-       "      <td>-0.865546</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>6 embeddings - 19k</td>\n",
-       "      <td>68.907563</td>\n",
-       "      <td>66.857143</td>\n",
-       "      <td>-2.050420</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Complex - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Transe - Probase</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.596639</td>\n",
-       "      <td>-0.016807</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Transe - 19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.310924</td>\n",
-       "      <td>-0.302521</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Transe - Probase-19k</td>\n",
-       "      <td>66.613445</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-0.605042</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Abstract - 19k</td>\n",
-       "      <td>68.899160</td>\n",
-       "      <td>66.008403</td>\n",
-       "      <td>-2.890756</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Combination  \\\n",
-       "11  Complex-Transe-AbsFirstSent - Probase-19k   \n",
-       "5   Abstract First Sentence - 19k               \n",
-       "10  Complex-Transe-AbsFirstSent - 19k           \n",
-       "13  6 embeddings - Probase-19k                  \n",
-       "12  6 embeddings - 19k                          \n",
-       "6   Complex - Probase                           \n",
-       "7   Transe - Probase                            \n",
-       "3   Transe - 19k                                \n",
-       "9   Transe - Probase-19k                        \n",
-       "4   Abstract - 19k                              \n",
-       "\n",
-       "    Accuracy (in %) of old embeddings compared to annotated category  \\\n",
-       "11  70.042017                                                          \n",
-       "5   73.285714                                                          \n",
-       "10  70.042017                                                          \n",
-       "13  68.907563                                                          \n",
-       "12  68.907563                                                          \n",
-       "6   66.613445                                                          \n",
-       "7   66.613445                                                          \n",
-       "3   66.613445                                                          \n",
-       "9   66.613445                                                          \n",
-       "4   68.899160                                                          \n",
-       "\n",
-       "    Accuracy (in %) of new embeddings compared to annotated category  \\\n",
-       "11  70.630252                                                          \n",
-       "5   70.075630                                                          \n",
-       "10  68.302521                                                          \n",
-       "13  68.042017                                                          \n",
-       "12  66.857143                                                          \n",
-       "6   66.596639                                                          \n",
-       "7   66.596639                                                          \n",
-       "3   66.310924                                                          \n",
-       "9   66.008403                                                          \n",
-       "4   66.008403                                                          \n",
-       "\n",
-       "    Increase  count  \n",
-       "11  0.588235  1      \n",
-       "5  -3.210084  1      \n",
-       "10 -1.739496  1      \n",
-       "13 -0.865546  1      \n",
-       "12 -2.050420  1      \n",
-       "6  -0.016807  1      \n",
-       "7  -0.016807  1      \n",
-       "3  -0.302521  1      \n",
-       "9  -0.605042  1      \n",
-       "4  -2.890756  1      "
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "allCombs1[allCombs1['count'] == 1].sort_values(by=['Accuracy (in %) of new embeddings compared to annotated category'], ascending=False).head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "smoking-trinity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "successful-spider",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "moral-thought",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "western-easter",
-   "metadata": {},
-   "source": [
-    "# Generate Embeddings Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "nervous-physiology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = pd.concat([wordSim353AnnotDF_New2[['word1_kg_id', 'Word 1']], wordSim353AnnotDF_New2[['word2_kg_id', 'Word 2']].rename(columns={'Word 2': 'Word 1', 'word2_kg_id': 'word1_kg_id'})])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "neither-hammer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList = wordList[~wordList.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "welsh-indiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in embedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "processed-wonder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['word1_kg_id'].apply(lambda p: \"\\t\".join([str(p1) for p1 in newEmbedDict[p].tolist()])).to_csv('../data/wordsim353_embeddings_new_attempt1.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "psychological-template",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordList['Word 1'].to_csv('../data/wordsim353_embeddings_words.tsv', index=False, header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prime-munich",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assigned-appliance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-textbook",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "prompt-orlando",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "otherwise-qatar",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "inclusive-brick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "micro-kernel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "optical-atlas",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "assured-means",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-fever",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "opened-baker",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "supposed-charm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Scratchpad Nbook.ipynb b/Scratchpad Nbook.ipynb
deleted file mode 100644
index f63eb1f..0000000
--- a/Scratchpad Nbook.ipynb	
+++ /dev/null
@@ -1,848 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "detailed-citizenship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "unlimited-panel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "american-prerequisite",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>concat_19k_v2_word1_new</th>\n",
-       "      <th>concat_19k_v2_word2_new</th>\n",
-       "      <th>concat_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_19k_v2_2_new_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_word1_old</th>\n",
-       "      <th>concat_probase_19k_v2_word2_old</th>\n",
-       "      <th>concat_probase_19k_v2_word1_new</th>\n",
-       "      <th>concat_probase_19k_v2_word2_new</th>\n",
-       "      <th>concat_probase_19k_v2_old_cosSim</th>\n",
-       "      <th>concat_probase_19k_v2_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004515859576248157, -0.01172366887295375, ...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.175157</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005011610937734838, -0.013383541550578623,...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.00497129831746384, -0.01325426749683971, -...</td>\n",
-       "      <td>3.244539</td>\n",
-       "      <td>3.240270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006720443092017323, 0.008331487679612525, 0...</td>\n",
-       "      <td>[-0.004862257088069111, 0.014077170590128979, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.005805280438126139, 0.015115419696749257, ...</td>\n",
-       "      <td>[0.006720440291834372, 0.008331484208161396, 0...</td>\n",
-       "      <td>[-0.004862255062129871, 0.014077164724644747, ...</td>\n",
-       "      <td>3.085083</td>\n",
-       "      <td>3.013958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.01523113309590259, 0.005271417380511587, -0...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.745230</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013922101136130544, 0.0023911842131386003, ...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013814564227839972, 0.0024352598952305004, ...</td>\n",
-       "      <td>2.823093</td>\n",
-       "      <td>2.816176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.006466897059812569, 0.014694837542908411, -...</td>\n",
-       "      <td>[0.013016090623183913, 0.01114268771757639, -0...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.886161</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.01327496060589727, 0.010965634890657862, -0...</td>\n",
-       "      <td>[0.006466894365273728, 0.014694831420063071, -...</td>\n",
-       "      <td>[0.013242882546106787, 0.010953106196386554, -...</td>\n",
-       "      <td>2.920986</td>\n",
-       "      <td>2.919120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.01322626159341165, 0.005047111630696947, -0...</td>\n",
-       "      <td>[0.010410829393995813, 0.014776997996557633, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>[0.013226256082472592, 0.005047109527735016, -...</td>\n",
-       "      <td>[0.010410825056152807, 0.014776991839478788, -...</td>\n",
-       "      <td>1.707655</td>\n",
-       "      <td>1.707655</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 104 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...                            concat_19k_v2_word1_new  \\\n",
-       "0  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "1  ...  [0.006720443092017323, 0.008331487679612525, 0...   \n",
-       "2  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "3  ...  [0.006466897059812569, 0.014694837542908411, -...   \n",
-       "4  ...  [0.01322626159341165, 0.005047111630696947, -0...   \n",
-       "\n",
-       "                             concat_19k_v2_word2_new concat_19k_v2_old_cosSim  \\\n",
-       "0  [-0.004515859576248157, -0.01172366887295375, ...                 3.244539   \n",
-       "1  [-0.004862257088069111, 0.014077170590128979, ...                 3.085083   \n",
-       "2  [0.01523113309590259, 0.005271417380511587, -0...                 2.823093   \n",
-       "3  [0.013016090623183913, 0.01114268771757639, -0...                 2.920986   \n",
-       "4  [0.010410829393995813, 0.014776997996557633, -...                 1.707655   \n",
-       "\n",
-       "   concat_19k_v2_2_new_cosSim  \\\n",
-       "0                    3.175157   \n",
-       "1                    3.013958   \n",
-       "2                    2.745230   \n",
-       "3                    2.886161   \n",
-       "4                    1.707655   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_old  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_old  \\\n",
-       "0  [-0.005011610937734838, -0.013383541550578623,...   \n",
-       "1  [-0.005805280438126139, 0.015115419696749257, ...   \n",
-       "2  [0.013922101136130544, 0.0023911842131386003, ...   \n",
-       "3  [0.01327496060589727, 0.010965634890657862, -0...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word1_new  \\\n",
-       "0  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "1  [0.006720440291834372, 0.008331484208161396, 0...   \n",
-       "2  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "3  [0.006466894365273728, 0.014694831420063071, -...   \n",
-       "4  [0.013226256082472592, 0.005047109527735016, -...   \n",
-       "\n",
-       "                     concat_probase_19k_v2_word2_new  \\\n",
-       "0  [-0.00497129831746384, -0.01325426749683971, -...   \n",
-       "1  [-0.004862255062129871, 0.014077164724644747, ...   \n",
-       "2  [0.013814564227839972, 0.0024352598952305004, ...   \n",
-       "3  [0.013242882546106787, 0.010953106196386554, -...   \n",
-       "4  [0.010410825056152807, 0.014776991839478788, -...   \n",
-       "\n",
-       "  concat_probase_19k_v2_old_cosSim concat_probase_19k_v2_2_new_cosSim  \n",
-       "0                         3.244539                           3.240270  \n",
-       "1                         3.085083                           3.013958  \n",
-       "2                         2.823093                           2.816176  \n",
-       "3                         2.920986                           2.919120  \n",
-       "4                         1.707655                           1.707655  \n",
-       "\n",
-       "[5 rows x 104 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "southwest-maldives",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')\n",
-    "wordSimOG_DF = pd.read_csv('../data/wordsim.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "light-reference",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSimOG_DF.set_index(['Word 1', 'Word 2'])[['word1_kg_id', 'word2_kg_id']]).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ranging-ordering",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
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-       "      <th>ID</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
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-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.6</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
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-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
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-       "      <td>3.6</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>4.0625</td>\n",
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-       "      <td>Q8333</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
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-       "      <td>3</td>\n",
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-       "      <td>Q21004260</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
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-       "      <td>s</td>\n",
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-       "      <td>4.8750</td>\n",
-       "      <td>5.1250</td>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed word1_kg_id  \\\n",
-       "0   NaN    4.0   NaN  3.6  0.547723  2.1250      7.8750      Q34211   \n",
-       "1   NaN    4.0   NaN  3.6  0.547723  3.0625      6.9375      Q34211   \n",
-       "2     u    4.0   NaN  3.6  0.547723  4.0625      5.9375       Q8333   \n",
-       "3     u    3.0     u  3.0  0.000000  5.0625      4.9375       Q8333   \n",
-       "4     s    2.0     s  2.2  0.447214  4.8750      5.1250      Q13371   \n",
-       "\n",
-       "  word2_kg_id  \n",
-       "0        Q454  \n",
-       "1   Q13648784  \n",
-       "2     Q178022  \n",
-       "3   Q21004260  \n",
-       "4      Q49112  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "hidden-textbook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      3.60\n",
-       "1      3.60\n",
-       "2      3.60\n",
-       "3      3.00\n",
-       "4      2.20\n",
-       "       ... \n",
-       "339    3.00\n",
-       "340    3.80\n",
-       "341    3.00\n",
-       "342    1.80\n",
-       "343    3.75\n",
-       "Name: Avg, Length: 344, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "opposite-broadway",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New[['word1_kg_id', 'word2_kg_id', 'Avg']]\n",
-    "temp1 = wordSim353AnnotDF_New_Merged_DF.set_index(['word1_kg_id', 'word2_kg_id']).join(wordSim353AnnotDF_New.set_index(['word1_kg_id', 'word2_kg_id']), rsuffix=\"_old\").reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "ranging-adams",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp2 = temp1[temp1.Avg != temp1.Avg_old][['Avg', 'Avg_old']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "variable-cabinet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "formed-firewall",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Avg_old</th>\n",
-       "      <th>Cat</th>\n",
-       "      <th>Cat_old</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>2.2</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>3.6</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>3.6</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>U</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>2.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>1.8</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>1.8</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185</th>\n",
-       "      <td>2.8</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>193</th>\n",
-       "      <td>2.8</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>1.6</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>I</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>238</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>298</th>\n",
-       "      <td>2.2</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>299</th>\n",
-       "      <td>1.6</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>336</th>\n",
-       "      <td>2.4</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Avg  Avg_old Cat Cat_old\n",
-       "33   4.0      3.4   U       M\n",
-       "37   2.2      3.2   M       M\n",
-       "64   3.6      3.2   U       M\n",
-       "65   3.6      3.2   U       M\n",
-       "66   3.0      3.6   M       U\n",
-       "67   3.0      3.6   M       U\n",
-       "68   3.0      3.2   M       M\n",
-       "69   3.0      3.2   M       M\n",
-       "96   2.8      3.4   M       M\n",
-       "128  1.8      2.2   M       M\n",
-       "164  1.8      2.2   M       M\n",
-       "171  2.0      2.4   M       M\n",
-       "185  2.8      3.6   M       U\n",
-       "193  2.8      3.2   M       M\n",
-       "202  1.6      2.4   I       M\n",
-       "238  2.0      2.4   M       M\n",
-       "298  2.2      2.6   M       M\n",
-       "299  1.6      2.2   I       M\n",
-       "336  2.4      3.0   M       M"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp2['Cat'] = temp2.Avg.apply(labelSamples)\n",
-    "temp2['Cat_old'] = temp2.Avg_old.apply(labelSamples)\n",
-    "temp2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "atmospheric-associate",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "oriented-stockholm",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "instrumental-thing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Similarity Work-Copy1.ipynb b/Similarity Work-Copy1.ipynb
deleted file mode 100644
index bd52b23..0000000
--- a/Similarity Work-Copy1.ipynb	
+++ /dev/null
@@ -1,9632 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "protecting-craft",
-   "metadata": {},
-   "source": [
-    "Downloaded dataset from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "indirect-kingdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "import pandas as pd\n",
-    "from time import time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "elder-russian",
-   "metadata": {},
-   "source": [
-    "# Process and format DBPedia dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "characteristic-orientation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3db8345a6c34dbe8f54427985216b25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cnt = 0\n",
-    "\n",
-    "p1s = []\n",
-    "p11s = []\n",
-    "p2s = []\n",
-    "lines = []\n",
-    "with open(\"../data/short-abstracts_lang=en.ttl\", 'r', encoding='utf-8') as f:\n",
-    "    for line in tqdm(f):\n",
-    "        p1 = line[:line.find(\" \")]\n",
-    "#         p11 = p1.split(\"/\")[-1][:-1]\n",
-    "        p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "        line = line[line.find(\" \")+1:]\n",
-    "        p2 = line[:line.find(\" \")]\n",
-    "        line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "#         print(p1, p2, line)\n",
-    "        p1s.append(p1)\n",
-    "        p11s.append(p11)\n",
-    "        p2s.append(p2)\n",
-    "        lines.append(line)\n",
-    "#         except UnicodeDecodeError:\n",
-    "#             cnt += 1\n",
-    "# print(\"Encoding error:\", cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "prostate-master",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "df1.to_csv('../data/short-abstracts_lang=en.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "occupational-sheriff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "environmental-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "dirty-sample",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df1.set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "defensive-switch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Animalia_(book)</th>\n",
-       "      <td>1</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Animalia_(book)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Animalia is an illustrated children's book by ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Agricultural_science</th>\n",
-       "      <td>2</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Agricultural_scie...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Agricultural science is a broad multidisciplin...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albedo</th>\n",
-       "      <td>3</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Albedo&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Albedo () (Latin: albedo, meaning 'whiteness')...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alain_Connes</th>\n",
-       "      <td>4</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Alain_Connes&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Alain Connes (French: [alɛ̃ kɔn]; born 1 April...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      ignore  \\\n",
-       "node1                          \n",
-       "NaN                        0   \n",
-       "Animalia_(book)            1   \n",
-       "Agricultural_science       2   \n",
-       "Albedo                     3   \n",
-       "Alain_Connes               4   \n",
-       "\n",
-       "                                                                    url  \\\n",
-       "node1                                                                     \n",
-       "NaN                                                                   #   \n",
-       "Animalia_(book)           <http://dbpedia.org/resource/Animalia_(book)>   \n",
-       "Agricultural_science  <http://dbpedia.org/resource/Agricultural_scie...   \n",
-       "Albedo                             <http://dbpedia.org/resource/Albedo>   \n",
-       "Alain_Connes                 <http://dbpedia.org/resource/Alain_Connes>   \n",
-       "\n",
-       "                                                             ignore2  \\\n",
-       "node1                                                                  \n",
-       "NaN                                                          started   \n",
-       "Animalia_(book)       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Agricultural_science  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Albedo                <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Alain_Connes          <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                               abstract  \n",
-       "node1                                                                    \n",
-       "NaN                                                                 NaN  \n",
-       "Animalia_(book)       Animalia is an illustrated children's book by ...  \n",
-       "Agricultural_science  Agricultural science is a broad multidisciplin...  \n",
-       "Albedo                Albedo () (Latin: albedo, meaning 'whiteness')...  \n",
-       "Alain_Connes          Alain Connes (French: [alɛ̃ kɔn]; born 1 April...  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "specified-emission",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore url  ignore2 abstract\n",
-       "node1                              \n",
-       "NaN         0   #  started      NaN"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "streaming-watch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "clean-brook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>23399</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/NaN&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>In computing, NaN, standing for Not a Number, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore                                url  \\\n",
-       "node1                                              \n",
-       "NaN         0                                  #   \n",
-       "NaN     23399  <http://dbpedia.org/resource/NaN>   \n",
-       "\n",
-       "                                              ignore2  \\\n",
-       "node1                                                   \n",
-       "NaN                                           started   \n",
-       "NaN    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                abstract  \n",
-       "node1                                                     \n",
-       "NaN                                                  NaN  \n",
-       "NaN    In computing, NaN, standing for Not a Number, ...  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "residential-bunch",
-   "metadata": {},
-   "source": [
-    "There are many duplicate element names with different abstracts. For example, 390 corresponds to year 390, ibm 390 systemss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "whole-retirement",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Wikidata Sitelinks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "usual-albuquerque",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "classical-tamil",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "undefined-gospel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "backed-criminal",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "armed-gardening",
-   "metadata": {
-    "hidden": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Universe</th>\n",
-       "      <td>Q1-wikipedia_sitelink-5e459a-0</td>\n",
-       "      <td>Q1</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Universe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Boston</th>\n",
-       "      <td>Q100-wikipedia_sitelink-c612f2-0</td>\n",
-       "      <td>Q100</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Boston</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gabon</th>\n",
-       "      <td>Q1000-wikipedia_sitelink-b7f1ee-0</td>\n",
-       "      <td>Q1000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Gabon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dutch_Wikipedia</th>\n",
-       "      <td>Q10000-wikipedia_sitelink-16adbd-0</td>\n",
-       "      <td>Q10000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dutch_Wikipedia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cadier_en_Keer</th>\n",
-       "      <td>Q100000-wikipedia_sitelink-d81a0a-0</td>\n",
-       "      <td>Q100000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Cadier_en_Keer</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  id    node1  \\\n",
-       "trimmedNode2                                                    \n",
-       "Universe              Q1-wikipedia_sitelink-5e459a-0       Q1   \n",
-       "Boston              Q100-wikipedia_sitelink-c612f2-0     Q100   \n",
-       "Gabon              Q1000-wikipedia_sitelink-b7f1ee-0    Q1000   \n",
-       "Dutch_Wikipedia   Q10000-wikipedia_sitelink-16adbd-0   Q10000   \n",
-       "Cadier_en_Keer   Q100000-wikipedia_sitelink-d81a0a-0  Q100000   \n",
-       "\n",
-       "                              label  \\\n",
-       "trimmedNode2                          \n",
-       "Universe         wikipedia_sitelink   \n",
-       "Boston           wikipedia_sitelink   \n",
-       "Gabon            wikipedia_sitelink   \n",
-       "Dutch_Wikipedia  wikipedia_sitelink   \n",
-       "Cadier_en_Keer   wikipedia_sitelink   \n",
-       "\n",
-       "                                                        node2  \n",
-       "trimmedNode2                                                   \n",
-       "Universe                http://en.wikipedia.org/wiki/Universe  \n",
-       "Boston                    http://en.wikipedia.org/wiki/Boston  \n",
-       "Gabon                      http://en.wikipedia.org/wiki/Gabon  \n",
-       "Dutch_Wikipedia  http://en.wikipedia.org/wiki/Dutch_Wikipedia  \n",
-       "Cadier_en_Keer    http://en.wikipedia.org/wiki/Cadier_en_Keer  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sitelinksDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "thousand-marriage",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "alpha-sport",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "editorial-cache",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "naval-morrison",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join DBPedia file with sitelinks - File X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "incorrect-beach",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF = df1.join(sitelinksDF2).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "answering-rhythm",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "peripheral-edinburgh",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "len(abstractsDF), abstractsDF.index.nunique(), abstractsDF.node1.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "first-encoding",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "print(f\"From {len(abstractsDF)} Qnodes, there are {abstractsDF.node2.isna().sum()} dbpedia statements which do not have a sitelink i.e {len(abstractsDF) - abstractsDF.node2.isna().sum()} have a sitelink\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modular-spirituality",
-   "metadata": {},
-   "source": [
-    "# Similarity file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "wired-hayes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF = pd.read_csv(\"../data/SimIP_new.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "referenced-newport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  \n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45  \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29  \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64  \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49  \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "persistent-electronics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesList = set(wordsimDF.g_qnode.to_list() + wordsimDF.p_qnode.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "billion-syndrome",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From the similarity file having 118 records, we have list1 with 19 unique Qnodes and list2 with 21 unique Qnodes. Taking their union, we get 31 unique Qnodes and by taking their intersection, we get 9 unique Qnodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From the similarity file having {len(wordsimDF)} records, we have list1 with {len(set(wordsimDF.g_qnode.to_list()))} unique Qnodes and list2 with {len(set(wordsimDF.p_qnode.to_list()))} unique Qnodes. Taking their union, we get {len(set(wordsimDF.g_qnode.to_list() + wordsimDF.p_qnode.to_list()))} unique Qnodes and by taking their intersection, we get {len(set(wordsimDF.g_qnode.to_list()).intersection(set(wordsimDF.p_qnode.to_list())))} unique Qnodes\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "affecting-people",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListDF1 = pd.DataFrame({'node1': list(wordsimQNodesList)}).set_index('node1')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-document",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Filtering File X Qnodes using similarity file Qnodes list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "universal-romance",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF1 = abstractsDF[abstractsDF.node1.apply(lambda p:p in wordsimQNodesList)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "secure-default",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4002</th>\n",
-       "      <td>100</td>\n",
-       "      <td>159716</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/100&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>100 or one hundred (Roman numeral: C) is the n...</td>\n",
-       "      <td>Q37413-wikipedia_sitelink-c0dbd3-0</td>\n",
-       "      <td>Q37413</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>306492</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1074621</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/5&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>5 (five) is a number, numeral, and glyph. It i...</td>\n",
-       "      <td>Q203-wikipedia_sitelink-c8e811-0</td>\n",
-       "      <td>Q203</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>312986</th>\n",
-       "      <td>7</td>\n",
-       "      <td>103187</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/7&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>7 (seven) is the natural number following 6 an...</td>\n",
-       "      <td>Q23350-wikipedia_sitelink-6dd502-0</td>\n",
-       "      <td>Q23350</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363000</th>\n",
-       "      <td>Abuse</td>\n",
-       "      <td>165447</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Abuse&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Abuse is the improper usage or treatment of a ...</td>\n",
-       "      <td>Q600571-wikipedia_sitelink-5719bc-0</td>\n",
-       "      <td>Q600571</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Abuse</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364696</th>\n",
-       "      <td>Academic_discipline</td>\n",
-       "      <td>1830099</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Academic_discipline&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An academic discipline or academic field is a ...</td>\n",
-       "      <td>Q11862829-wikipedia_sitelink-cdefe0-0</td>\n",
-       "      <td>Q11862829</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Academic_discipline</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      index   ignore  \\\n",
-       "4002                    100   159716   \n",
-       "306492                    5  1074621   \n",
-       "312986                    7   103187   \n",
-       "363000                Abuse   165447   \n",
-       "364696  Academic_discipline  1830099   \n",
-       "\n",
-       "                                                      url  \\\n",
-       "4002                    <http://dbpedia.org/resource/100>   \n",
-       "306492                    <http://dbpedia.org/resource/5>   \n",
-       "312986                    <http://dbpedia.org/resource/7>   \n",
-       "363000                <http://dbpedia.org/resource/Abuse>   \n",
-       "364696  <http://dbpedia.org/resource/Academic_discipline>   \n",
-       "\n",
-       "                                               ignore2  \\\n",
-       "4002    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "306492  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "312986  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "363000  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "364696  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                 abstract  \\\n",
-       "4002    100 or one hundred (Roman numeral: C) is the n...   \n",
-       "306492  5 (five) is a number, numeral, and glyph. It i...   \n",
-       "312986  7 (seven) is the natural number following 6 an...   \n",
-       "363000  Abuse is the improper usage or treatment of a ...   \n",
-       "364696  An academic discipline or academic field is a ...   \n",
-       "\n",
-       "                                           id      node1               label  \\\n",
-       "4002       Q37413-wikipedia_sitelink-c0dbd3-0     Q37413  wikipedia_sitelink   \n",
-       "306492       Q203-wikipedia_sitelink-c8e811-0       Q203  wikipedia_sitelink   \n",
-       "312986     Q23350-wikipedia_sitelink-6dd502-0     Q23350  wikipedia_sitelink   \n",
-       "363000    Q600571-wikipedia_sitelink-5719bc-0    Q600571  wikipedia_sitelink   \n",
-       "364696  Q11862829-wikipedia_sitelink-cdefe0-0  Q11862829  wikipedia_sitelink   \n",
-       "\n",
-       "                                                   node2  \n",
-       "4002                    http://en.wikipedia.org/wiki/100  \n",
-       "306492                    http://en.wikipedia.org/wiki/5  \n",
-       "312986                    http://en.wikipedia.org/wiki/7  \n",
-       "363000                http://en.wikipedia.org/wiki/Abuse  \n",
-       "364696  http://en.wikipedia.org/wiki/Academic_discipline  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "abroad-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initially, there were 5732949 short abstracts. From 5785371 Qnodes with short abstracts, there are 324 Qnodes which have some similarity measure\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Initially, there were {len(df1)} short abstracts. From {len(abstractsDF)} Qnodes with short abstracts, there are {len(abstractsDF1)} Qnodes which have some similarity measure\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "extensive-giving",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "absDFLabelList = abstractsDF1['index'].to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "charged-valuable",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1['index'].nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recent-kansas",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "terminal-offer",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join sitelinks with DBPedia file - File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "particular-nerve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "dimensional-preserve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>Q956244</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>Q353153</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>Q371</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>Q91868926</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>Q2705922</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         index                                     id      node1  \\\n",
-       "0                 Q956244-wikipedia_sitelink-539183-0    Q956244   \n",
-       "1           !!    Q353153-wikipedia_sitelink-d716fb-0    Q353153   \n",
-       "2          !!!       Q371-wikipedia_sitelink-628937-0       Q371   \n",
-       "3      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0  Q91868926   \n",
-       "4  !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   Q2705922   \n",
-       "\n",
-       "                label                                     node2    ignore  \\\n",
-       "0  wikipedia_sitelink           http://en.wikipedia.org/wiki///       NaN   \n",
-       "1  wikipedia_sitelink           http://en.wikipedia.org/wiki/!!       NaN   \n",
-       "2  wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!  211415.0   \n",
-       "3  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!       NaN   \n",
-       "4  wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)  584428.0   \n",
-       "\n",
-       "                                         url  \\\n",
-       "0                                        NaN   \n",
-       "1                                        NaN   \n",
-       "2          <http://dbpedia.org/resource/!!!>   \n",
-       "3                                        NaN   \n",
-       "4  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1                                             NaN   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3                                             NaN   \n",
-       "4  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                            abstract  \n",
-       "0                                                NaN  \n",
-       "1                                                NaN  \n",
-       "2  !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "3                                                NaN  \n",
-       "4  !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "existing-disabled",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 8472828 Qnodes, there are 2741574 sitelink Qnodes which do not have a short abstract i.e 5731254 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "normal-rachel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attempted-scotland",
-   "metadata": {},
-   "source": [
-    "# Join list of similarity file Qnodes with File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "premier-attack",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = sitelinksDF2.join(df1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "disturbed-windsor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp2 = temp1.reset_index().set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "southern-cookbook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q956244</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q353153</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q371</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q91868926</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q2705922</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 index                                     id  \\\n",
-       "node1                                                           \n",
-       "Q956244                   Q956244-wikipedia_sitelink-539183-0   \n",
-       "Q353153             !!    Q353153-wikipedia_sitelink-d716fb-0   \n",
-       "Q371               !!!       Q371-wikipedia_sitelink-628937-0   \n",
-       "Q91868926      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0   \n",
-       "Q2705922   !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   \n",
-       "\n",
-       "                        label                                     node2  \\\n",
-       "node1                                                                     \n",
-       "Q956244    wikipedia_sitelink           http://en.wikipedia.org/wiki///   \n",
-       "Q353153    wikipedia_sitelink           http://en.wikipedia.org/wiki/!!   \n",
-       "Q371       wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!   \n",
-       "Q91868926  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!   \n",
-       "Q2705922   wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)   \n",
-       "\n",
-       "             ignore                                        url  \\\n",
-       "node1                                                            \n",
-       "Q956244         NaN                                        NaN   \n",
-       "Q353153         NaN                                        NaN   \n",
-       "Q371       211415.0          <http://dbpedia.org/resource/!!!>   \n",
-       "Q91868926       NaN                                        NaN   \n",
-       "Q2705922   584428.0  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q956244                                               NaN   \n",
-       "Q353153                                               NaN   \n",
-       "Q371       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q91868926                                             NaN   \n",
-       "Q2705922   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \n",
-       "node1                                                         \n",
-       "Q956244                                                  NaN  \n",
-       "Q353153                                                  NaN  \n",
-       "Q371       !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "Q91868926                                                NaN  \n",
-       "Q2705922   !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "ordinary-connectivity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsDF = wordsimQNodesListDF1.join(temp2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "married-prior",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
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-       "      <th>node1</th>\n",
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-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>Q27096235</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \n",
-       "node1                                                         \n",
-       "Q11499147                                                NaN  \n",
-       "Q1048835                                                 NaN  \n",
-       "Q27096235                                                NaN  \n",
-       "Q48352     A head of state (or chief of state) is the pub...  \n",
-       "Q83307     A minister is a politician who heads a ministr...  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "durable-keeping",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "oriental-devices",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF[~wordsimQNodesListWAbstractsDF.abstract.isna()].index.to_list()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dominican-illinois",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "afraid-asian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beneficial-dynamics",
-   "metadata": {},
-   "source": [
-    "# Adding labels from labels.en"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "satisfactory-nickname",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "potential-vegetable",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P10-label-en</td>\n",
-       "      <td>P10</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'video'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1000-label-en</td>\n",
-       "      <td>P1000</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'record held'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1001-label-en</td>\n",
-       "      <td>P1001</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'applies to jurisdiction'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1002-label-en</td>\n",
-       "      <td>P1002</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'engine configuration'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1003-label-en</td>\n",
-       "      <td>P1003</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'National Library of Romania ID'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id  node1  label                                node2\n",
-       "0    P10-label-en    P10  label                           'video'@en\n",
-       "1  P1000-label-en  P1000  label                     'record held'@en\n",
-       "2  P1001-label-en  P1001  label         'applies to jurisdiction'@en\n",
-       "3  P1002-label-en  P1002  label            'engine configuration'@en\n",
-       "4  P1003-label-en  P1003  label  'National Library of Romania ID'@en"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "significant-cross",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsDF.join(labelsDF.set_index('node1'), rsuffix='_labelsDF')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "bigger-separation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
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-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id_labelsDF</th>\n",
-       "      <th>label_labelsDF</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11499147-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'political activist'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1048835-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'political territorial entity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q27096235</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q27096235-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'artificial geographic entity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "      <td>Q48352-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'head of state'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "      <td>Q83307-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'minister'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352     A head of state (or chief of state) is the pub...   \n",
-       "Q83307     A minister is a politician who heads a ministr...   \n",
-       "\n",
-       "                  id_labelsDF label_labelsDF  \\\n",
-       "node1                                          \n",
-       "Q11499147  Q11499147-label-en          label   \n",
-       "Q1048835    Q1048835-label-en          label   \n",
-       "Q27096235  Q27096235-label-en          label   \n",
-       "Q48352        Q48352-label-en          label   \n",
-       "Q83307        Q83307-label-en          label   \n",
-       "\n",
-       "                              node2_labelsDF  \n",
-       "node1                                         \n",
-       "Q11499147            'political activist'@en  \n",
-       "Q1048835   'political territorial entity'@en  \n",
-       "Q27096235  'artificial geographic entity'@en  \n",
-       "Q48352                    'head of state'@en  \n",
-       "Q83307                         'minister'@en  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "native-british",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsLabelsDF.drop(columns=['id_labelsDF', 'label_labelsDF'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "popular-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp123 = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: type(p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "adolescent-panama",
-   "metadata": {},
-   "outputs": [
-    {
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-       "Columns: [index, id, label, node2, ignore, url, ignore2, abstract, node2_labelsDF]\n",
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-     "execution_count": 49,
-     "metadata": {},
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-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF[temp123 != str]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "twenty-purpose",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: p[1:-4] if type(p) == str else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "beginning-department",
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-       "      <th>node2_labelsDF</th>\n",
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-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political territorial entity</td>\n",
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-       "      <th>Q27096235</th>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "      <td>head of state</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "      <td>minister</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352     A head of state (or chief of state) is the pub...   \n",
-       "Q83307     A minister is a politician who heads a ministr...   \n",
-       "\n",
-       "                         node2_labelsDF  \n",
-       "node1                                    \n",
-       "Q11499147            political activist  \n",
-       "Q1048835   political territorial entity  \n",
-       "Q27096235  artificial geographic entity  \n",
-       "Q48352                    head of state  \n",
-       "Q83307                         minister  "
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "fifteen-patent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = wordsimQNodesListWAbstractsLabelsDF[~wordsimQNodesListWAbstractsLabelsDF.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "structured-archive",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "shared-secretariat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "fossil-courtesy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>Minister_(government)</td>\n",
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-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
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-       "      <td>A minister is a politician who heads a ministr...</td>\n",
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-       "      <th>Q702269</th>\n",
-       "      <td>Professional</td>\n",
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-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Professional</td>\n",
-       "      <td>126325.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Professional&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A professional is a member of a profession or ...</td>\n",
-       "      <td>professional</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1414937</th>\n",
-       "      <td>Combatant</td>\n",
-       "      <td>Q1414937-wikipedia_sitelink-e5fcb2-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Combatant</td>\n",
-       "      <td>80578.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Combatant&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Combatant is the legal status of an individual...</td>\n",
-       "      <td>combatant</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q189290</th>\n",
-       "      <td>Officer_(armed_forces)</td>\n",
-       "      <td>Q189290-wikipedia_sitelink-baaa26-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Officer_(armed_fo...</td>\n",
-       "      <td>3619863.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Officer_(armed_fo...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An officer is a member of an armed forces or u...</td>\n",
-       "      <td>military officer</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "Q702269             Professional   Q702269-wikipedia_sitelink-e8b4b7-0   \n",
-       "Q1414937               Combatant  Q1414937-wikipedia_sitelink-e5fcb2-0   \n",
-       "Q189290   Officer_(armed_forces)   Q189290-wikipedia_sitelink-baaa26-0   \n",
-       "\n",
-       "                       label  \\\n",
-       "node1                          \n",
-       "Q48352    wikipedia_sitelink   \n",
-       "Q83307    wikipedia_sitelink   \n",
-       "Q702269   wikipedia_sitelink   \n",
-       "Q1414937  wikipedia_sitelink   \n",
-       "Q189290   wikipedia_sitelink   \n",
-       "\n",
-       "                                                      node2     ignore  \\\n",
-       "node1                                                                    \n",
-       "Q48352           http://en.wikipedia.org/wiki/Head_of_state     5945.0   \n",
-       "Q83307    http://en.wikipedia.org/wiki/Minister_(governm...   243508.0   \n",
-       "Q702269           http://en.wikipedia.org/wiki/Professional   126325.0   \n",
-       "Q1414937             http://en.wikipedia.org/wiki/Combatant    80578.0   \n",
-       "Q189290   http://en.wikipedia.org/wiki/Officer_(armed_fo...  3619863.0   \n",
-       "\n",
-       "                                                        url  \\\n",
-       "node1                                                         \n",
-       "Q48352          <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307    <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "Q702269          <http://dbpedia.org/resource/Professional>   \n",
-       "Q1414937            <http://dbpedia.org/resource/Combatant>   \n",
-       "Q189290   <http://dbpedia.org/resource/Officer_(armed_fo...   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q48352    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q702269   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1414937  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q189290   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract    node2_labelsDF  \n",
-       "node1                                                                          \n",
-       "Q48352    A head of state (or chief of state) is the pub...     head of state  \n",
-       "Q83307    A minister is a politician who heads a ministr...          minister  \n",
-       "Q702269   A professional is a member of a profession or ...      professional  \n",
-       "Q1414937  Combatant is the legal status of an individual...         combatant  \n",
-       "Q189290   An officer is a member of an armed forces or u...  military officer  "
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "compatible-september",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF1.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "subject-access",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "silver-structure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "dramatic-projection",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstract'] = wordsimQNodesListWAbstractsLabelsDF2.apply(lambda row: row.node2_labelsDF if pd.isna(row.abstract) else row.abstract, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "accessible-trunk",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q27096235</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "      <td>head of state</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "      <td>minister</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                 political activist   \n",
-       "Q1048835                        political territorial entity   \n",
-       "Q27096235                       artificial geographic entity   \n",
-       "Q48352     A head of state (or chief of state) is the pub...   \n",
-       "Q83307     A minister is a politician who heads a ministr...   \n",
-       "\n",
-       "                         node2_labelsDF  \n",
-       "node1                                    \n",
-       "Q11499147            political activist  \n",
-       "Q1048835   political territorial entity  \n",
-       "Q27096235  artificial geographic entity  \n",
-       "Q48352                    head of state  \n",
-       "Q83307                         minister  "
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "colored-imagination",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "labeled-eagle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF2[~wordsimQNodesListWAbstractsLabelsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "welcome-orleans",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF2.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "quick-chicken",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "potential-format",
-   "metadata": {},
-   "source": [
-    "# Fetch saved files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "defined-harris",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "documented-replication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "apart-divide",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Sentence Transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "parental-craps",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "initial-coating",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "documented-alignment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.550719976425171 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertEnc = getEmbeddings('bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "continuing-impression",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1.24G/1.24G [02:08<00:00, 9.68MB/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.648171424865723 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLargeEnc = getEmbeddings('bert-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "responsible-national",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0568156242370605 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "distilBertEnc = getEmbeddings('distilbert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "interracial-mitchell",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEnc = getEmbeddings('roberta-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "amended-powell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.765424489974976 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "robertaEnc = getEmbeddings('roberta-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "divided-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "characteristic-religious",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "quality-sponsorship",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncMap = getMap(bertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "electronic-parcel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncMap = getMap(bertLargeEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "confused-snake",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertMap = getMap(distilBertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "vanilla-subject",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEncMap = getMap(robertaBaseEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "familiar-soundtrack",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaEncMap = getMap(robertaEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "subjective-leeds",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "bronze-sally",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "detailed-imperial",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def checkSimilarity(row, encodingsMap):\n",
-    "#     print(row)\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "funded-prototype",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "improving-nevada",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLargeEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "conventional-white",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(distilBertMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "related-garage",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaBaseEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "portable-joint",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "mental-forestry",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF[['Human (Mean)']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "round-cornell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-79-5105af3a2513>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = bertEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = bertEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "exotic-fundamental",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-122-3b87f5bd1dcd>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = bertLargeEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = bertLargeEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "latter-cleanup",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-80-c904909da8f6>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = distilBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = distilBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "excellent-meeting",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-134-f159fb4d0068>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "south-rwanda",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-81-92c04e1dd63d>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = robertaBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = robertaBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "proper-stations",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-82-96b13f46512b>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "legendary-campus",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-123-cdaec8bd10a3>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "ambient-victor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-83-383da2239499>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "velvet-ethics",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-135-c2b58af44ba8>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "colonial-coordination",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-84-ce07de4ac656>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "informational-fountain",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.float64"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(wordsimDF1.iloc[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "japanese-order",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.426863</td>\n",
-       "      <td>0.360219</td>\n",
-       "      <td>0.436207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.339922</td>\n",
-       "      <td>0.433100</td>\n",
-       "      <td>0.427664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.686578</td>\n",
-       "      <td>0.706978</td>\n",
-       "      <td>0.645407</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.296175</td>\n",
-       "      <td>0.373679</td>\n",
-       "      <td>0.371295</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.340811</td>\n",
-       "      <td>0.390540</td>\n",
-       "      <td>0.488393</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim\n",
-       "0        5.5360  0.426863       0.360219        0.436207\n",
-       "1        4.1250  0.339922       0.433100        0.427664\n",
-       "2        6.6250  0.686578       0.706978        0.645407\n",
-       "3        2.0625  0.296175       0.373679        0.371295\n",
-       "4        7.1875  0.340811       0.390540        0.488393"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "marine-general",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertLargeSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "living-condition",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.robertaBaseBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "hearing-adjustment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertSim *= 10\n",
-    "wordsimDF1.distilBertSim *= 10\n",
-    "wordsimDF1.robertaBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "suspected-plumbing",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>bertLargeSim</th>\n",
-       "      <th>robertaBaseBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.602192</td>\n",
-       "      <td>4.362067</td>\n",
-       "      <td>[[0.4949737852167514]]</td>\n",
-       "      <td>4.196118</td>\n",
-       "      <td>5.313815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.331005</td>\n",
-       "      <td>4.276645</td>\n",
-       "      <td>[[0.4818230866992358]]</td>\n",
-       "      <td>4.045933</td>\n",
-       "      <td>5.239753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>7.069776</td>\n",
-       "      <td>6.454071</td>\n",
-       "      <td>[[0.6407051485261465]]</td>\n",
-       "      <td>6.215223</td>\n",
-       "      <td>6.513252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>3.736790</td>\n",
-       "      <td>3.712948</td>\n",
-       "      <td>[[0.6186497481059658]]</td>\n",
-       "      <td>3.581833</td>\n",
-       "      <td>4.266170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>3.905397</td>\n",
-       "      <td>4.883926</td>\n",
-       "      <td>[[0.8556663921383962]]</td>\n",
-       "      <td>4.322380</td>\n",
-       "      <td>5.111274</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim  \\\n",
-       "0        5.5360  4.268633       3.602192        4.362067   \n",
-       "1        4.1250  3.399217       4.331005        4.276645   \n",
-       "2        6.6250  6.865782       7.069776        6.454071   \n",
-       "3        2.0625  2.961755       3.736790        3.712948   \n",
-       "4        7.1875  3.408112       3.905397        4.883926   \n",
-       "\n",
-       "                  amanSim  bertLargeSim  robertaBaseBertSim  \n",
-       "0  [[0.4949737852167514]]      4.196118            5.313815  \n",
-       "1  [[0.4818230866992358]]      4.045933            5.239753  \n",
-       "2  [[0.6407051485261465]]      6.215223            6.513252  \n",
-       "3  [[0.6186497481059658]]      3.581833            4.266170  \n",
-       "4  [[0.8556663921383962]]      4.322380            5.111274  "
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "incorporate-punishment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.sort_values(by=['Human (Mean)']).plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "prospective-international",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "appointed-tablet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# cosine_similarity([encodingsMap['Q847337']], [encodingsMap['Q551800']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "adaptive-bride",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "beautiful-wages",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "equivalent-wireless",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37556037504702056, pvalue=1.7903693658447951e-25)"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "informal-error",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3529686820781901, pvalue=1.0899920454225404e-22)"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "allied-vulnerability",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3156122362936971, pvalue=1.8520533516011967e-18)"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "victorian-seeking",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3583059753020537, pvalue=2.481962318957373e-23)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fallen-action",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "smart-musician",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "stock-agent",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5316995237579449, pvalue=7.350680368606645e-27)"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "wrapped-batman",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5043971026064306, pvalue=6.35665360448366e-24)"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "fallen-professor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4564962071637959, pvalue=2.286865863469491e-19)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "fewer-stock",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5120576203985243, pvalue=1.0133475563672188e-24)"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-dressing",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "royal-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "threatened-roman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "flexible-metropolitan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "superb-machinery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "excess-sight",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.568063735961914 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "metropolitan-sunglasses",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(118, 118, 1.0)"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "entire-branch",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Human (Mean)'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Human (Mean)'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-70-2ab25beb21dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkendalltau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwordsimDF1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Human (Mean)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwordsimDF1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bertLabelsSim'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Human (Mean)'"
-     ]
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "positive-clone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "choice-alfred",
-   "metadata": {},
-   "source": [
-    "# Amandeep's file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "attached-persian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# q1 = \"kgtk query -i ../data/wikidataos.for.text-embedding.tsv.gz --match '(node1)-[]->(node2)' --where 'node1 in [\" + \",\".join(['\"'+word1+'\"' for word1 in wordsimQNodesListWAbstractsLabelsDF2.index.to_list()]) + \"]' -o ../data/wikidataos.for.text-embedding.subset2.tsv\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "fleet-finder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import os\n",
-    "# os.system(\"screen -dm \"+q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "fancy-personality",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSDF = pd.read_csv('../data/wikidataos.for.text-embedding.subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "broke-grave",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q104021-P1245-868aab-b3f18d7b-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1245</td>\n",
-       "      <td>1412</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q104021-P1296-7a59c3-5f49b5a7-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1296</td>\n",
-       "      <td>0108342</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q104021-P1343-Q19180675-e305e6f3-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q19180675</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q104021-P1343-Q2657718-2d01d189-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q2657718</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q104021-P1343-Q4091875-50bd890f-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q4091875</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   id    node1  label      node2    rank  \\\n",
-       "0     Q104021-P1245-868aab-b3f18d7b-0  Q104021  P1245       1412  normal   \n",
-       "1     Q104021-P1296-7a59c3-5f49b5a7-0  Q104021  P1296    0108342  normal   \n",
-       "2  Q104021-P1343-Q19180675-e305e6f3-0  Q104021  P1343  Q19180675  normal   \n",
-       "3   Q104021-P1343-Q2657718-2d01d189-0  Q104021  P1343   Q2657718  normal   \n",
-       "4   Q104021-P1343-Q4091875-50bd890f-0  Q104021  P1343   Q4091875  normal   \n",
-       "\n",
-       "  node2;wikidatatype  \n",
-       "0        external-id  \n",
-       "1        external-id  \n",
-       "2      wikibase-item  \n",
-       "3      wikibase-item  \n",
-       "4      wikibase-item  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "behavioral-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk query -i ../data/wikidataos.for.text-embedding.subset2.tsv \\\n",
-    "#     --match '(node1)-[]->()' \\\n",
-    "#     --return 'count(distinct node1)'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "resistant-jungle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset2.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "humanitarian-divide",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsLabelsDF2.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "unique-accuracy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF = pd.read_csv('../data/wikidataos-text-embeddings-subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "smooth-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF1 = wikidataOSEmbDF[wikidataOSEmbDF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "wired-surveillance",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.50568223,-0.10802102,-0.3486761,0.78598565,-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.4146536,0.36972183,-0.9731038,0.72235125,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>0.085437864,0.50590533,-0.7758463,0.575578,1.1...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.50568223,-0.10802102,-0.3486761,0.78598565,-...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.4146536,0.36972183,-0.9731038,0.72235125,0....\n",
-       "8      Q9635  0.085437864,0.50590533,-0.7758463,0.575578,1.1..."
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSEmbDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "loved-serial",
-   "metadata": {},
-   "source": [
-    "# Version 2 - Comparison of cosine similarity measures with Amandeep's original file new pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "novel-milwaukee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "computational-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-28-6aed93388a22>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = amanEncSim * 10\n",
-      "<ipython-input-28-6aed93388a22>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "amanEncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataOSEmbDF1.iterrows()}\n",
-    "amanEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(amanEncMap, ))\n",
-    "wordsimDF1['amanSim'] = amanEncSim * 10\n",
-    "wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "virtual-sodium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(amanEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "dated-delta",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3654839025870469, pvalue=3.731075991381434e-24)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "realistic-belize",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5075204852629356, pvalue=3.0234963712614295e-24)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-armor",
-   "metadata": {},
-   "source": [
-    "# Version 3 - P31, P279 edges from lexicalized version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "empty-laugh",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279  \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset3.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "biological-enhancement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = pd.read_csv('../data/wikidataos-text-embeddings-subset3.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "silent-logging",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = wikidataV3DF[wikidataV3DF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "color-sword",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.60533667,-0.32207027,-0.19160181,0.70176995,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.3510175,0.29355207,-1.0211854,0.35043457,1....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>-0.010522888,0.4303582,-0.76446587,0.26379517,...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.60533667,-0.32207027,-0.19160181,0.70176995,...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.3510175,0.29355207,-1.0211854,0.35043457,1....\n",
-       "8      Q9635  -0.010522888,0.4303582,-0.76446587,0.26379517,..."
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataV3DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "essential-virtue",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-36-7dbb17313628>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-      "<ipython-input-36-7dbb17313628>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "wikiV3EncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataV3DF.iterrows()}\n",
-    "wikiV3EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(wikiV3EncMap, ))\n",
-    "wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-    "wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "charming-indicator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(wikiV3EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "dependent-sensitivity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.33429987563573715, pvalue=1.805960166533804e-20)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "fresh-correspondence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4737260118146468, pvalue=6.343179394616253e-21)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "specified-tomorrow",
-   "metadata": {},
-   "source": [
-    "# Version 4 - First sentence of abstract vs human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "opposite-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "single-antigua",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "structured-specialist",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "unnecessary-outdoors",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF1.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "breathing-purchase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q213449</th>\n",
-       "      <td>Eating</td>\n",
-       "      <td>Q213449-wikipedia_sitelink-1e88be-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Eating</td>\n",
-       "      <td>104645.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Eating&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "      <td>eating</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              index                                    id               label  \\\n",
-       "node1                                                                           \n",
-       "Q8068         Flood     Q8068-wikipedia_sitelink-410669-0  wikipedia_sitelink   \n",
-       "Q1368         Money     Q1368-wikipedia_sitelink-59ade8-0  wikipedia_sitelink   \n",
-       "Q872          Radio      Q872-wikipedia_sitelink-1e49e4-0  wikipedia_sitelink   \n",
-       "Q5070802  Emergency  Q5070802-wikipedia_sitelink-41cebc-0  wikipedia_sitelink   \n",
-       "Q213449      Eating   Q213449-wikipedia_sitelink-1e88be-0  wikipedia_sitelink   \n",
-       "\n",
-       "                                           node2     ignore  \\\n",
-       "node1                                                         \n",
-       "Q8068         http://en.wikipedia.org/wiki/Flood    23841.0   \n",
-       "Q1368         http://en.wikipedia.org/wiki/Money  1312979.0   \n",
-       "Q872          http://en.wikipedia.org/wiki/Radio  1866867.0   \n",
-       "Q5070802  http://en.wikipedia.org/wiki/Emergency    80429.0   \n",
-       "Q213449      http://en.wikipedia.org/wiki/Eating   104645.0   \n",
-       "\n",
-       "                                              url  \\\n",
-       "node1                                               \n",
-       "Q8068         <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368         <http://dbpedia.org/resource/Money>   \n",
-       "Q872          <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802  <http://dbpedia.org/resource/Emergency>   \n",
-       "Q213449      <http://dbpedia.org/resource/Eating>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q213449   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract node2_labelsDF  \\\n",
-       "node1                                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...          flood   \n",
-       "Q1368     Money is any item or verifiable record that is...          money   \n",
-       "Q872      Radio is the technology of signaling and commu...          radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...      emergency   \n",
-       "Q213449   Eating (also known as consuming) is the ingest...         eating   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  \n",
-       "Q213449   Eating (also known as consuming) is the ingest...  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "surgical-title",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "grateful-harassment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getFirstSentEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getFirstSentMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "owned-helen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.5808649063110352 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-47-9075dca18092>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-      "<ipython-input-47-9075dca18092>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV4Enc = getFirstSentEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV4EncMap = getFirstSentMap(bertV4Enc)\n",
-    "bertV4EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV4EncMap, ))\n",
-    "wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-    "wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV4EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "christian-median",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(239, 349, 0.6848137535816619)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV4EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "nervous-cycling",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548\n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040\n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217\n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000\n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "retired-cookie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "future-boring",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3633652092919623, pvalue=2.1070516667335453e-22)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "composite-lebanon",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.49261494527923566, pvalue=9.798975953928462e-23)"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupational-rough",
-   "metadata": {},
-   "source": [
-    "# Version 5 - DBPedia's short abstracts vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "effective-elements",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "graduate-congress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "little-yukon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "choice-seating",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.15574932098388672 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV5Enc = getAbsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV5EncMap = getAbsMap(bertV5Enc)\n",
-    "bertV5EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV5EncMap, ))\n",
-    "wordsimDF1['bertV5EncSim'] = bertV5EncSim\n",
-    "wordsimDF1['bertV5EncSim'] = wordsimDF1.bertV5EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV5EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "computational-crime",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(58, 118, 0.4915254237288136)"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV5EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "durable-import",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>5.670399</td>\n",
-       "      <td>4.803427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>4.471632</td>\n",
-       "      <td>5.585540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>7.259848</td>\n",
-       "      <td>6.529090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>4.595971</td>\n",
-       "      <td>4.234194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>5.130080</td>\n",
-       "      <td>4.503454</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  bertV5EncSim  \n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45       5.670399      4.803427  \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29       4.471632      5.585540  \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64       7.259848      6.529090  \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49       4.595971      4.234194  \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33       5.130080      4.503454  "
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "champion-waste",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "pretty-observation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "adverse-calculator",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sexual-leone",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "elder-refund",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q27096235</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "      <td>head of state</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "      <td>minister</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                 political activist   \n",
-       "Q1048835                        political territorial entity   \n",
-       "Q27096235                       artificial geographic entity   \n",
-       "Q48352     A head of state (or chief of state) is the pub...   \n",
-       "Q83307     A minister is a politician who heads a ministr...   \n",
-       "\n",
-       "                         node2_labelsDF  \n",
-       "node1                                    \n",
-       "Q11499147            political activist  \n",
-       "Q1048835   political territorial entity  \n",
-       "Q27096235  artificial geographic entity  \n",
-       "Q48352                    head of state  \n",
-       "Q83307                         minister  "
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "inclusive-gender",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "dental-choice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "understanding-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "political-manufacturer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.17983198165893555 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "oriental-olympus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(118, 118, 1.0)"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "simplified-recipient",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>5.670399</td>\n",
-       "      <td>4.803427</td>\n",
-       "      <td>4.803428</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>4.471632</td>\n",
-       "      <td>5.585540</td>\n",
-       "      <td>5.585539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>7.259848</td>\n",
-       "      <td>6.529090</td>\n",
-       "      <td>6.529088</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>4.595971</td>\n",
-       "      <td>4.234194</td>\n",
-       "      <td>4.234195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>5.130080</td>\n",
-       "      <td>4.503454</td>\n",
-       "      <td>4.503455</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \\\n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45       5.670399   \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29       4.471632   \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64       7.259848   \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49       4.595971   \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33       5.130080   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  \n",
-       "0      4.803427      4.803428  \n",
-       "1      5.585540      5.585539  \n",
-       "2      6.529090      6.529088  \n",
-       "3      4.234194      4.234195  \n",
-       "4      4.503454      4.503455  "
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "convenient-glory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "split-doctor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "criminal-tragedy",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "certified-compact",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "billion-southwest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "relevant-looking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "based-duplicate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1048835</th>\n",
-       "      <td>Political_division</td>\n",
-       "      <td>Q1048835-wikipedia_sitelink-a3aa16-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_division</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "      <td>political territorial entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q27096235</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "      <td>artificial geographic entity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q48352</th>\n",
-       "      <td>Head_of_state</td>\n",
-       "      <td>Q48352-wikipedia_sitelink-0ba458-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Head_of_state</td>\n",
-       "      <td>5945.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Head_of_state&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>A head of state (or chief of state) is the pub...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q83307</th>\n",
-       "      <td>Minister_(government)</td>\n",
-       "      <td>Q83307-wikipedia_sitelink-519dcf-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Minister_(governm...</td>\n",
-       "      <td>243508.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Minister_(governm...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "      <td>minister</td>\n",
-       "      <td>A minister is a politician who heads a ministr...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index                                    id  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                    NaN                                   NaN   \n",
-       "Q1048835      Political_division  Q1048835-wikipedia_sitelink-a3aa16-0   \n",
-       "Q27096235                    NaN                                   NaN   \n",
-       "Q48352             Head_of_state    Q48352-wikipedia_sitelink-0ba458-0   \n",
-       "Q83307     Minister_(government)    Q83307-wikipedia_sitelink-519dcf-0   \n",
-       "\n",
-       "                        label  \\\n",
-       "node1                           \n",
-       "Q11499147                 NaN   \n",
-       "Q1048835   wikipedia_sitelink   \n",
-       "Q27096235                 NaN   \n",
-       "Q48352     wikipedia_sitelink   \n",
-       "Q83307     wikipedia_sitelink   \n",
-       "\n",
-       "                                                       node2    ignore  \\\n",
-       "node1                                                                    \n",
-       "Q11499147                                                NaN       NaN   \n",
-       "Q1048835     http://en.wikipedia.org/wiki/Political_division       NaN   \n",
-       "Q27096235                                                NaN       NaN   \n",
-       "Q48352            http://en.wikipedia.org/wiki/Head_of_state    5945.0   \n",
-       "Q83307     http://en.wikipedia.org/wiki/Minister_(governm...  243508.0   \n",
-       "\n",
-       "                                                         url  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                                NaN   \n",
-       "Q1048835                                                 NaN   \n",
-       "Q27096235                                                NaN   \n",
-       "Q48352           <http://dbpedia.org/resource/Head_of_state>   \n",
-       "Q83307     <http://dbpedia.org/resource/Minister_(governm...   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q11499147                                             NaN   \n",
-       "Q1048835                                              NaN   \n",
-       "Q27096235                                             NaN   \n",
-       "Q48352     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q83307     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q11499147                                 political activist   \n",
-       "Q1048835                        political territorial entity   \n",
-       "Q27096235                       artificial geographic entity   \n",
-       "Q48352     A head of state (or chief of state) is the pub...   \n",
-       "Q83307     A minister is a politician who heads a ministr...   \n",
-       "\n",
-       "                         node2_labelsDF  \\\n",
-       "node1                                     \n",
-       "Q11499147            political activist   \n",
-       "Q1048835   political territorial entity   \n",
-       "Q27096235  artificial geographic entity   \n",
-       "Q48352                    head of state   \n",
-       "Q83307                         minister   \n",
-       "\n",
-       "                                           abstractFirstSent  \n",
-       "node1                                                         \n",
-       "Q11499147                                 political activist  \n",
-       "Q1048835                        political territorial entity  \n",
-       "Q27096235                       artificial geographic entity  \n",
-       "Q48352     A head of state (or chief of state) is the pub...  \n",
-       "Q83307     A minister is a politician who heads a ministr...  "
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "starting-parcel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "independent-consultancy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.14003443717956543 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "incomplete-dispute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(118, 118, 1.0)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "pregnant-message",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>5.670399</td>\n",
-       "      <td>4.803427</td>\n",
-       "      <td>4.803428</td>\n",
-       "      <td>6.238037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>4.471632</td>\n",
-       "      <td>5.585540</td>\n",
-       "      <td>5.585539</td>\n",
-       "      <td>4.817909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>7.259848</td>\n",
-       "      <td>6.529090</td>\n",
-       "      <td>6.529088</td>\n",
-       "      <td>5.945703</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>4.595971</td>\n",
-       "      <td>4.234194</td>\n",
-       "      <td>4.234195</td>\n",
-       "      <td>3.154826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>5.130080</td>\n",
-       "      <td>4.503454</td>\n",
-       "      <td>4.503455</td>\n",
-       "      <td>4.739718</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \\\n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45       5.670399   \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29       4.471632   \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64       7.259848   \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49       4.595971   \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33       5.130080   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  bertV7EncSim  \n",
-       "0      4.803427      4.803428      6.238037  \n",
-       "1      5.585540      5.585539      4.817909  \n",
-       "2      6.529090      6.529088      5.945703  \n",
-       "3      4.234194      4.234195      3.154826  \n",
-       "4      4.503454      4.503455      4.739718  "
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "broke-oregon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "recent-moment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "varying-prescription",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accessible-serum",
-   "metadata": {},
-   "source": [
-    "# Version 8, 9, 10 - complex, transe, text, class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "japanese-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-82-99910acabed9>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-      "<ipython-input-82-99910acabed9>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-      "<ipython-input-82-99910acabed9>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['text'] = wordsimDF.text * 10\n",
-      "<ipython-input-82-99910acabed9>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['class'] = wordsimDF['class'] * 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-    "wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-    "wordsimDF1['text'] = wordsimDF.text * 10\n",
-    "wordsimDF1['class'] = wordsimDF['class'] * 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "fitting-jamaica",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "educational-phoenix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF1.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "backed-scroll",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1['bertLabelsSim'] /= 10\n",
-    "wordsimDF1['bertV5EncSim'] /= 10\n",
-    "wordsimDF1['bertV6EncSim'] /= 10\n",
-    "wordsimDF1['bertV7EncSim'] /= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "classical-bandwidth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.567040</td>\n",
-       "      <td>0.480343</td>\n",
-       "      <td>0.480343</td>\n",
-       "      <td>0.623804</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.447163</td>\n",
-       "      <td>0.558554</td>\n",
-       "      <td>0.558554</td>\n",
-       "      <td>0.481791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0.725985</td>\n",
-       "      <td>0.652909</td>\n",
-       "      <td>0.652909</td>\n",
-       "      <td>0.594570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.459597</td>\n",
-       "      <td>0.423419</td>\n",
-       "      <td>0.423420</td>\n",
-       "      <td>0.315483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>0.513008</td>\n",
-       "      <td>0.450345</td>\n",
-       "      <td>0.450346</td>\n",
-       "      <td>0.473972</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \\\n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45       0.567040   \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29       0.447163   \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64       0.725985   \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49       0.459597   \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33       0.513008   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  bertV7EncSim  \n",
-       "0      0.480343      0.480343      0.623804  \n",
-       "1      0.558554      0.558554      0.481791  \n",
-       "2      0.652909      0.652909      0.594570  \n",
-       "3      0.423419      0.423420      0.315483  \n",
-       "4      0.450345      0.450346      0.473972  "
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "contained-heavy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1.to_csv('../data/SimIP_new_OP.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "amazing-expense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.052735</td>\n",
-       "      <td>6.361162</td>\n",
-       "      <td>5.509827</td>\n",
-       "      <td>5.600105</td>\n",
-       "      <td>3.272164</td>\n",
-       "      <td>3.733567</td>\n",
-       "      <td>4.655997</td>\n",
-       "      <td>4.464247</td>\n",
-       "      <td>4.286958</td>\n",
-       "      <td>3.438595</td>\n",
-       "      <td>5.578697</td>\n",
-       "      <td>1.937397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.044971</td>\n",
-       "      <td>1.601204</td>\n",
-       "      <td>2.015174</td>\n",
-       "      <td>1.995942</td>\n",
-       "      <td>2.736918</td>\n",
-       "      <td>2.912649</td>\n",
-       "      <td>2.279742</td>\n",
-       "      <td>2.077533</td>\n",
-       "      <td>1.645163</td>\n",
-       "      <td>2.033467</td>\n",
-       "      <td>2.021548</td>\n",
-       "      <td>2.753170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.312500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.483480</td>\n",
-       "      <td>-0.526745</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1.436633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.375000</td>\n",
-       "      <td>5.304373</td>\n",
-       "      <td>4.548204</td>\n",
-       "      <td>4.739625</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.015642</td>\n",
-       "      <td>3.120310</td>\n",
-       "      <td>3.317798</td>\n",
-       "      <td>2.151662</td>\n",
-       "      <td>4.626498</td>\n",
-       "      <td>0.145023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.937500</td>\n",
-       "      <td>6.461515</td>\n",
-       "      <td>5.597513</td>\n",
-       "      <td>5.787425</td>\n",
-       "      <td>3.463857</td>\n",
-       "      <td>4.437084</td>\n",
-       "      <td>4.774273</td>\n",
-       "      <td>4.206756</td>\n",
-       "      <td>4.171686</td>\n",
-       "      <td>3.240109</td>\n",
-       "      <td>5.767745</td>\n",
-       "      <td>0.639914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5.375000</td>\n",
-       "      <td>7.399760</td>\n",
-       "      <td>6.735863</td>\n",
-       "      <td>6.744476</td>\n",
-       "      <td>5.183337</td>\n",
-       "      <td>5.999169</td>\n",
-       "      <td>6.218609</td>\n",
-       "      <td>5.675579</td>\n",
-       "      <td>4.996030</td>\n",
-       "      <td>4.731990</td>\n",
-       "      <td>6.812977</td>\n",
-       "      <td>2.350565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000001</td>\n",
-       "      <td>10.000001</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Human (Mean)  bertLabelsSim     amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "count    349.000000     349.000000  349.000000    349.000000    349.000000   \n",
-       "mean       4.052735       6.361162    5.509827      5.600105      3.272164   \n",
-       "std        2.044971       1.601204    2.015174      1.995942      2.736918   \n",
-       "min        0.312500       0.000000    0.000000      0.000000      0.000000   \n",
-       "25%        2.375000       5.304373    4.548204      4.739625      0.000000   \n",
-       "50%        3.937500       6.461515    5.597513      5.787425      3.463857   \n",
-       "75%        5.375000       7.399760    6.735863      6.744476      5.183337   \n",
-       "max       10.000000      10.000002   10.000000     10.000000     10.000002   \n",
-       "\n",
-       "       bertV5EncSim  bertV6EncSim  bertV7EncSim     complex      transe  \\\n",
-       "count    349.000000    349.000000    349.000000  349.000000  349.000000   \n",
-       "mean       3.733567      4.655997      4.464247    4.286958    3.438595   \n",
-       "std        2.912649      2.279742      2.077533    1.645163    2.033467   \n",
-       "min        0.000000     -0.483480     -0.526745    0.000000   -1.436633   \n",
-       "25%        0.000000      3.015642      3.120310    3.317798    2.151662   \n",
-       "50%        4.437084      4.774273      4.206756    4.171686    3.240109   \n",
-       "75%        5.999169      6.218609      5.675579    4.996030    4.731990   \n",
-       "max       10.000001     10.000001     10.000002   10.000000   10.000000   \n",
-       "\n",
-       "             text       class  \n",
-       "count  349.000000  349.000000  \n",
-       "mean     5.578697    1.937397  \n",
-       "std      2.021548    2.753170  \n",
-       "min      0.000000    0.000000  \n",
-       "25%      4.626498    0.145023  \n",
-       "50%      5.767745    0.639914  \n",
-       "75%      6.812977    2.350565  \n",
-       "max     10.000000   10.000000  "
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "headed-reality",
-   "metadata": {},
-   "source": [
-    "# Agreement Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "typical-highland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "versionNames = ['bertLabelsSim', 'amanSim', 'wikiV3EncSim', 'bertV4EncSim', 'bertV5EncSim', 'bertV6EncSim', 'bertV7EncSim', 'complex', 'transe', 'text', 'class']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "irish-yesterday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAM = []\n",
-    "spearmanAM = []\n",
-    "for ver in versionNames:\n",
-    "    kAMTemp = []\n",
-    "    sAMTemp = []\n",
-    "    for ver2 in versionNames:\n",
-    "        kAMTemp.append(stats.kendalltau(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "        sAMTemp.append(stats.spearmanr(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "    kendallAM.append(kAMTemp)\n",
-    "    spearmanAM.append(sAMTemp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "massive-optimum",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF = pd.DataFrame(kendallAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "higher-shirt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
-       "      <th>Version 2</th>\n",
-       "      <th>Version 3</th>\n",
-       "      <th>Version 4</th>\n",
-       "      <th>Version 5</th>\n",
-       "      <th>Version 6</th>\n",
-       "      <th>Version 7</th>\n",
-       "      <th>Version 8</th>\n",
-       "      <th>Version 9</th>\n",
-       "      <th>Version 10</th>\n",
-       "      <th>Version 11</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Version 1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.174948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 2</th>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.238586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 3</th>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.235619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 4</th>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.259318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 5</th>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.248043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 6</th>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.220891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 7</th>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.241190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.334855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>0.255377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 10</th>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.232037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 11</th>\n",
-       "      <td>0.174948</td>\n",
-       "      <td>0.238586</td>\n",
-       "      <td>0.235619</td>\n",
-       "      <td>0.259318</td>\n",
-       "      <td>0.248043</td>\n",
-       "      <td>0.220891</td>\n",
-       "      <td>0.241190</td>\n",
-       "      <td>0.334855</td>\n",
-       "      <td>0.255377</td>\n",
-       "      <td>0.232037</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
-       "Version 1    1.000000   0.412089   0.383523   0.160567   0.146498   0.287018   \n",
-       "Version 2    0.412089   1.000000   0.838947   0.296893   0.266232   0.369277   \n",
-       "Version 3    0.383523   0.838947   1.000000   0.275386   0.250581   0.351252   \n",
-       "Version 4    0.160567   0.296893   0.275386   1.000000   0.820504   0.611740   \n",
-       "Version 5    0.146498   0.266232   0.250581   0.820504   1.000000   0.782150   \n",
-       "Version 6    0.287018   0.369277   0.351252   0.611740   0.782150   1.000000   \n",
-       "Version 7    0.383673   0.458566   0.429853   0.619886   0.449513   0.650565   \n",
-       "Version 8    0.208911   0.309217   0.304272   0.229670   0.246373   0.265199   \n",
-       "Version 9    0.209933   0.247839   0.249097   0.135532   0.143346   0.201692   \n",
-       "Version 10   0.400400   0.912377   0.839894   0.299409   0.269472   0.368089   \n",
-       "Version 11   0.174948   0.238586   0.235619   0.259318   0.248043   0.220891   \n",
-       "\n",
-       "            Version 7  Version 8  Version 9  Version 10  Version 11  \n",
-       "Version 1    0.383673   0.208911   0.209933    0.400400    0.174948  \n",
-       "Version 2    0.458566   0.309217   0.247839    0.912377    0.238586  \n",
-       "Version 3    0.429853   0.304272   0.249097    0.839894    0.235619  \n",
-       "Version 4    0.619886   0.229670   0.135532    0.299409    0.259318  \n",
-       "Version 5    0.449513   0.246373   0.143346    0.269472    0.248043  \n",
-       "Version 6    0.650565   0.265199   0.201692    0.368089    0.220891  \n",
-       "Version 7    1.000000   0.260021   0.239819    0.453371    0.241190  \n",
-       "Version 8    0.260021   1.000000   0.447656    0.309583    0.334855  \n",
-       "Version 9    0.239819   0.447656   1.000000    0.244593    0.255377  \n",
-       "Version 10   0.453371   0.309583   0.244593    1.000000    0.232037  \n",
-       "Version 11   0.241190   0.334855   0.255377    0.232037    1.000000  "
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kendallAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "distant-level",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spearmanAMDF = pd.DataFrame(spearmanAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "skilled-battery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
-       "      <th>Version 2</th>\n",
-       "      <th>Version 3</th>\n",
-       "      <th>Version 4</th>\n",
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-       "      <th>Version 1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.576425</td>\n",
-       "      <td>0.539416</td>\n",
-       "      <td>0.213744</td>\n",
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-       "      <td>0.959002</td>\n",
-       "      <td>0.392378</td>\n",
-       "      <td>0.350908</td>\n",
-       "      <td>0.515853</td>\n",
-       "      <td>0.628626</td>\n",
-       "      <td>0.440326</td>\n",
-       "      <td>0.352366</td>\n",
-       "      <td>0.985972</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 3</th>\n",
-       "      <td>0.539416</td>\n",
-       "      <td>0.959002</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.371106</td>\n",
-       "      <td>0.337087</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.596281</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.352982</td>\n",
-       "      <td>0.959131</td>\n",
-       "      <td>0.336037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 4</th>\n",
-       "      <td>0.213744</td>\n",
-       "      <td>0.392378</td>\n",
-       "      <td>0.371106</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.946527</td>\n",
-       "      <td>0.733577</td>\n",
-       "      <td>0.628222</td>\n",
-       "      <td>0.325008</td>\n",
-       "      <td>0.189124</td>\n",
-       "      <td>0.399380</td>\n",
-       "      <td>0.355801</td>\n",
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-       "      <th>Version 5</th>\n",
-       "      <td>0.192074</td>\n",
-       "      <td>0.350908</td>\n",
-       "      <td>0.337087</td>\n",
-       "      <td>0.946527</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.356645</td>\n",
-       "      <td>0.343931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 6</th>\n",
-       "      <td>0.400162</td>\n",
-       "      <td>0.515853</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.733577</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.298756</td>\n",
-       "      <td>0.513084</td>\n",
-       "      <td>0.321041</td>\n",
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-       "    <tr>\n",
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-       "      <td>0.532950</td>\n",
-       "      <td>0.628626</td>\n",
-       "      <td>0.596281</td>\n",
-       "      <td>0.628222</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>0.347957</td>\n",
-       "      <td>0.624048</td>\n",
-       "      <td>0.345162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.301924</td>\n",
-       "      <td>0.440326</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.325008</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.619056</td>\n",
-       "      <td>0.439102</td>\n",
-       "      <td>0.477997</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
-       "      <td>0.305490</td>\n",
-       "      <td>0.352366</td>\n",
-       "      <td>0.352982</td>\n",
-       "      <td>0.189124</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.298756</td>\n",
-       "      <td>0.347957</td>\n",
-       "      <td>0.619056</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>0.364509</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 10</th>\n",
-       "      <td>0.563628</td>\n",
-       "      <td>0.985972</td>\n",
-       "      <td>0.959131</td>\n",
-       "      <td>0.399380</td>\n",
-       "      <td>0.356645</td>\n",
-       "      <td>0.513084</td>\n",
-       "      <td>0.624048</td>\n",
-       "      <td>0.439102</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.333301</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 11</th>\n",
-       "      <td>0.253590</td>\n",
-       "      <td>0.339260</td>\n",
-       "      <td>0.336037</td>\n",
-       "      <td>0.355801</td>\n",
-       "      <td>0.343931</td>\n",
-       "      <td>0.321041</td>\n",
-       "      <td>0.345162</td>\n",
-       "      <td>0.477997</td>\n",
-       "      <td>0.364509</td>\n",
-       "      <td>0.333301</td>\n",
-       "      <td>1.000000</td>\n",
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-       "</div>"
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-      "text/plain": [
-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
-       "Version 1    1.000000   0.576425   0.539416   0.213744   0.192074   0.400162   \n",
-       "Version 2    0.576425   1.000000   0.959002   0.392378   0.350908   0.515853   \n",
-       "Version 3    0.539416   0.959002   1.000000   0.371106   0.337087   0.493746   \n",
-       "Version 4    0.213744   0.392378   0.371106   1.000000   0.946527   0.733577   \n",
-       "Version 5    0.192074   0.350908   0.337087   0.946527   1.000000   0.793177   \n",
-       "Version 6    0.400162   0.515853   0.493746   0.733577   0.793177   1.000000   \n",
-       "Version 7    0.532950   0.628626   0.596281   0.628222   0.556256   0.829959   \n",
-       "Version 8    0.301924   0.440326   0.430792   0.325008   0.343765   0.381097   \n",
-       "Version 9    0.305490   0.352366   0.352982   0.189124   0.203396   0.298756   \n",
-       "Version 10   0.563628   0.985972   0.959131   0.399380   0.356645   0.513084   \n",
-       "Version 11   0.253590   0.339260   0.336037   0.355801   0.343931   0.321041   \n",
-       "\n",
-       "            Version 7  Version 8  Version 9  Version 10  Version 11  \n",
-       "Version 1    0.532950   0.301924   0.305490    0.563628    0.253590  \n",
-       "Version 2    0.628626   0.440326   0.352366    0.985972    0.339260  \n",
-       "Version 3    0.596281   0.430792   0.352982    0.959131    0.336037  \n",
-       "Version 4    0.628222   0.325008   0.189124    0.399380    0.355801  \n",
-       "Version 5    0.556256   0.343765   0.203396    0.356645    0.343931  \n",
-       "Version 6    0.829959   0.381097   0.298756    0.513084    0.321041  \n",
-       "Version 7    1.000000   0.373446   0.347957    0.624048    0.345162  \n",
-       "Version 8    0.373446   1.000000   0.619056    0.439102    0.477997  \n",
-       "Version 9    0.347957   0.619056   1.000000    0.346778    0.364509  \n",
-       "Version 10   0.624048   0.439102   0.346778    1.000000    0.333301  \n",
-       "Version 11   0.345162   0.477997   0.364509    0.333301    1.000000  "
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spearmanAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "indoor-coordinator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF.to_csv('../data/kendallAMDF.csv')\n",
-    "spearmanAMDF.to_csv('../data/spearmanAMDF.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "innocent-prerequisite",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "active-leisure",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "boolean-impossible",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suitable-brazil",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "desirable-collect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for ver in versionNames:\n",
-    "    wordsimDF[ver] = wordsimDF1[ver]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "flexible-fifth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
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-       "      <th>bertV4EncSim</th>\n",
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-       "      <th>bertV6EncSim</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>3.611154</td>\n",
-       "      <td>4.466324</td>\n",
-       "      <td>4.856529</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.021635</td>\n",
-       "      <td>2.743883</td>\n",
-       "      <td>4.793135</td>\n",
-       "      <td>0.058511</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.944618</td>\n",
-       "      <td>4.292894</td>\n",
-       "      <td>6.267841</td>\n",
-       "      <td>3.936571</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>3.248249</td>\n",
-       "      <td>3.790767</td>\n",
-       "      <td>6.186442</td>\n",
-       "      <td>2.088125</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.612144</td>\n",
-       "      <td>3.603834</td>\n",
-       "      <td>8.638458</td>\n",
-       "      <td>8.886211</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  bertLabelsSim  \\\n",
-       "0        5.5360  3.611154  4.466324  4.856529  0.000000       6.809672   \n",
-       "1        4.1250  3.021635  2.743883  4.793135  0.058511       4.554877   \n",
-       "2        6.6250  6.944618  4.292894  6.267841  3.936571       7.643815   \n",
-       "3        2.0625  3.248249  3.790767  6.186442  2.088125       7.194899   \n",
-       "4        7.1875  3.612144  3.603834  8.638458  8.886211       8.137879   \n",
-       "\n",
-       "    amanSim  wikiV3EncSim  bertV4EncSim  bertV5EncSim  bertV6EncSim  \\\n",
-       "0  4.949738      5.145601      3.770548      4.268633      4.268633   \n",
-       "1  4.818231      3.987047      4.168040      3.399217      3.399217   \n",
-       "2  6.407051      5.669631      4.997217      6.865782      6.865782   \n",
-       "3  6.186497      6.186497      0.000000      0.000000      2.961755   \n",
-       "4  8.556664      9.098408      0.000000      0.000000      3.408112   \n",
-       "\n",
-       "   bertV7EncSim  \n",
-       "0      3.770548  \n",
-       "1      4.168040  \n",
-       "2      4.997217  \n",
-       "3      5.296330  \n",
-       "4      5.621890  "
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "ambient-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF.to_csv('../data/wordsim_updated.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "tested-screening",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-identifier",
-   "metadata": {},
-   "source": [
-    "# Part 2 - Pedro's updated file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "casual-research",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDFNew = pd.read_csv('../data/SimIP_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "capable-brisbane",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(set(wordsimDFNew['g_qnode'].to_list() + wordsimDFNew['p_qnode'].to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valid-agriculture",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "worst-posting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-asbestos",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "democratic-assignment",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.580217361450195 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-13-d93d87c23bc8>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-      "<ipython-input-13-d93d87c23bc8>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "extraordinary-motivation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "recovered-assignment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.25719783185089823, pvalue=9.11953585838357e-13)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "southern-berry",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.37036443456409784, pvalue=8.677074016866423e-13)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "public-arizona",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "breathing-bennett",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                     school holiday  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "communist-destination",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "cardiac-coral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "original-combat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "fossil-blame",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.902165412902832 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-66-ef38b58ef6d6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-      "<ipython-input-66-ef38b58ef6d6>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "touched-response",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "senior-chase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  \n",
-       "0      4.268633      4.268633  \n",
-       "1      3.399217      3.399217  \n",
-       "2      6.865782      6.865782  \n",
-       "3      0.000000      2.961755  \n",
-       "4      0.000000      3.408112  "
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "considerable-assignment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "cutting-floor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "centered-chosen",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unable-humor",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quarterly-amendment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "latest-fountain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "experienced-uganda",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \\\n",
-       "node1                                                                         \n",
-       "Q1514190                                     school holiday  school holiday   \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood   \n",
-       "Q1368     Money is any item or verifiable record that is...           money   \n",
-       "Q872      Radio is the technology of signaling and commu...           radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q1514190                                     school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  "
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "adequate-stocks",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['g_qnode'] in encodingsMap and row['p_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['g_qnode']]], [encodingsMap[row['p_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "static-partition",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48781871795654297 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-76-ef0e3d1b2360>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-      "<ipython-input-76-ef0e3d1b2360>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "approved-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "banner-techno",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  bertV7EncSim  \n",
-       "0      4.268633      4.268633      3.770548  \n",
-       "1      3.399217      3.399217      4.168040  \n",
-       "2      6.865782      6.865782      4.997217  \n",
-       "3      0.000000      2.961755      5.296330  \n",
-       "4      0.000000      3.408112      5.621890  "
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "aggregate-lighter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "functioning-donor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3793557735858387, pvalue=5.886665529373508e-26)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "adjusted-commission",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5330950015966213, pvalue=5.11634460488722e-27)"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "presidential-penetration",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-reward",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outer-symposium",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "399px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Similarity Work-Copy2.ipynb b/Similarity Work-Copy2.ipynb
deleted file mode 100644
index 434d37a..0000000
--- a/Similarity Work-Copy2.ipynb	
+++ /dev/null
@@ -1,10065 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "protecting-craft",
-   "metadata": {},
-   "source": [
-    "Downloaded dataset from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "indirect-kingdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "import pandas as pd\n",
-    "from time import time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "elder-russian",
-   "metadata": {},
-   "source": [
-    "# Process and format DBPedia dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "characteristic-orientation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3db8345a6c34dbe8f54427985216b25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cnt = 0\n",
-    "\n",
-    "p1s = []\n",
-    "p11s = []\n",
-    "p2s = []\n",
-    "lines = []\n",
-    "with open(\"../data/short-abstracts_lang=en.ttl\", 'r', encoding='utf-8') as f:\n",
-    "    for line in tqdm(f):\n",
-    "        p1 = line[:line.find(\" \")]\n",
-    "#         p11 = p1.split(\"/\")[-1][:-1]\n",
-    "        p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "        line = line[line.find(\" \")+1:]\n",
-    "        p2 = line[:line.find(\" \")]\n",
-    "        line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "#         print(p1, p2, line)\n",
-    "        p1s.append(p1)\n",
-    "        p11s.append(p11)\n",
-    "        p2s.append(p2)\n",
-    "        lines.append(line)\n",
-    "#         except UnicodeDecodeError:\n",
-    "#             cnt += 1\n",
-    "# print(\"Encoding error:\", cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "prostate-master",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "df1.to_csv('../data/short-abstracts_lang=en.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "occupational-sheriff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "environmental-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "dirty-sample",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df1.set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "defensive-switch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Animalia_(book)</th>\n",
-       "      <td>1</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Animalia_(book)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Animalia is an illustrated children's book by ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Agricultural_science</th>\n",
-       "      <td>2</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Agricultural_scie...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Agricultural science is a broad multidisciplin...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albedo</th>\n",
-       "      <td>3</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Albedo&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Albedo () (Latin: albedo, meaning 'whiteness')...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alain_Connes</th>\n",
-       "      <td>4</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Alain_Connes&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Alain Connes (French: [alɛ̃ kɔn]; born 1 April...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      ignore  \\\n",
-       "node1                          \n",
-       "NaN                        0   \n",
-       "Animalia_(book)            1   \n",
-       "Agricultural_science       2   \n",
-       "Albedo                     3   \n",
-       "Alain_Connes               4   \n",
-       "\n",
-       "                                                                    url  \\\n",
-       "node1                                                                     \n",
-       "NaN                                                                   #   \n",
-       "Animalia_(book)           <http://dbpedia.org/resource/Animalia_(book)>   \n",
-       "Agricultural_science  <http://dbpedia.org/resource/Agricultural_scie...   \n",
-       "Albedo                             <http://dbpedia.org/resource/Albedo>   \n",
-       "Alain_Connes                 <http://dbpedia.org/resource/Alain_Connes>   \n",
-       "\n",
-       "                                                             ignore2  \\\n",
-       "node1                                                                  \n",
-       "NaN                                                          started   \n",
-       "Animalia_(book)       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Agricultural_science  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Albedo                <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Alain_Connes          <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                               abstract  \n",
-       "node1                                                                    \n",
-       "NaN                                                                 NaN  \n",
-       "Animalia_(book)       Animalia is an illustrated children's book by ...  \n",
-       "Agricultural_science  Agricultural science is a broad multidisciplin...  \n",
-       "Albedo                Albedo () (Latin: albedo, meaning 'whiteness')...  \n",
-       "Alain_Connes          Alain Connes (French: [alɛ̃ kɔn]; born 1 April...  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "specified-emission",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore url  ignore2 abstract\n",
-       "node1                              \n",
-       "NaN         0   #  started      NaN"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "streaming-watch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "clean-brook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>23399</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/NaN&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>In computing, NaN, standing for Not a Number, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore                                url  \\\n",
-       "node1                                              \n",
-       "NaN         0                                  #   \n",
-       "NaN     23399  <http://dbpedia.org/resource/NaN>   \n",
-       "\n",
-       "                                              ignore2  \\\n",
-       "node1                                                   \n",
-       "NaN                                           started   \n",
-       "NaN    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                abstract  \n",
-       "node1                                                     \n",
-       "NaN                                                  NaN  \n",
-       "NaN    In computing, NaN, standing for Not a Number, ...  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "residential-bunch",
-   "metadata": {},
-   "source": [
-    "There are many duplicate element names with different abstracts. For example, 390 corresponds to year 390, ibm 390 systemss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "whole-retirement",
-   "metadata": {},
-   "source": [
-    "# Wikidata Sitelinks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "usual-albuquerque",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "classical-tamil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "undefined-gospel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "backed-criminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "armed-gardening",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Universe</th>\n",
-       "      <td>Q1-wikipedia_sitelink-5e459a-0</td>\n",
-       "      <td>Q1</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Universe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Boston</th>\n",
-       "      <td>Q100-wikipedia_sitelink-c612f2-0</td>\n",
-       "      <td>Q100</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Boston</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gabon</th>\n",
-       "      <td>Q1000-wikipedia_sitelink-b7f1ee-0</td>\n",
-       "      <td>Q1000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Gabon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dutch_Wikipedia</th>\n",
-       "      <td>Q10000-wikipedia_sitelink-16adbd-0</td>\n",
-       "      <td>Q10000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dutch_Wikipedia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cadier_en_Keer</th>\n",
-       "      <td>Q100000-wikipedia_sitelink-d81a0a-0</td>\n",
-       "      <td>Q100000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Cadier_en_Keer</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  id    node1  \\\n",
-       "trimmedNode2                                                    \n",
-       "Universe              Q1-wikipedia_sitelink-5e459a-0       Q1   \n",
-       "Boston              Q100-wikipedia_sitelink-c612f2-0     Q100   \n",
-       "Gabon              Q1000-wikipedia_sitelink-b7f1ee-0    Q1000   \n",
-       "Dutch_Wikipedia   Q10000-wikipedia_sitelink-16adbd-0   Q10000   \n",
-       "Cadier_en_Keer   Q100000-wikipedia_sitelink-d81a0a-0  Q100000   \n",
-       "\n",
-       "                              label  \\\n",
-       "trimmedNode2                          \n",
-       "Universe         wikipedia_sitelink   \n",
-       "Boston           wikipedia_sitelink   \n",
-       "Gabon            wikipedia_sitelink   \n",
-       "Dutch_Wikipedia  wikipedia_sitelink   \n",
-       "Cadier_en_Keer   wikipedia_sitelink   \n",
-       "\n",
-       "                                                        node2  \n",
-       "trimmedNode2                                                   \n",
-       "Universe                http://en.wikipedia.org/wiki/Universe  \n",
-       "Boston                    http://en.wikipedia.org/wiki/Boston  \n",
-       "Gabon                      http://en.wikipedia.org/wiki/Gabon  \n",
-       "Dutch_Wikipedia  http://en.wikipedia.org/wiki/Dutch_Wikipedia  \n",
-       "Cadier_en_Keer    http://en.wikipedia.org/wiki/Cadier_en_Keer  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sitelinksDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "thousand-marriage",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "alpha-sport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A-2_(Michigan_county_highway)</th>\n",
-       "      <td>Q100149995-wikipedia_sitelink-029972-0</td>\n",
-       "      <td>Q100149995</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/A-2_(Michigan_cou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A-2_(Michigan_county_highway)</th>\n",
-       "      <td>Q100150013-wikipedia_sitelink-14ff5e-0</td>\n",
-       "      <td>Q100150013</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231096-wikipedia_sitelink-832b42-0</td>\n",
-       "      <td>Q100231096</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Communis...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231099-wikipedia_sitelink-f5327d-0</td>\n",
-       "      <td>Q100231099</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Movement...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231125-wikipedia_sitelink-b3524d-0</td>\n",
-       "      <td>Q100231125</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Serbian_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_47</th>\n",
-       "      <td>Q999979-wikipedia_sitelink-069c8c-0</td>\n",
-       "      <td>Q999979</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_12</th>\n",
-       "      <td>Q26346274-wikipedia_sitelink-bd20c7-0</td>\n",
-       "      <td>Q26346274</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_12</th>\n",
-       "      <td>Q999984-wikipedia_sitelink-d64e95-0</td>\n",
-       "      <td>Q999984</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_13</th>\n",
-       "      <td>Q26346384-wikipedia_sitelink-61fe02-0</td>\n",
-       "      <td>Q26346384</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_13</th>\n",
-       "      <td>Q999992-wikipedia_sitelink-025e0c-0</td>\n",
-       "      <td>Q999992</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>22572010 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                   id  \\\n",
-       "trimmedNode2                                                            \n",
-       "A-2_(Michigan_county_highway)  Q100149995-wikipedia_sitelink-029972-0   \n",
-       "A-2_(Michigan_county_highway)  Q100150013-wikipedia_sitelink-14ff5e-0   \n",
-       "color                          Q100231096-wikipedia_sitelink-832b42-0   \n",
-       "color                          Q100231099-wikipedia_sitelink-f5327d-0   \n",
-       "color                          Q100231125-wikipedia_sitelink-b3524d-0   \n",
-       "...                                                               ...   \n",
-       "New_Hampshire_Route_47            Q999979-wikipedia_sitelink-069c8c-0   \n",
-       "New_Hampshire_Route_12          Q26346274-wikipedia_sitelink-bd20c7-0   \n",
-       "New_Hampshire_Route_12            Q999984-wikipedia_sitelink-d64e95-0   \n",
-       "New_Hampshire_Route_13          Q26346384-wikipedia_sitelink-61fe02-0   \n",
-       "New_Hampshire_Route_13            Q999992-wikipedia_sitelink-025e0c-0   \n",
-       "\n",
-       "                                    node1               label  \\\n",
-       "trimmedNode2                                                    \n",
-       "A-2_(Michigan_county_highway)  Q100149995  wikipedia_sitelink   \n",
-       "A-2_(Michigan_county_highway)  Q100150013  wikipedia_sitelink   \n",
-       "color                          Q100231096  wikipedia_sitelink   \n",
-       "color                          Q100231099  wikipedia_sitelink   \n",
-       "color                          Q100231125  wikipedia_sitelink   \n",
-       "...                                   ...                 ...   \n",
-       "New_Hampshire_Route_47            Q999979  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_12          Q26346274  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_12            Q999984  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_13          Q26346384  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_13            Q999992  wikipedia_sitelink   \n",
-       "\n",
-       "                                                                           node2  \n",
-       "trimmedNode2                                                                      \n",
-       "A-2_(Michigan_county_highway)  http://en.wikipedia.org/wiki/A-2_(Michigan_cou...  \n",
-       "A-2_(Michigan_county_highway)  http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Communis...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Movement...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Serbian_...  \n",
-       "...                                                                          ...  \n",
-       "New_Hampshire_Route_47         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "New_Hampshire_Route_12         http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "New_Hampshire_Route_12         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "New_Hampshire_Route_13         http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "New_Hampshire_Route_13         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "\n",
-       "[22572010 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "editorial-cache",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "naval-morrison",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join DBPedia file with sitelinks - File X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "incorrect-beach",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF = df1.join(sitelinksDF2).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "answering-rhythm",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "peripheral-edinburgh",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "len(abstractsDF), abstractsDF.index.nunique(), abstractsDF.node1.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "first-encoding",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "print(f\"From {len(abstractsDF)} Qnodes, there are {abstractsDF.node2.isna().sum()} dbpedia statements which do not have a sitelink i.e {len(abstractsDF) - abstractsDF.node2.isna().sum()} have a sitelink\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modular-spirituality",
-   "metadata": {},
-   "source": [
-    "# Similarity file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "wired-hayes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF = pd.read_csv(\"../data/SimIP_new_max.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "referenced-newport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>G_token</th>\n",
-       "      <th>P_token</th>\n",
-       "      <th>G_entity</th>\n",
-       "      <th>G_qnode</th>\n",
-       "      <th>P_entity</th>\n",
-       "      <th>P_qnode</th>\n",
-       "      <th>token_mismatch_type</th>\n",
-       "      <th>P_event_confidence</th>\n",
-       "      <th>Original_Text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.spokesperson</td>\n",
-       "      <td>spokesperson_Q17221</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.minister</td>\n",
-       "      <td>minister_Q83307</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>0.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition</td>\n",
-       "      <td>professional_Q702269 | firefighter_Q107711 | s...</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.008333</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.militarypersonnel</td>\n",
-       "      <td>military personnel_Q47064</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.401134</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.politician</td>\n",
-       "      <td>politician_Q82955</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.099668</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index         G_token         P_token                         G_entity  \\\n",
-       "0      1           Putin           Putin  per.politician.headofgovernment   \n",
-       "1      2           Putin           Putin  per.politician.headofgovernment   \n",
-       "2      3           Putin           Putin  per.politician.headofgovernment   \n",
-       "3      4  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "4      5  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "\n",
-       "                                             G_qnode  \\\n",
-       "0  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "1  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "2  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "3  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "4  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "\n",
-       "                                P_entity  \\\n",
-       "0  per.professionalposition.spokesperson   \n",
-       "1      per.professionalposition.minister   \n",
-       "2               per.professionalposition   \n",
-       "3                  per.militarypersonnel   \n",
-       "4                         per.politician   \n",
-       "\n",
-       "                                             P_qnode token_mismatch_type  \\\n",
-       "0                                spokesperson_Q17221                same   \n",
-       "1                                    minister_Q83307                same   \n",
-       "2  professional_Q702269 | firefighter_Q107711 | s...                same   \n",
-       "3                          military personnel_Q47064                same   \n",
-       "4                                  politician_Q82955                same   \n",
-       "\n",
-       "   P_event_confidence                                      Original_Text  \\\n",
-       "0            0.016665  Why not be more accurate and state that those ...   \n",
-       "1            0.016665  Why not be more accurate and state that those ...   \n",
-       "2            0.008333  Why not be more accurate and state that those ...   \n",
-       "3            0.401134  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "4            0.099668  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "\n",
-       "  complex  transe  text  class  node2vec-v0  H_3x6  H_5x8  \n",
-       "0       x     NaN   NaN    NaN          NaN   0.52   0.42  \n",
-       "1       x     NaN   NaN    NaN          NaN   0.79   0.73  \n",
-       "2       x     NaN   NaN    NaN          NaN   0.54   0.42  \n",
-       "3       x     NaN   NaN    NaN          NaN   0.70   0.44  \n",
-       "4       x     NaN   NaN    NaN          NaN   0.61   0.64  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "united-contribution",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "listTemp1 = wordsimDF.G_qnode.apply(lambda p: [pT.strip().split('_')[-1] for pT in p.split('|')]).to_list()\n",
-    "listTemp2 = wordsimDF.P_qnode.apply(lambda p: [pT.strip().split('_')[-1] for pT in p.split('|')]).to_list()\n",
-    "wordsimQNodesList = set([item for sublist in listTemp1 for item in sublist] + [item for sublist in listTemp2 for item in sublist])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "homeless-ownership",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Q1048835',\n",
-       " 'Q10553309',\n",
-       " 'Q107711',\n",
-       " 'Q11499147',\n",
-       " 'Q1251441',\n",
-       " 'Q12909644',\n",
-       " 'Q1414937',\n",
-       " 'Q16334295',\n",
-       " 'Q17221',\n",
-       " 'Q174782',\n",
-       " 'Q189290',\n",
-       " 'Q1930187',\n",
-       " 'Q201948',\n",
-       " 'Q215627',\n",
-       " 'Q22667',\n",
-       " 'Q2285706',\n",
-       " 'Q245065',\n",
-       " 'Q27096235',\n",
-       " 'Q2855502',\n",
-       " 'Q327333',\n",
-       " 'Q47064',\n",
-       " 'Q48352',\n",
-       " 'Q532',\n",
-       " 'Q702269',\n",
-       " 'Q702492',\n",
-       " 'Q7188',\n",
-       " 'Q7278',\n",
-       " 'Q82794',\n",
-       " 'Q82955',\n",
-       " 'Q83307',\n",
-       " 'Q9352089'}"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesList"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "billion-syndrome",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# print(f\"From the similarity file having {len(wordsimDF)} records, we have list1 with {len(set(wordsimDF.G_qnode.to_list()))} unique Qnodes and list2 with {len(set(wordsimDF.P_qnode.to_list()))} unique Qnodes. Taking their union, we get {len(set(wordsimDF.G_qnode.to_list() + wordsimDF.P_qnode.to_list()))} unique Qnodes and by taking their intersection, we get {len(set(wordsimDF.G_qnode.to_list()).intersection(set(wordsimDF.P_qnode.to_list())))} unique Qnodes\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "affecting-people",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListDF1 = pd.DataFrame({'node1': list(wordsimQNodesList)}).set_index('node1')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-document",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Filtering File X Qnodes using similarity file Qnodes list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "universal-romance",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF1 = abstractsDF[abstractsDF.node1.apply(lambda p:p in wordsimQNodesList)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "secure-default",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4002</th>\n",
-       "      <td>100</td>\n",
-       "      <td>159716</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/100&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>100 or one hundred (Roman numeral: C) is the n...</td>\n",
-       "      <td>Q37413-wikipedia_sitelink-c0dbd3-0</td>\n",
-       "      <td>Q37413</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>306492</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1074621</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/5&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>5 (five) is a number, numeral, and glyph. It i...</td>\n",
-       "      <td>Q203-wikipedia_sitelink-c8e811-0</td>\n",
-       "      <td>Q203</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>312986</th>\n",
-       "      <td>7</td>\n",
-       "      <td>103187</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/7&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>7 (seven) is the natural number following 6 an...</td>\n",
-       "      <td>Q23350-wikipedia_sitelink-6dd502-0</td>\n",
-       "      <td>Q23350</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363000</th>\n",
-       "      <td>Abuse</td>\n",
-       "      <td>165447</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Abuse&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Abuse is the improper usage or treatment of a ...</td>\n",
-       "      <td>Q600571-wikipedia_sitelink-5719bc-0</td>\n",
-       "      <td>Q600571</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Abuse</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364696</th>\n",
-       "      <td>Academic_discipline</td>\n",
-       "      <td>1830099</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Academic_discipline&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An academic discipline or academic field is a ...</td>\n",
-       "      <td>Q11862829-wikipedia_sitelink-cdefe0-0</td>\n",
-       "      <td>Q11862829</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Academic_discipline</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      index   ignore  \\\n",
-       "4002                    100   159716   \n",
-       "306492                    5  1074621   \n",
-       "312986                    7   103187   \n",
-       "363000                Abuse   165447   \n",
-       "364696  Academic_discipline  1830099   \n",
-       "\n",
-       "                                                      url  \\\n",
-       "4002                    <http://dbpedia.org/resource/100>   \n",
-       "306492                    <http://dbpedia.org/resource/5>   \n",
-       "312986                    <http://dbpedia.org/resource/7>   \n",
-       "363000                <http://dbpedia.org/resource/Abuse>   \n",
-       "364696  <http://dbpedia.org/resource/Academic_discipline>   \n",
-       "\n",
-       "                                               ignore2  \\\n",
-       "4002    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "306492  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "312986  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "363000  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "364696  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                 abstract  \\\n",
-       "4002    100 or one hundred (Roman numeral: C) is the n...   \n",
-       "306492  5 (five) is a number, numeral, and glyph. It i...   \n",
-       "312986  7 (seven) is the natural number following 6 an...   \n",
-       "363000  Abuse is the improper usage or treatment of a ...   \n",
-       "364696  An academic discipline or academic field is a ...   \n",
-       "\n",
-       "                                           id      node1               label  \\\n",
-       "4002       Q37413-wikipedia_sitelink-c0dbd3-0     Q37413  wikipedia_sitelink   \n",
-       "306492       Q203-wikipedia_sitelink-c8e811-0       Q203  wikipedia_sitelink   \n",
-       "312986     Q23350-wikipedia_sitelink-6dd502-0     Q23350  wikipedia_sitelink   \n",
-       "363000    Q600571-wikipedia_sitelink-5719bc-0    Q600571  wikipedia_sitelink   \n",
-       "364696  Q11862829-wikipedia_sitelink-cdefe0-0  Q11862829  wikipedia_sitelink   \n",
-       "\n",
-       "                                                   node2  \n",
-       "4002                    http://en.wikipedia.org/wiki/100  \n",
-       "306492                    http://en.wikipedia.org/wiki/5  \n",
-       "312986                    http://en.wikipedia.org/wiki/7  \n",
-       "363000                http://en.wikipedia.org/wiki/Abuse  \n",
-       "364696  http://en.wikipedia.org/wiki/Academic_discipline  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "abroad-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initially, there were 5732949 short abstracts. From 5785371 Qnodes with short abstracts, there are 324 Qnodes which have some similarity measure\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Initially, there were {len(df1)} short abstracts. From {len(abstractsDF)} Qnodes with short abstracts, there are {len(abstractsDF1)} Qnodes which have some similarity measure\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "extensive-giving",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "absDFLabelList = abstractsDF1['index'].to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "charged-valuable",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1['index'].nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recent-kansas",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "terminal-offer",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join sitelinks with DBPedia file - File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "particular-nerve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "dimensional-preserve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>Q956244</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>Q353153</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>Q371</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>Q91868926</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>Q2705922</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         index                                     id      node1  \\\n",
-       "0                 Q956244-wikipedia_sitelink-539183-0    Q956244   \n",
-       "1           !!    Q353153-wikipedia_sitelink-d716fb-0    Q353153   \n",
-       "2          !!!       Q371-wikipedia_sitelink-628937-0       Q371   \n",
-       "3      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0  Q91868926   \n",
-       "4  !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   Q2705922   \n",
-       "\n",
-       "                label                                     node2    ignore  \\\n",
-       "0  wikipedia_sitelink           http://en.wikipedia.org/wiki///       NaN   \n",
-       "1  wikipedia_sitelink           http://en.wikipedia.org/wiki/!!       NaN   \n",
-       "2  wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!  211415.0   \n",
-       "3  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!       NaN   \n",
-       "4  wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)  584428.0   \n",
-       "\n",
-       "                                         url  \\\n",
-       "0                                        NaN   \n",
-       "1                                        NaN   \n",
-       "2          <http://dbpedia.org/resource/!!!>   \n",
-       "3                                        NaN   \n",
-       "4  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1                                             NaN   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3                                             NaN   \n",
-       "4  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                            abstract  \n",
-       "0                                                NaN  \n",
-       "1                                                NaN  \n",
-       "2  !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "3                                                NaN  \n",
-       "4  !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "existing-disabled",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 8472828 Qnodes, there are 2741574 sitelink Qnodes which do not have a short abstract i.e 5731254 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "normal-rachel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attempted-scotland",
-   "metadata": {},
-   "source": [
-    "# Join list of similarity file Qnodes with File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "premier-attack",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = sitelinksDF2.join(df1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "disturbed-windsor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp2 = temp1.reset_index().set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "southern-cookbook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q956244</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q353153</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q371</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q91868926</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q2705922</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 index                                     id  \\\n",
-       "node1                                                           \n",
-       "Q956244                   Q956244-wikipedia_sitelink-539183-0   \n",
-       "Q353153             !!    Q353153-wikipedia_sitelink-d716fb-0   \n",
-       "Q371               !!!       Q371-wikipedia_sitelink-628937-0   \n",
-       "Q91868926      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0   \n",
-       "Q2705922   !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   \n",
-       "\n",
-       "                        label                                     node2  \\\n",
-       "node1                                                                     \n",
-       "Q956244    wikipedia_sitelink           http://en.wikipedia.org/wiki///   \n",
-       "Q353153    wikipedia_sitelink           http://en.wikipedia.org/wiki/!!   \n",
-       "Q371       wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!   \n",
-       "Q91868926  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!   \n",
-       "Q2705922   wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)   \n",
-       "\n",
-       "             ignore                                        url  \\\n",
-       "node1                                                            \n",
-       "Q956244         NaN                                        NaN   \n",
-       "Q353153         NaN                                        NaN   \n",
-       "Q371       211415.0          <http://dbpedia.org/resource/!!!>   \n",
-       "Q91868926       NaN                                        NaN   \n",
-       "Q2705922   584428.0  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q956244                                               NaN   \n",
-       "Q353153                                               NaN   \n",
-       "Q371       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q91868926                                             NaN   \n",
-       "Q2705922   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \n",
-       "node1                                                         \n",
-       "Q956244                                                  NaN  \n",
-       "Q353153                                                  NaN  \n",
-       "Q371       !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "Q91868926                                                NaN  \n",
-       "Q2705922   !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "ordinary-connectivity",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsDF = wordsimQNodesListDF1.join(temp2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "married-prior",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \n",
-       "node1                                                         \n",
-       "Q2855502   A chief of police is the title given to an app...  \n",
-       "Q107711    A firefighter (also known as a fireman or fire...  \n",
-       "Q7188      A government is the system or group of people ...  \n",
-       "Q16334295                                                NaN  \n",
-       "Q11499147                                                NaN  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "durable-keeping",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "oriental-devices",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF[~wordsimQNodesListWAbstractsDF.abstract.isna()].index.to_list()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dominican-illinois",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "afraid-asian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beneficial-dynamics",
-   "metadata": {},
-   "source": [
-    "# Adding labels from labels.en"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "satisfactory-nickname",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "potential-vegetable",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>P10-label-en</td>\n",
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-       "      <td>'video'@en</td>\n",
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-       "      <td>P1000-label-en</td>\n",
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-       "      <td>'record held'@en</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>P1001-label-en</td>\n",
-       "      <td>P1001</td>\n",
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-       "      <td>'applies to jurisdiction'@en</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>P1002-label-en</td>\n",
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-       "      <td>label</td>\n",
-       "      <td>'engine configuration'@en</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1003-label-en</td>\n",
-       "      <td>P1003</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'National Library of Romania ID'@en</td>\n",
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-      "text/plain": [
-       "               id  node1  label                                node2\n",
-       "0    P10-label-en    P10  label                           'video'@en\n",
-       "1  P1000-label-en  P1000  label                     'record held'@en\n",
-       "2  P1001-label-en  P1001  label         'applies to jurisdiction'@en\n",
-       "3  P1002-label-en  P1002  label            'engine configuration'@en\n",
-       "4  P1003-label-en  P1003  label  'National Library of Romania ID'@en"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "significant-cross",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsDF.join(labelsDF.set_index('node1'), rsuffix='_labelsDF')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "bigger-separation",
-   "metadata": {},
-   "outputs": [
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-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>Q2855502-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'chief of police'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>Q107711-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'firefighter'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>Q7188-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'government'@en</td>\n",
-       "    </tr>\n",
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-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q16334295-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'group of humans'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q11499147-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'political activist'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q2855502   A chief of police is the title given to an app...   \n",
-       "Q107711    A firefighter (also known as a fireman or fire...   \n",
-       "Q7188      A government is the system or group of people ...   \n",
-       "Q16334295                                                NaN   \n",
-       "Q11499147                                                NaN   \n",
-       "\n",
-       "                  id_labelsDF label_labelsDF           node2_labelsDF  \n",
-       "node1                                                                  \n",
-       "Q2855502    Q2855502-label-en          label     'chief of police'@en  \n",
-       "Q107711      Q107711-label-en          label         'firefighter'@en  \n",
-       "Q7188          Q7188-label-en          label          'government'@en  \n",
-       "Q16334295  Q16334295-label-en          label     'group of humans'@en  \n",
-       "Q11499147  Q11499147-label-en          label  'political activist'@en  "
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "native-british",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsLabelsDF.drop(columns=['id_labelsDF', 'label_labelsDF'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "popular-harmony",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp123 = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: type(p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "adolescent-panama",
-   "metadata": {},
-   "outputs": [
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-       "Columns: [index, id, label, node2, ignore, url, ignore2, abstract, node2_labelsDF]\n",
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-     "metadata": {},
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-    "wordsimQNodesListWAbstractsLabelsDF[temp123 != str]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "twenty-purpose",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: p[1:-4] if type(p) == str else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "beginning-department",
-   "metadata": {},
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-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>chief of police</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>firefighter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>government</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>group of humans</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q2855502   A chief of police is the title given to an app...   \n",
-       "Q107711    A firefighter (also known as a fireman or fire...   \n",
-       "Q7188      A government is the system or group of people ...   \n",
-       "Q16334295                                                NaN   \n",
-       "Q11499147                                                NaN   \n",
-       "\n",
-       "               node2_labelsDF  \n",
-       "node1                          \n",
-       "Q2855502      chief of police  \n",
-       "Q107711           firefighter  \n",
-       "Q7188              government  \n",
-       "Q16334295     group of humans  \n",
-       "Q11499147  political activist  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "fifteen-patent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = wordsimQNodesListWAbstractsLabelsDF[~wordsimQNodesListWAbstractsLabelsDF.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "structured-archive",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "shared-secretariat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "fossil-courtesy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>chief of police</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>firefighter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>government</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q245065</th>\n",
-       "      <td>Intergovernmental_organization</td>\n",
-       "      <td>Q245065-wikipedia_sitelink-1b7141-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Intergovernmental...</td>\n",
-       "      <td>6572.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Intergovernmental...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An intergovernmental organization (IGO) or int...</td>\n",
-       "      <td>intergovernmental organization</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7278</th>\n",
-       "      <td>Political_party</td>\n",
-       "      <td>Q7278-wikipedia_sitelink-7a73df-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Political_party</td>\n",
-       "      <td>10973.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Political_party&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A political party is an organized group of peo...</td>\n",
-       "      <td>political party</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   index  \\\n",
-       "node1                                      \n",
-       "Q2855502                 Chief_of_police   \n",
-       "Q107711                      Firefighter   \n",
-       "Q7188                         Government   \n",
-       "Q245065   Intergovernmental_organization   \n",
-       "Q7278                    Political_party   \n",
-       "\n",
-       "                                            id               label  \\\n",
-       "node1                                                                \n",
-       "Q2855502  Q2855502-wikipedia_sitelink-2cf037-0  wikipedia_sitelink   \n",
-       "Q107711    Q107711-wikipedia_sitelink-4a93a6-0  wikipedia_sitelink   \n",
-       "Q7188        Q7188-wikipedia_sitelink-481ab9-0  wikipedia_sitelink   \n",
-       "Q245065    Q245065-wikipedia_sitelink-1b7141-0  wikipedia_sitelink   \n",
-       "Q7278        Q7278-wikipedia_sitelink-7a73df-0  wikipedia_sitelink   \n",
-       "\n",
-       "                                                      node2    ignore  \\\n",
-       "node1                                                                   \n",
-       "Q2855502       http://en.wikipedia.org/wiki/Chief_of_police  248977.0   \n",
-       "Q107711            http://en.wikipedia.org/wiki/Firefighter   80424.0   \n",
-       "Q7188               http://en.wikipedia.org/wiki/Government    5382.0   \n",
-       "Q245065   http://en.wikipedia.org/wiki/Intergovernmental...    6572.0   \n",
-       "Q7278          http://en.wikipedia.org/wiki/Political_party   10973.0   \n",
-       "\n",
-       "                                                        url  \\\n",
-       "node1                                                         \n",
-       "Q2855502      <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711           <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188              <http://dbpedia.org/resource/Government>   \n",
-       "Q245065   <http://dbpedia.org/resource/Intergovernmental...   \n",
-       "Q7278         <http://dbpedia.org/resource/Political_party>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q2855502  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q245065   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7278     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  \\\n",
-       "node1                                                         \n",
-       "Q2855502  A chief of police is the title given to an app...   \n",
-       "Q107711   A firefighter (also known as a fireman or fire...   \n",
-       "Q7188     A government is the system or group of people ...   \n",
-       "Q245065   An intergovernmental organization (IGO) or int...   \n",
-       "Q7278     A political party is an organized group of peo...   \n",
-       "\n",
-       "                          node2_labelsDF  \n",
-       "node1                                     \n",
-       "Q2855502                 chief of police  \n",
-       "Q107711                      firefighter  \n",
-       "Q7188                         government  \n",
-       "Q245065   intergovernmental organization  \n",
-       "Q7278                    political party  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "compatible-september",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF1.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "subject-access",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "silver-structure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "dramatic-projection",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstract'] = wordsimQNodesListWAbstractsLabelsDF2.apply(lambda row: row.node2_labelsDF if pd.isna(row.abstract) else row.abstract, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "accessible-trunk",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>chief of police</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>firefighter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>government</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>group of humans</td>\n",
-       "      <td>group of humans</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q2855502   A chief of police is the title given to an app...   \n",
-       "Q107711    A firefighter (also known as a fireman or fire...   \n",
-       "Q7188      A government is the system or group of people ...   \n",
-       "Q16334295                                    group of humans   \n",
-       "Q11499147                                 political activist   \n",
-       "\n",
-       "               node2_labelsDF  \n",
-       "node1                          \n",
-       "Q2855502      chief of police  \n",
-       "Q107711           firefighter  \n",
-       "Q7188              government  \n",
-       "Q16334295     group of humans  \n",
-       "Q11499147  political activist  "
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "colored-imagination",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "labeled-eagle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF2[~wordsimQNodesListWAbstractsLabelsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "welcome-orleans",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF2.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "quick-chicken",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "potential-format",
-   "metadata": {},
-   "source": [
-    "# Fetch saved files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "defined-harris",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "documented-replication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "apart-divide",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Sentence Transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "parental-craps",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "initial-coating",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "documented-alignment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.550719976425171 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertEnc = getEmbeddings('bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "continuing-impression",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1.24G/1.24G [02:08<00:00, 9.68MB/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.648171424865723 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLargeEnc = getEmbeddings('bert-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "responsible-national",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0568156242370605 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "distilBertEnc = getEmbeddings('distilbert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "interracial-mitchell",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEnc = getEmbeddings('roberta-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "amended-powell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.765424489974976 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "robertaEnc = getEmbeddings('roberta-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "divided-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "characteristic-religious",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "quality-sponsorship",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncMap = getMap(bertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "electronic-parcel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncMap = getMap(bertLargeEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "confused-snake",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertMap = getMap(distilBertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "vanilla-subject",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEncMap = getMap(robertaBaseEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "familiar-soundtrack",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaEncMap = getMap(robertaEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "subjective-leeds",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "bronze-sally",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "detailed-imperial",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def checkSimilarity(row, encodingsMap):\n",
-    "#     print(row)\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "funded-prototype",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "improving-nevada",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLargeEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "conventional-white",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(distilBertMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "related-garage",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaBaseEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "portable-joint",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "mental-forestry",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF[['Human (Mean)']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "round-cornell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-79-5105af3a2513>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = bertEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = bertEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "exotic-fundamental",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-122-3b87f5bd1dcd>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = bertLargeEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = bertLargeEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "latter-cleanup",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-80-c904909da8f6>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = distilBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = distilBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "excellent-meeting",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-134-f159fb4d0068>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "south-rwanda",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-81-92c04e1dd63d>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = robertaBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = robertaBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "proper-stations",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-82-96b13f46512b>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "legendary-campus",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-123-cdaec8bd10a3>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "ambient-victor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-83-383da2239499>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "velvet-ethics",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-135-c2b58af44ba8>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "colonial-coordination",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-84-ce07de4ac656>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "informational-fountain",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.float64"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(wordsimDF1.iloc[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "japanese-order",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.426863</td>\n",
-       "      <td>0.360219</td>\n",
-       "      <td>0.436207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.339922</td>\n",
-       "      <td>0.433100</td>\n",
-       "      <td>0.427664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.686578</td>\n",
-       "      <td>0.706978</td>\n",
-       "      <td>0.645407</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.296175</td>\n",
-       "      <td>0.373679</td>\n",
-       "      <td>0.371295</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.340811</td>\n",
-       "      <td>0.390540</td>\n",
-       "      <td>0.488393</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim\n",
-       "0        5.5360  0.426863       0.360219        0.436207\n",
-       "1        4.1250  0.339922       0.433100        0.427664\n",
-       "2        6.6250  0.686578       0.706978        0.645407\n",
-       "3        2.0625  0.296175       0.373679        0.371295\n",
-       "4        7.1875  0.340811       0.390540        0.488393"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "marine-general",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertLargeSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "living-condition",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.robertaBaseBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "hearing-adjustment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertSim *= 10\n",
-    "wordsimDF1.distilBertSim *= 10\n",
-    "wordsimDF1.robertaBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "suspected-plumbing",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>bertLargeSim</th>\n",
-       "      <th>robertaBaseBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.602192</td>\n",
-       "      <td>4.362067</td>\n",
-       "      <td>[[0.4949737852167514]]</td>\n",
-       "      <td>4.196118</td>\n",
-       "      <td>5.313815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.331005</td>\n",
-       "      <td>4.276645</td>\n",
-       "      <td>[[0.4818230866992358]]</td>\n",
-       "      <td>4.045933</td>\n",
-       "      <td>5.239753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>7.069776</td>\n",
-       "      <td>6.454071</td>\n",
-       "      <td>[[0.6407051485261465]]</td>\n",
-       "      <td>6.215223</td>\n",
-       "      <td>6.513252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>3.736790</td>\n",
-       "      <td>3.712948</td>\n",
-       "      <td>[[0.6186497481059658]]</td>\n",
-       "      <td>3.581833</td>\n",
-       "      <td>4.266170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>3.905397</td>\n",
-       "      <td>4.883926</td>\n",
-       "      <td>[[0.8556663921383962]]</td>\n",
-       "      <td>4.322380</td>\n",
-       "      <td>5.111274</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim  \\\n",
-       "0        5.5360  4.268633       3.602192        4.362067   \n",
-       "1        4.1250  3.399217       4.331005        4.276645   \n",
-       "2        6.6250  6.865782       7.069776        6.454071   \n",
-       "3        2.0625  2.961755       3.736790        3.712948   \n",
-       "4        7.1875  3.408112       3.905397        4.883926   \n",
-       "\n",
-       "                  amanSim  bertLargeSim  robertaBaseBertSim  \n",
-       "0  [[0.4949737852167514]]      4.196118            5.313815  \n",
-       "1  [[0.4818230866992358]]      4.045933            5.239753  \n",
-       "2  [[0.6407051485261465]]      6.215223            6.513252  \n",
-       "3  [[0.6186497481059658]]      3.581833            4.266170  \n",
-       "4  [[0.8556663921383962]]      4.322380            5.111274  "
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "incorporate-punishment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.sort_values(by=['Human (Mean)']).plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "prospective-international",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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1GY1CodBy0T8jD/nYtXjSdwkT//3dO2Sc3k5EQtkI4JkAzgNwOoBJIcTzW7b7ZSHE5UKIyw8cWD1vtwdNY3QIVTMYD+DiiAWlDfvYi8GARhtAsoSSlXSlP1YJpc0ecCvh4TfePqBFxznBmBVmFXIAJ6Mh4GWteLyVdnSX7lE0y9Rov6PNr+pCwS72IneXDsDsuREAYARBVas0CTsSYcrbF+z4870wmAGwMtnwqObunz58GBffSnjClYTP3PoZvOGyN+DR73s0Lr7kYixVS0fZyZ1n3mdTCXYtms+koeA/C6Ukpnr23yMzyab75wf48Td9Cbceun2Y+YlIKE8EsJ2IDhBRBeAjAB7V3IiI3klEDyeih2/ZsuUEDpdaTOQxqB2AV5nVJ9uKVNUOaWQVv+OJPMYQcg7gK4gDD5PICTDwTEcH7GqbPkaJR05M2N+tFMCNjk5MVmKTP/CGRaQsJ494Bj4OwM1ggIPvfBfodga2ZEzNMEI2/nVvfyDMh38dADDoYtUAPDDw8vZNJNsznAn/PV8tYKlawoPf+2D8w1X/sKLfH77kEtz8jGeOfO6BzbAolBk3QQxc2Yu58q4ZdgtEWbXi8cjNF4hMSPQLK9D1Vo4SM+m7s+PwEm4+uIibD971APw2AD8ihJgQ9u17AoBrV2dYRzdiqfS6igAOoLUCXu3olOSJNwywNREU8SiUo7+QuxdvwfT9XotbFxqeZ10BH3oxsP/ol6OoAXU7qQbHqoGLSQvgK9XANekkkSdIUgyEuZa7HLs+2hgP/M1bceDNb8bcpz61orGthvlzC+GE7JmoHvJSmAe+CAAw7ALZagN4dQITFRHw6d8D9l0z+rmzWwZR954H4ZKrLwEAfObWlamg5a23obytpUiau0Y0HwGrbpSlEHd6Db9lzE1AC2V8B0RT+jEUmHpg4BvWAQDU7EJzU/v97eTbOREN/BsA/h3AtwFc5fb1zlUa11GNp9LXLta4ckEBaUKOW9I5cPZFr+x28b+rqkbBHrSVSChXz9iGEt+f/Srmv/Ql7P/rv7ZfLOwDrv4ocOvRa0Lcngz8WCUU6Qai51bGkGpTszBCA+EBmpdfZZ16lqt54iOCxmngw+vsZCinp1c0ttUwz65EmJfYiu30x8Gc9nA7ti5BVe2TU/+qqzC4/oYVH9NPeCekgS8eBC77W+BfbDji+kXCvXdSUpdmwPIhFkD44g6bwHXxKReveJxtSVph9csm7rqRFOdXW3dF8+OfX5oJn4044olQVymAZ3nH/juTMm0f6HB7+eZPKAqFiP6AiO5LRA8gohcQ0ep5Xo52bBYHrsuUgRNPwvGMwF1wyWK/+QNt6gqZOTYnpg6SgcDOX30ZDr39HX5n7l82kYy5g3kdtdbVtmOWUBwrM/Mpi9ALi60gxKNQLAMfTd1PonlGmsMyQGSSGLddV16Gmd3bUd5mkyRktz3VwAyHWPp2S7jcMvbF6/cvWwohRqG0SEN9HaJQ+l2JrDbY25LQdcvPPhvbnzkqNYyzKKGcCAN343Ra8xv/UeOP36uTJDb+7M/LyJJX6oMgXQNaj65U/fvGALzyxyWCNLQiBv7tfd/GgaXj85kRET53zb7j8005AF9YjBJQE8DJEOpGKn0Gyx7zuVTfb0nmXVU7aTMxNYsDDxq4D8s1owzcOE1RMQZueJnQepho4Ms1PA6/J1/UPT6QH7jyvfH4nHmMceYVNSEzt88S61g1epm5azWThoDNfOD9uOU5zxkZY+LE1NGJyRm40e2gYcdlRv67ycDnfu4Xsf0pT0O1c6c7Tvt92f/nf4Fbn/c8LN5wfev3TdtxeAm/8E/fwueuHR8DnTVDU9l5LV5+DQbXWoliqSuhKsI7vpzGAB9Pev2qAHg9wJd6Pcy6673Jzce8Lg2/9gsivgsrdiL7lULjufYhdzx22oPd//4q4f1v1ED/6Nm2L/rUi/Csjz9rZWNp2Oev3Y+X/vPlI/djReYmoMXFmA2rm925iELxNgn37rrz7sylk/hq+MmWs5MWwClcGB1iu2vPwPUoA/fe/RTAGWBXAygmoaxEA48Pe7yMX7jyo4yB12Gs415IXwLgaJ1r2uz6w9fj2kPjdfZjrYXiV7b6cBoXrOfnQf1+AmCAXRrHin0UmR/XwBmAl1Ufr//q60OyTiuAtwy2W7LPxkhb5a02Vfxl711ZFuPAadbjOspzR7gHo4qVBTjwzvdi7j8+geHkOlS5QK5H99W/6iq3gxVIBvuusfHFqwDgS4MZ/MZpp+DjPZV8nqw4GVlZkDLIWysN4/Qr2hEZpWWCrfdfDQB40necnDm3Mif58To7Z/t2TD/YvzJfTmLuvi/1ZzG9RHjdBzTu880jjW0oLXZHFPxd3blUhCD+ftwOdtICOE+l98wiJMYxVuBfRKpGNXDN6zxXJbJjjEKJqwBAdKwGls8tjQD4zle8Atc/7OGt+yj8pkcp+tRmb77izfiLy/9i7PfH6kAhYWdAM9NYurrrNNLclQzLxLRRKNvzDCW4NBX/e9fsTnzspo+FTuU6ud4rW8KPC0tU66wTaXKFCTVe/tKGcPhf/hW3PPd56QbsOH6ZXDuisONhQ5z7/n/BuR/6EL7w22+GVgJ5bUvNUlVh7tJLMfPhj2DmIx8BAHTvd7/lB3PkVuDtjwR+8PnAaM3w+AG8P5wDCYF+Y96oqnhxQhlmCMxJGf5esYRStwN428q1HqSVGul2lsC7uQWC/vE4lt2zvjSYx4s/a/CQmwmdQXpOwlCSEAitQQM7WeQNX8ixkqhjtZO2qV+8MBHA9TIMXNRp2VkALv3b/3eJnPhyP+7ji9ftxy+851v4xu8+AaetixosZ+Bq/XrU+/ejmO2PaOALn/t86zkQUWDgdTWE6vVWdvLOSl2OOIi4HasGDpkDMNAzKeMI17MB4NyJSaRBpPGc07fiqeU+PC78ljuGKzee0eW692McjQFeuffb+OgHNuGymw/hstc9IQ59vQXwqYG9rkdzlPlro4mw74/+aOT7hIFT+ll/HaH3oIcCQsDsvh61tAxca42lyy/Hrlf9VrIvMbH8fT2wfw+2ALhu+y0oVoGBl5V1pJUNrdmUHMDtuawTORakGOuDGGcBwJvhji0MfFjb44bruKIjHL91XJnfwfEAuLvHg8EiLjgyZqSOgXvsICJAFfHr4RDSEbrbux7RScvAfdQCjwPXPgqFOTGbDDyv2jVwqobIkM6q3t77dbs8//6ulEkoF0vbHR6BWr/e/vf8kAH48qy6NjVj4Ct/Ya88cCX+6Ot/BENmWcA7Vv2NRG63n2/UtvaOqcbLqXXJnJgEQQZLUqKfMPB4DSq/UnLXhzPw2oHL0VYLOTJ85Du7RitAurTwycHKIohCsMSY1nn8GYqrDLttLbIgi2RKQGcui7QqsfvgdgDAnz87x6/9msJ1ZwI7Z5fvSXrtTpv1d/nNB0L44IkA+NABeNWYxOpqFMDXywILQoZrtuLVmh4noYxe+1JbWSEA+O2cyOMZ+GBMZNCy5p7x4XAJ0+OkemowcGNAMo9fs2eK1jTwduOZmF5+0JmP1+WRDykD7yWJmBzoKyjOCFsiSJqkbmLeOkm2zF8XALw3X7Y7MVtsqIeBgWsG4GZpCXOf/ezYl+lru76GD1z/AdSmXvaF8yC18mfHJdP0G550v6NGZI4xVUx2IQ206KhJR3f3Invw4Ne4nnPORNcd5vJdX8cnrv/3kREWUCOfAcBsZq/f1ICOKYu2t/3G1u/5ysFr4N5Bp0V8bXIlUUv7t6hKHJq3/oMHXPRj+JnHvgzU7aCs0slGLyxguH17/Lvq4+0b1kGLpeCrOZE48MDAGw+s5pPCfB8v/ZTGJlNgXkroOeeXwApBz43z0DvfiZmPfDR8THu+NzoeNzmH0qu3cyJPpuyRBisoCT1igfDVWLccgPOJyhhAMDEjwQ7377GPZEV28gI4q4XiZ0PTFgfuWaObjTtDxsp5tEQ1RM6dmPwmuH+b4U8eiAQkRGFn4ImFCjQcYOf/bMRgx/I1JgZ6EBg4B/DD//qv2PWK38DcJz855tyNO6Vq2SXvMTNwv85rgodn4E0npi5jIo82ADQmBpQwdc7AfWimZ9681Kw+YqNMjGuv9guf+yX87tf/7+g5HWlns4f6NnJmqg9oc3Tw8xp457abw2d8MuQRG5qA91/3/jDha/ayZlJA+848wzJE3fzkBU/Hyx/8chRZZ2T9vOOXfhk3/+RTw99XLl2Ft23cgC9m312VWiilY7hlg3BwCWXTF2/FT3yH8OgrKsxLGd4n05LF3GZeQpn54Aex53d/N37Rcn/qKmXgR5MTzBgitVLzt/F4GLhw9z0vNSbG3QKDUE4WcO8FOyeOP8dbUG6ldtIDuCEd0rWNS2lMHIK+c4aL95WIxZp0AvQlFNoB3EQET4wCW5FhyT21oDHctw/zO3tYumH5Mp2cgXMJRXatZrr339+Pr+/5+sjv/MOgSS8roehjBPBBYB/NuNd2DVzrMvoQyCby/M07NC68imXhMWegbkooSeMHB1xs/6Jt3FW7l9KzXCuhrLySpOCslB2v5veDgD/+xh/HpDAG4EoK1FKEfXkAlju/CXzvQyCYkXKk/e/YeHXjQHpQ2+tlhA6/P5FaKMMA4A0JhQF4NWHZzuZZg74UMH71tcJm3uOqJRKNQko9TGOmm71om5aEOFbHHkni34nj0cCFO68Ny2W+EyXkg+oqJTd6FDvWJBRm39s5w8rJxroEnoFzYAhFZ5jzcjhrWZ6mFMAzDtqcnVMFNXHzSPpBSOSBCLPuuiVguGDDpMxg+ZdwUC6FWONDv/v7OPSe99i9FdYhYi67HL/3ld8d+Z1/wGtTryg9faUOlJ3OiTvSoCJEoTQ87BzojUFGdtnZW2LXkTHw2iVEBIcZZ+duAjQMwtsKk5kxIQz++k8OKDhLlzPvxEzYEi9uxsbmE638xFAzAM+VhFb2NaJqCDriEo6+/lbgIy8FlXMQVYPKKfugGpfx6jXiDiQW+/az49XAiYgxcJF0RzJsHNW0XTFOzdn1nHGX1QxXFuLXLLccAWwUUnSVdq85mkzDn+nZ4Zheo9f9F9CfaR9bYODHDuCytMfeuMxlEIagG0mAfPJPVqBB6j3moazITjoAv3HfPJ7xt1/F0IMKk1DIAXjSUd4nFrCMu/6sS1ThgFRXyBIGHr/bIz+KiXPeidsW0yQRMhqPvtpAaBHCzqb7hMGifQnnl5Z31pT9OM1X370K+//MdqNJIjcWRqmAZxjLpabz01vp8s0D1VKzV2hg4OlLW/OJ0hCUqP0Ag3E/Q9N5yScAz2j4UFszVMctqR04TQ5aEi9azE++Sa0W3XgpG+OIEkrU4TMlUEv7t6yGIDcO+fjXAr9+OSAVROPt9U0P9Pw8qCzxjDd+GU/4rkEHgHa/p+MMI7zxMY/F5P/9TwCw5WJZTY+6jADuw3CnZmtoIeCvKq0QwNFw/mrXxabt17oBpLyERZtxAJ9hRbeCHbkVeP/zgI/88pjfHz8Dl+43GxfcPvKWjSgSDsBFV3HSw9n5mgYebaFcwJG+XQaee9MQf/pPNYyugmRCTkLRTELx0gaP/x7MzdjteBRKnToxORtbgmVV/Tp9PC+4ahav/A+DC644HICgUwH7Zq10smtp+Zdh2B+zPGQPRz7XHwHglTJwc4wMvOtoWN8Y6IUF3PDIR2HxG9+MDLwBRDzFmMiEcrxcMuAlCYzb3uuamjFC7Z13bP9tNWLGlqd1+1rXR5L9Oc7CqfCcATbZ8BouvGAXAFQyhozlUqJ2Grgoh4F9yQ2nA6fcG5ByRAryVR/N3ByqPXvQGdT4lUsNCoggqR0vA9cHDyJ30VKlEMAwOgyJrwTcBZiYqRwDdxLKcGUOxqaEUu3ZCwC4GaM3zThQjCGny8s0R2XgflKabe9BaQjITI0Hb//OMWvPvqaNz16dm2zZiCgNgKjrhrN+LQql1X7mEz+DT+/4EADgqf/Zx732AmaxD+21vRYJJTLweAHL+RkA6VKH6goZf7D4DXIPpZJp2PzEnAPtvg7A0iuBgZNQMFyeAYzr3sGBZGrJoGw45bgGvtxy9JhT6d12QyIMr70W+sgRHPibv0k0cCLCua/9L7z5M9cnjlcyBIVA+ePnbDLyerhn4Dwqwl8/rum3MfC2UsFABCfrxFxJMw4voXCJh0k67pmqZdq4GQAMd2IqgcoxcFENwupJZRbkSyI0F0q8bG+1Ozax6pmYF2DK43dieisFAMao0/tlr2N3MWXgZoVNqJsAXu+zAD7ZooFTE8DHlEPwlgB42QLg/j7J9ogkIsIP770Wr7nsPSi337LssZq/83kiXgOfbwFwYRqrNVODZx+X/XjN77LVCO8M27e0D0dcp4w4m5vw8noGzpe/fqaUmjDn8ikWZu0+kkgTU6XlZHk6uHu8M5ECuPDdR5QMQNAtgXLJ3n1xNADvj/GUsIdjeokwqJv1FRwD7x8BzTW62N30eeB9z7MswS/9V/js+Kp7ggAod651nWjgfl9/84WbkutMxkAhfVHtqYwWCIsSSgSL7PAiPvinNe5/fbxmrWV2x738Dniyxss1zkImJru2Sd0Wd25VFieSYWXjymoV19VKCtSKA7h73lx1OhKjztgA4HNzCYCfunsQJgt9ggAuDaGEAC0cjufEWT17KPKaoENLuxVGoeh2Bl60+Sgqwv/+H4MJd3g6ygTLpcG5wSyqvXuT78u6j589fSu+OaYOMxEwVVr5kgZHr7sSftcS+bM00X6AxHdSpxIKJQlTx7YKPlY7aQCciFCbOt7c4N01AQjIxX9yB5RndqoGFhyAz81YiSMJmdJ1CuBcXnHaeNZg4JIBuD+OBXD78MijAHg1aNfIOWud7gP9On0IPevWugI1GdOObwDX/xdA5tjrgbvNpCGIPHNjqRMNnLN5zY5NRHEFkzh0OICnEgpne5O3WQ31R74Tt291Yo4BZ966bSXg5x21ml1bw8DcyztaWVnsz/6pRvnmt9vPWNKGjQO310rV/bByU7ll4BbA02MHCWV+HtXuPeHzqUPsxR8McPElF+PVX371Uc+lzdYv2kQeMxdDWU3CwOMzduoMUPk6OCtVaxsaeABZsqsWbg+7ivBzX+HyZI1KG7zuI9/Dl64f7cfJx7bhc9/GTY97PPrfi/Hltxy8Bf/vzRL/8YN2idIQoeuetaPVNKp270Z5663QhvD6D1yRfDcsCFU+OiEJg6QMB+nUicmlwds7lf6kAfBQ7MglAYSkAB1T6YMTk2ngPnJA1oRhz17FBVfrIw39KZOWavy76YUhXvRZHQv7O/O6OikZsq8UAXrOA/jybGYcA+egt64PLNWNxJrDNgmkBo2GEYYkIn3MYYRJ/WuXrEJaJxq4HqNvw5jwlHLGmTgxmwycR0joUfaetb1749z5iQPy6AVRdK3x7Bs+DzXPGCpnVe4l7AiDqQFw/l4A19rELZ7RlUmB0jHwrBpEnwsDcDkGwPWclVB8BEjeZ+cwtBPLp2/59FHPpc02LlgNnOaPxHNukVAAYOsRwrpF4OnfMNi4326za6aPt3/pB2Mn/xENfMFp5yRQNgp0TC2m+yCt8ZbP3YD3fXMHLvnaLSP75hLYxI02wWhw9dXxWDvtquWJX22fzA0BPY8JR2npt/f//j/ses1rcOuhRXzq27cm35WdiCnpCaRhhEanDJw/13FM93AJxdcr7h6xkSAxplTH9GPviOHREZ6Ba6B2AN6fsy9tkkqva2Qt1fEA4MWfOYinXU6Y+u5VyZik86bXSqSZe66ouyqXB/B6DAP3LK5SVkIZYeALlrXUwKgCHlYoscXZSkOYQhNfAuAje+oqqYXCH0TTZOA+ioc9q7SMhMIBJfgc2G/bGPhYRsWdSMOjA3jxvSvwC9dcivt961ts3ywuPazq2LE9cI9kYlrEyup+0HdV1gm/aTJwv7ox81ZC2X+KqyXdZxPICs5hOds0TyiFQL0YWWrSYJpFgmw7DLzqYxov/ILBA66wz9ovXXI53vip67DjcLsE0cwVqBwZEYQRAO80FkREGp+7xj7DvLZQ+J5FEdU9OxFqVqO+WrL/XbewYyBl4G2p/VRVWPjKVwAA5c6dqHbvRq9QKBrO76oASLYwcEonwGYYYVq299hWwcdqJw2Ah8gFSkHC6DoAtr/Y/OH3M2VWA1Vue2Oe/sVrsfeP/ySVUEyVlJPl4WqZl0oabNczcAvgOiwd5Zx96NVRGHg9zolZayDPsdBzEkrVlFDccUGjD4aJAO5XeSud/QOAGwa8tQavhcIXIUm0B5kgQSUMnNc7qVMJhYNAs0ks0K6Bj3WAcQbUjLtusTZmpltiprOF+IqE85Lxs0wJlM5fkFfDcP3lMhKKPwfPwPdtljAAikEK4B/80xq/8fFjDIVzdH/jgpVF6qX4jJkxDHzbEQpVHH0E0WLpJ9sxDHyYko8gBxJrbehssgngWqN2xx+2NNTgEWB1z9XnYW3+KldPfByAEwFdRy7aopYOvfvd2PFLv4yF//kf1Pv2QR86DNIanQaA1znQ4pOFoFTKI0rfw2Rl6f36rSM9cTtpANwzcJ/AExvO6uCcIfdicQ++v9BKAyYjVAWw7lAfR9773lRCMRqSOy6TanSOyTZugzIewCVQ11h0ZCKfs+NR5fK3rV5GQhFKYa7XroF7R5NGy4MRanJHtrzSh8c7MSVFgKO6BvmECV2nEkpStpdiGCZn4AxwK/eCRAmFOUFbJJRjiQNPGdHRQ/DKlv20OTG9/LF/fdxOMFampEDlNPFcD4Baw4Br4GJkJeFXa2Z+DnpuDnM9YFA0AHxgEfVHrznGMLjCHmzzPOHeNwuUrDCZaRRgAoDBaVPYeni0fVyQKMcBeLMy5WDJ1u9uYeDNEr+kDWr3HA1b6pXw+1AGAI8ridrV6jFjATwy8Go4GlJ66FYbfvh373uVnRiMgT4yg42NGPi6EDBtDNxQ6iNrauDcBxdWwfdwBu7Zm3fghawuXYMaDLweMscUY+CkAM0C8xNm2pBQ2gBcNzz0Pj3fOMBbtKtmdObteLKyhSEzMy0SClWVjfxQEvMTol1C8ZmMokVCYQw89uM7VicmYsF+rQFXaIq0TplGwsAJsiUKJUlK8okwLVEobeGBrRLKOD2IM/AVJMHM6ZnRfevRSeB/HnIali4eYM/G+CKLhoRSurjwrC7tKkUC0lV2bGPgPs5fz80DVYWhMhgUQKfPjn+CUSg//TXCr34EmP/adXGfPD/CJ7tsnca2IxSutXDSii/HO+7JqRrAOzt/EI9+36Mxq6tRBt4AcEMav/CFf8SLrvlka0s7Qxo/9j2DMw9QAFDNGHgE8FH4OviOdyC//uqggQ8bAH75LYfx/ptuAgBs3h0JlD54ED++4woMCuCmbe6zQrQycFC6SjC6TiS8ZGUZgi1a9rMKdtLUA/cRDPsX3NPgNXDNUumlBGBQDZYCwH/iho/j1K0LOFsDpAQoJ3j4F/xFN3UAIGAMA2dv4p4/eAPOu3kYx6I1Fm1FU/Tma/c7gAYDmzrdosWJFqAxw6GVUJTCfA84ez+wpNvDCO2hR9bn/gSOuRZxYGHE9NK6Bhn3FNdVwsCTiBCi6ATmIVUMFIMT06yMgbc6MbVBa0FCroGvgIHXI6iaJiZ5ueG2U9dh06nrYL7KgIBLKFKgdBp4XlsJpVYxZ6BNA/fnqufnQHWNoSL0C6A7OLZJqNUIKAtC4SpZJaCdMHBHStZPYKoP1C7e2T/rXu4fN/k3J1ffQ1KTCXX5vWXNFYjROPPwTlBvE25uAXBtarz8v+zn173IrVZYRrJ2Egq1APiBt/w1zgCw8zTbRGPQuI4/OLAAuIzh8/cy1rxrJx6z60p89/45Ogs1AILpiBDZxk1QugKxAM7Gz4hN6N17T9fAfR0Nr53FpADNHkz7ZldMn/vsLZ/Ga//7dywY5BIbJGOELNWddI08KQMZ74hPcPGZm6Q1Zj7wAeRV2BjQBotdVyOaAY9ZXISQYy5zC8uifh+ka5BS2LkZ2HYE0Dt2pdvwcq3NHYQoFHPMUShBQmEaONWRXVBdJZpoogMagvIvP/g2PAplvITiSx1wsDtuBr6CUqxt2i4l4aduxSckDpjNIVLEDizOILmSMCJHpSwDh2fgKrfaaEsUis/+7M8cCgA+KIBun93X466FInDLhRrfP9sxVwZgSZ1qfx3zDJLGSyitxzAmOadBDsAxXWlia8Oxv9c1BBkoMhi2VAzkmZqX77LF3OZuuAZLV9gwPy8vmWI8fJ2mbfRNPWudpfv/+q9x5AMfRDdXmHQrv20xQAd6+3Z0TI1dW2ToK6AL0do9SBCleSKNRJ7kObq9xG9nJxGAey+LBwlrZHRsQ+VerLqMkoM0QO5JaSbRYU9eubTAttPYxJZpnNF5bbwzf4v9qt9cExrLwFsappvFxVC8qGmi5SU1w6HNeFQSn3uIhBHAhv/4aroNB9HG7xeu24cd/70pdInvVYNW9t86Hi+hEGLTWq1j+d1BP2V0PPIGBJ/PJ9igkhRj11pLH7LhePxBF75uxVGcmGPPpVEa+GjWFu+cdG13xzFCwpgODH9TGIBnSkBThioDMl0B2kALQMkclaZlGfiBmV2AMailQL8Q6DkGXsvjB3AQUCrg73/S+YP6zB/UwsCRKUjDATwdbOvc33AMLnUA4SYHSay14bghGgNpDJQxrRp4kknrSxPs3Itbf/75dugDd075+AOdrg8BANRh+6wdevs7sPcP/gCdTGGiZYInl0lqJIVgBCpkOwM3gEmyuHVyoZIw5rVUemvDaoj/8z6NB13nigV5ecCYAATGFRmqWfaVMkDun4dcweh4QyoG4GRqbJ2Zx6zLvEoy0sJNcMvSxdT5KIyBqDWWGIBXPiZ9cRGiAeAh+qXZjgp2VUC1BimBI9MC3z9HYPK6tOYDT59vPhbfu2knFnZ3QVUFrQ3e/bk34oev+vLIcdqMs7DEiel00R0vfyWOPDm2MWvWPQ49RZNEHraqWZqx2x7Z7vbNCkaxdGu/bFct9GUlDDyRCsaYbpFQ/IT0t1+4EQfn7T0mKGgqEgYuGhKKpgylAoq6AmkDLQEpFSpt3d5NBu4TVTpepVLWielXboMCo51ulrN9VwMffzlQlyACainChKOHXELhUVZuUHlmAZytvgBA+pDcFtxpRvAsdgHpolakGY1CGfm90ZBGQ5JujULhxKBZD4eMAblJSbRIKOF33s9g0sF0MolJBuAH1wvIyUmY/dbPUytGDjsSGBNGyJ3pxujkRTQtDPwen4k5rIa46DbC1gONO6prxsDt6WgmoUiKDBxFF1THG6KZE1HqGqfOzuOWU53zhl3x4DB1Hg2zNArgALDAAPyI08PN4uJILKnXaGULgG9/xjMx/7nPhd/MTQBqvhmFwiWU9MmY9VJTPYAxBpuG85heOAIaLkJ/64Mjx+P79EDDnZioaxzmGvz8PCbLPt7/yd9H79oo7QiisFJJsJEzcF8CwOuCTKuWQUKhEHHUVsxqbFA7u18fufUL+Oer/7l1s29uP4zv3HYk7ajid6FrDCqNN33mBmzfb1cLBhKamgycA7iEQYYyB3JdQhiCkYBShQVw2RKFUqcArhXQj/WxUKllJqo2+9pbge/8C3DVBwESqCXCeM2QySZJnXx3vbMMEizip6GBe+b43f3fxf6l/W78DQDvRMCUZM8naL8twyWjoVAiE/1WAK/ZSmiiEcllFhYAV6ZZNeCLvxc+R8M0HCbaECbKEv0C2Psj98Jf/GwOdcpmIAA4IfORyoUaq4GbZiYme/52H5nHN24+lIzpHp+JOSz7yMzostoycOccc57/BJiJSQOdiYSBg0khGw7PIdcGt5xm/+YMPOjtfqJolIn1BXAGhQhMbc4xedPvY7FMAV8PBjj4jr+HXGpf6pv5+TAZLfSAbGG8E3M0CsVFqNTDAMLSaBx80+/hhhf8Aaprv9X8hT/hsD+J+LJTXaNqgN1Fh2/B+nIJ2/4zJjaRYVEofLe8wl9Yyfgxxu9kFcHfhxu2hxGyF4e/Fey/v3PoOnx5Z/uq442fug5/9bkbR2p52F1XrMiVu9dQqE0nYeCyEQeuTYZhbhk4HAMXUqHUBoRRCcX3piw8A5eAzuNGVXZsAK633AdHpAS+/2HLwEV8DqlkK6CkDZhtRyJcDLtn/xNLhJv/1zOw9YiVFLzT+gWXvgA/9fGfGt0PgKWuQOZ6zQoXI+Av0cH1o+MlYyDFIvJiN87YfVPQtMPQGIPd0qhlZebngb5vENEAVzauULyukTdQG8JkVeH7Zytc96qnYfsWA9ntgXyTF0Uo/EKyo8bGgSfPoamT5+/yH+zHn33KRv/4SLB7vIRSukIzoslmtA4vm++UooepBh4AvJgAMQAXrOHCpoM2Ffg2x8DLqsbcIO0kEnThBoBL9+DwynWHph2Tr+uRFPyZD3wAB97yFlz4qZvHnq9vTjHfEyj6VVq3usWJScZg9hOfCA+uqasAwsJo7Pjc5QCAW69t7wHpH36vX4bCPsaMFGPq+JomXIMcw8CT+Gyfgenb4WkuocTfegY24vyzO2n7z0S7VSbWHm9apQ1qbRLnahgri3P3MgdBjgC4aDgxa+QY5kBeWYevkQCEQlmbVidm5WuGu7+1BAQH8GNk4J9dvBVPPut0LG3/MnwtEs/AiTFcaoQRkgSQpQC+6QhheOON2HbEpqvzqKM5V8aiKe8sdhBaA0qCDZ10J31w3eh4SddQxtZr+b1P/iV2vTqt90JsZbZlJr14en4ewstCTb2eXzP+HrDnt9YaE2WNfkeho1y2bJ4BLjSxVkDmfiu62dhMzKThi9YNCU+jchNHNnMY7/n0H6O7b9fIflbDThoA97Wzm2AIY8KS1DNw3vuvU0UNXEiF9feLOkfG9MFJF5rkmfMNe+bwZ5faWTS8gLqdgYd4cHY1v/AgB+BlNbKE5k2D9ejz4fblYtqn7Hj1bKQiaaVEa/3vXondv/0abLjVnruuhgH0pTGhO1Dd4rwD4sMfAJxHyDQK8Pd8lhvTIBMJhctPjAF5MKhDMwUG4F5CAVC5zNOjlZNNGDg7ZmZoLIBrQ9CGWnVyXVdxN6F0rEKFTrKsaNPAB7lA4So3aglAquDElJSOtWo4WWsFZCzWrsowXipqsQPVAvpSYhYASKCSIj5XyVK/wcAFgn/GA7gHL38vWzMxm07MbnzHpAEgYrWBg+tGH3Djolg86C98KV0t8ezJU2dtYtBNv/UsO565uVgkrjm25PycBDmcBc3EaoZmMMBEWaMsCB1f1z1TwMBXmqQouXYzLLQ0hRCGEg0cWmPIt6M6XPZN3/pvnNY/gjO/+J8j+1kNO2kAvHbLrCaAG2NTvQ2A2gM4Kwv58v8y+PN/cvqcVNjy1PvgXi/eAADIWebbpqHVrPqFixGHwcySBb0mA286MZWviSKBm88UuOICgb0u8YOqagSIeBLAsECraXdnRMe9SAzARU3YPJdqjB5wff0VXZXhRRBGQzjWlDdiyuMBUwbOr2FTAvBJEhzAiRDrgTNLGtR66cQzcPaiBtZDQOUSl1o18KRdVfy4Yi/Qr37S4AkfTQsTxZ8TiIDJpdHmu6Rrlvzk9icVBigaKaIsCkVK1JRbDbzWARghpNXAnZg8uOqq8NzoRhSElkCeMZZ4jAzcr2wGbpap8jySCVYHH7VGuXOXTYpxIY7CMXAvTXr914eEtmUQtjkx8xpuFQaQoMB62iQUYwyUiQDenBA4A1+/ZAG8f+q0/e3CQgTwxjVqK7NQVQPQX1wUjz07h4lSQxYligM32M/yLLwflYo+M9HJ2zMxqRFdpevk2JI9Rz4yThxHc+aV2AkBuBBigxDi34UQ1wkhrhVCPHK1Bta0yskiIx59rUFGw0igcg6LZhyw7y6tlILYfD6yoX25e8xB0neIOXSZmoIoMMbw7vrQshEJJTLwf3vBBN74MzIy2aoa0e15uczhGI/9kfIIttU1pHQv/cxM+O4+VyziL9+tbUpvkCPcJOUmkzd+8vt40yevcZ/p0A3nhtkbsFAuYKFcABHh0Hveg/rIkfBShnEvxs4sshGr2yahCCIInyXLN2cPdjFf4oN/WuPM7e468mgDBuB11cevfFLjVR9v6JcSabhgkr4cPz9tBti6q70IkyGCJgrdx7kR1UwD9/dIYkAFL0AIyTIxMyWgnYRS6BpC04iEAgC3/NxzMPPhjzT27cYuCTlj4OUxMnBNBqcfIgzm7cNUKQVDhf8ynp/W+METn4hbn/tcO9FIjERIefDydW3qVgCP468UMMwEJIDf/YDB5nmApJdQKEiJyXidhFK0L5KSEg2AJdPl0i32u7k5ZL7GUPMStdQyp7pOyLI8dACKgLogdJxvirJ4P4dZnMREJ2+NA5dNDVzXabAKxVovHsCbk9Rq2Yky8L8G8Ckiui+ABwG49sSH1G6+Q8pIcocxQK2hJVCTS1+u2i+WlArYcDaEWYQRhB5byWbufQ4ADoI2aXQGxjox/c0idKEAIdqlCD9kVuBpXMjVIpX4/w4dARX24HomMvDOksbE0EoM4XJ4AHenfu2uw1C+sJIxyB1ALvZn8fqvvh6/9z+/h/KWW7D/z96IhS98IVQfDONmy86sEQngJRTdZOBHiULZ6FKXf/jrPkQxXgfuBCsXZ/GEK0eBo1ZINXBD2PfGP0f/6qst8LOnWbVENwBMQmmRBoyuw+5Fg4EnL6iKNy1TAjV5J6aJGri0DNx4sCeCWbQyYLNHZiYAwRh4pcQxAfjGKw/iLe/UoP/aaH8vCDVZfVe406hlZM7DG2+KEopsfwC9hOKZpDAUfCFcfqqy+Aw/eLv73ksoEpiZGt231hpyGQDnVSEBu0IZKJe8892PRwBfTkJxlu85hLnbeuHviR22LK0uCIWXybI4ie0v4iSm8xx6RQycrbwACK3Dc6Q9A19Bk5HjseMGcCHEegCPBfAPAEBEJRHNrNK4RqwatksoZAxIWwbuJZRxMbRSSmDqNAhhKxP22HOSVwLDjEkXRDCuiUQsnDVGAw/1I2AbHINJEc2kH6QMvFIjXwOwoVgPKEuQl1AYA/cMl0szTQYuSYe4bGl0APDBYB4H+gdwsH8wNKKlqgrL1hC/zp63ZlncXmCvTOtOnJj283LHDpz57dhxxsdee42cA0EioXz5stZrUitgUJdQU87D31/C4X/6Jyx84YsQoGQyHAfgxunRpmW5bXjDCp9AohQGyCHYrCQZa82lRG0K68SsdWTgAKqG78ATC9FgY4Uk6B5j4DmOCcDzGe/gt89eX2hosrnxSls/S62Q9FoVztko83YA9xKK18Df+yaN3/yYGxMbf6laSIh3YkpgZmIUAG0iT/oZj0RpMnCtgMrRYv39zyAv0+eI77dp675+HfZ/J+o4D/jO39rjFYTO9z5g/5sRkZpJKEaqWELYfy8dQeGJOw4jwjvPneEBwI8hLPQY7EQY+HkADgD4JyHEd4QQ7xZCjHSQE0L8shDiciHE5QcOHDjug/mO2qOV3SLrqXxpl5bsLgBQUgHTWwFYAO8ynC/KyL4By8BrQxhW/Vhm1UsoIxq48T8KceYhm6ulpROtgIFrKaz0st668bkG7h8eyRm4e3g9gOeokXkGTjH1uT9YRKlLDPWQAXgdnHY6SD9cn07H5iWU5DoTQSJl4Dt+5VdRLLFOOSKOG0ilhOBoJkDfsL31mtQK2FnOoXfGe+3+luy1NYM+hEknw2UZuJucm2YMr+DoolBENsLAOWuVUkCLHIMcyGsDaBNCz8rahGbBQGTAorEq68Bg8XSNN/6MxD8+SWJmEiMRFstaAxxKQaj658WvpSMmTSemjGGETZNMQiGyoXWPvI5c6Yr4QFTZaPVBzsCX2rKTq2oEeKo9sTNRM8RTS6CiAURmYCqJ3K3WVsLAR2zehcfmhI7X+ZlTWivgz35W4QsPFBj0OjCNOPBaeQbOa6HYTEz/zgutA4B7Bj6uiuaJ2okAeAbgoQDeTkQPAbAI4LXNjYjonUT0cCJ6+JYtW477YGbYLqEQmeD5jwA+5uWFAKYcgDech5mxzMcvgyQRjCEM+geZhOKWlYsNCcUzcAmQo/DLMXDD6C0HnRf8fwr9aTswI4BdFzwfanoaWjQZuD0el1BCJ5jghKqDpCHZwzMYLqEyVQrgdazoGMMIxzcUmHAdb0QC4IBoaJDNlVAAcP+zEOnBdEdCUvuZmw/TFFIDIGgXzUP9ASSlDDyr2wHQSiiNJbCPmeblcv33KsOQ8kQXEs1ltShcIg9B6riU5k5Mv39/jhz0eoJQgHDFvSU+9XBpAfBYALzxvBsBVEsXxL89gDcmXAhAZDnaTHkN11DiiF78+tdZ1JeTUJqrSAEIQRCSsHsz8IkfTq+XaYnsqPfsQbV3L5Yuv3zEh6UlYPQQKifoUsbM6gaAr8TxWw5d1I0gFH6VIeL7WEvg5m0C73iaQmVGGbiWDoOSMMIaoLh6h2G+FPes3BWdmDsB7CSib7i//x0W0G8X85770bRkY6NQBFDCofIYBn7zoT4wdSoAoMpHX5BhhuC0sBo4oVw6xBodLC+hQAgosm9mAPA2Bt7QEMPxc0A7h4qWwK7zno2u6mCxlwK4Z2eKAFZX147FDUWRDgCuGKMZlH1UpkKpS9SHPYBXQQMPEsoy1fAmKw/g/IWJEoqfZPPTT09+Zxrf+1jsMosPoiCAFtIVjrda8QncwCx6Bj6w0kvCwNsB0Lji+zzT1t8DY3TEBHc9RYhCYTtpdEMn2cHA1abOBlWQUMoGgO88Yp3nwojEed0VBpLFoJI4xiV3g3lOqy5MvSlMTEY65siB01igl2MAPIQRGkpKNwyvvyE8v4engdmJNgmFICQghQ2jfO8TVNLdvbWZxsICbnrST+DW57+glYHregCZG9QDBlnNiLQV6MyLbpmtJKHjWbJgHYDYra21GolC0U5C4aG8RttcCf9bYTTLJ3Cf3dU0cCLaC2CHEOI+7qMnALhmVUbVYmachGIsAzcSqFwij9DtL29ZG6AzDeQTqFue22HOANxFKwz7DMDrGIWSn302bnAV31QAcEC4HlwkbTYcOQb+T0+UeNvTZHIuQKPwjxDQmatBIQGDDB2ZY6EL6DkWRuiuAZdQmiFUknSQUCZZ559B1UelUwZuO883olCWqUc95eK0k+gUQpBQvDTeBPCgK/ufuevJgVcAoMYKx1ut2AQuNPRS7DwuGgw8bwC4NoTFYY26xYkZztkwBu71fJmNALhoArhj4ACQ9yumgacAPrtoQ1UFpXJdRxhUzukIWGA9JgbeeN43ZBMAqZhOLxzwlGn5XxKAUGMA3N3EEYev0UED/8efkHjzT6sguwVzGrhgbItHc7QGGdQ14FZsptEZR0ugrIdQBaHu8+SxdBcrAfCqtBclk4yBI5IV/j4Oa5VIYICVWGTDiWkZOIXrIIxhvhTv97jrMXAAeAWAfxVCfA/AgwH8yQmPaIz5rjttiTzQNqusouUBPM8y62mcOjWpWeyXvBzAASuhlOV8qGfsowfM0hLUxg14689l6BeM7QlAUNyxlpGBl0wrXFyMYNys3FY7zU1LgChDVxVY6iDpCehf7okhUPgIkcYSLSMdolDWDWKnkWE5RGlKlLpMNHDtWHXtGAcNxjPwdbXd32IZgVYQKyfrhiQnU5eIZ5VeQvEaOAdeQcD2/T9oPS5n4Ao1jEu+Mv0BhEmvZVNC+efLbsGT3vxlJwlQcr28dmlYz88gBykL4ByAZEM3FrJjS6oCyMvYBMBGoYxKKNKkEkoXhJKiWGybQMTxH3n/B8K5tlmTrQspQaTCc+01cM7Afc2WsQw8tDCkUP7XnoOBdkA7NyEwOykgG3GyJASEBG8dmqSkmxYAT7JEG2CnJVDq0jLwPkseazLwFYTqGeejymTUwGtifho2zn4lRxi4kaMAbrOV43MkjI7hl77mzO0koZxQQwci+i6Ah6/OUJa30OJqGSemcQy8NQEEQObilmlqK0hER9lSB5gaAMNcpBIKEYbVUshS8y+KWVyEnJiAFhb8owYuAFb9rFYAeZCRcaKYX5qBf12bxe81A/CaMnRVB0sdAc1aSnkG/lfvsgPb3/9rmNm5ZD8KMQplmgE4tMFStYTa1KiPHLbXo66DRBWkn2UY+IRn4BwkmQYewKfx0PqJNdzDMQA+MebQmpUqyESF33zPZXgDgD2HbwFACRNsNoPYOzvA3rkB1vVyG4nCXsBwzqaGrmp86D//Dxa22tAzITMMqcnAU96TqSww6rxEZOA1gTiK1XEFUjLc7ArCYeoCsJMoIX3O977hDRhc/X1s+8M/HHNhGoRFKAByhIHnJZdQXJboOA081MCnNCrEaNSln+zd4Rq6ZiXsjeKfJ8XAWoC23BVTzWk4qoGXpoTMCHrIZ4XlJZQyGw1V9GpJJhiAo31FPNQS1OC4/hnkgQjBickYeFjJ+UYlKyzpfKx20nTkoXJMgSNjLJsQCGxHjWHg/pqW3VMABuCLXQvgZR5XZd6JWdaDCAaGSSibNsGAUgAXAkTxhdAKgTlpyR5iVoWwufwMYCIBA4Wu6qLfAWoO4I0H99Db3wG15ZTkM0k6OKImquiQzGtgqbbMmTsxtY/tdmyKWnoJevP1lHmnFcEklFj8K31oZUNCCQycSygOwBc7aTNcA6fZut9mog59D3cd3I4OGgzcWEYmXaahIYIhQGsnobDJJUTeaA0zcwRT9QBTO+01U14DZ2AkG7HTmcgDgCsTScDQFbMKVtcgYyCQNj3owqCkCQQAb0keGd7UvioBWvRyqQCSgU1qac8xYeBkWbG/Pk3zqzdtCJo3gqg16so/K/5w3Blj38POdN2QUAQQMnBHwWx4bWz9Ro3iVloClakgc5Mcp5nIwwHcwFZ45AA+yGMBsYI5MevD1wGOUvFnaFBmIQ7cAPjsQwU2lAYPul40WgtaJyYH8Cdf/2XMf05FAB9T2uFE7aRIpf/+rlnMzFvQadPAhdPAtVunyTGTXe1Od27Lw5KXxPey5E5MwC4fKz2MqbWegS8tQfZ60LBAq5gTEyYCeK1iGKFmDJwvF0cAnL10hhS6WQdLBWwVNmdt+miTtXAGzi1nk9v8/l1hPF5C8UtGy8DbJ8LCF+rhuydiDNx9ZDS0FEH79xq4agJ4CwOfaQSkGhmXr/b8KnRd5Eynts9FczXDOzN5jCu11Sd5Mk0I+TRmxEEtstwCOLdG9mKmVKJpRwaeauBCm8A++Vg70qDGdPi7DcD14cP4xs2H8Jefub7lyyaAS6uBMyemloBkAC5rJ9Vk7bUcuAbOW82R0aGaop8gsgYDJylw2kPncNZjjrDP2AYtDHz4AzZBDdIlmFYCpalHy04sw8CHRSqHAHal7fHjsNiAy5/+eQAuccofi0sotQz3r1bAPzxZYdD1YYRcA9c2byTkkGi85Dsfxc5ff0VMsLudGPhJAeB/+ZnrsfewL2YFJFMvkVsOIlQjHMfAfdDETfd6ERZldBotuVZonIELItSaUDEGHjTwQR+y10Mt7HE5gBsmoVQyhhFyCYVWwsAFoJGhm/XQ7wDEY89bALxuNEhWjIFz46BbBQmlCvqhr4JIZQWZtV/Htn2BAF6NkIgAbdCfyrBnkz1xOSKhmOScAcucu9VoBp8RKQPPRR0yQjsVWjvBVCwCyGvbpXbNnnkcL5NQRgBcKVRIy4o2Y6czqTBkHdJ96FilDQx/xbQOk5Zh17YHwia9NTnXptUzM/i5d34db/3CTSPfjfh8vITiAVy4yY8lZOWla/eWR1TkT4v0zzoRKg64gwVUTl7zYKeaGjiAfrYu/YwrHy1hhHo+SoC6EXqrBTAkjX/dlD4UC0bj0e97NA72D9rtmPNzmI8C+DC+8iCh0JebAMQVYC0BXjNhsRSBgUvG0UYkFGMAstevlkgzbf2kck8G8E6mWFo4kDBDJ6GQRGi4ME4Drxy1ObQ4TJhRYODMiSlh3MMbGbiXAKg/gOh1LQNvALg28YWoVYyn5hKKYEtSwx7+juqgcn8bBVRaoJP1sNQBsLCEa+97P8xdeil0W4hcY1mqSCOjUabjl5RZTUFrNmUZJJSaAbho7WkWjQM4q18EaedU29tTigAMzQxS0jUM0klsyr27M5OjziOSCP1JJaogoRSVi8hpToYDzsDd8p1cdiEvssUklGapYCVzACJl0g0JpWgwcO7ETH5XtwP4BDQquRmZjx1uAXDDE7ma1ogDH2hYJyZfzclYshewzm8SAtfuj5McXxX4ybg2FJyWAEDXfwa6AeBZc1ksgPc+8pN4wODdcfwMaaoWeY4YaOtG6K2WQCUE+p30wiyRwVw5hy/u+KLdBwPJQQuAc9mKkGFY2Q0CgDcJgClCkk+IRBPUUg/cMnA4ktFl7/fBI9+3+6rjamQ17aQA8G4uQ0icMkiSKsgY+KwyLdLSmE2rCPjObUdwaKFMWE6fAbj/fO/kIcx1PhWKaAEIjNEMBpDdnnViyjhhCCGgdXyTKxUZeMiGQxrOxTs+9bIeKoeCRhAqEuhmk8mDO/PvHx6piQ6MRudIGBQtupu/NtPstI587b9x6Fdeb4/rJZSqPioDz5PdxzhwkANM7Wtjp2MME6z7vq1oflNCIWH/FzRwWYeMUM/Am514q0EfRIRv33YkbcZMSBhRqJ1NejRJy9fJYNEIzSiUTIlWAC81xVoo7nz9cYmBRQGgzKbxsZ178PzZuVYAb5oZDnHTk34Cc5/+zMiKbN9CBVB0YpJn4Iz5FkOCEYR//iYrdcDG5KNQjCHUnIGXgxGHd9EoV0kCmJpajwXY2sxPP//pSV3tNg2cR9mMMHAFlCLtWrTk3BJnHCSs+5dLQZQ6WwdFw3GKFKCNyDCoDDqyQNmbSL73E6mmPDihQ2SVsB2WkpLOWgMUr/NUn3Waus05Z814n9KJ2EkC4CpUR1MNBk7MiamdT7atmzkAHF7S+Km3fQ1fvuFA8pL4mPAyEwEEBAGLk/+JmmUkSkNWv65ryBYGTkKgppSBhw7aIk4OPCbUKMJnHyzwlfsLdLNuAHCSQGWAbj5hGbgztWFDYKHLmSKNvIWBe9DlAK72HIzj8S99WSeg1H4M9gchVCO0DNylXfPzdtcpOD+NzaBtA6yZqQYD9wDuo1CaGjiN7qce9vHt247gp9/2NVy9KzJYY1IN3DN3rTVoMc0C9ZmKxCJPZEMD37a+lwK4G0czjFAwCQV8chQ5RFbgnLrGqw/PtEooAPD/XfE+XPox2/yguu02VDt2oP/tK0YkFNtGTI5o4AkDHxKMFCF3AmgAOItCqZk0YbKJ6MT0l6RH+KtnxetDQmDTpH0P/u0JX8Gf/OifpFE8bXIC753qNPDakxkJlEKEUE3AArQwNhLr7A9ehnr/ftsZx1mbhGKYVq9FgaVSo8g6KIuOO579Lg8AXoQJ3O9K+P/mz4/RoQ66kcA0C8E9d8ZKQ+qeLaFIZIY5wBgFvfBjO3HOLTVIIoT8tLbiQoxS2T3TT1NkeYxqCCO0plkEhzAmFt3pdmFc66rA+IVEpdsB/OGdTXily6bk0QCkgHf9pMJbn6HQm92NyqX1krAA0M0nsMSYh1q/fuwExU0ag7zBwPus0tr0UvskEF5ibXDTxMqDlARFhi0CA7dOzCBLNbXaY2DgRjgJxcswLAqlqP1z0dj9oI+5gb0G/l/A6eFcQnHHH5RV6muAl1CwLAP/u+c9tF1CaToxa1ZHhM1+RF1kTouWaJ/QAOCJO66w4zeE8rbbAADlzl2xfZg/PxeFEnwuwk5SqorbFaX9XIMDeDwwLyfLma3Oe6Euj2esPSJcdj+JmQl/ogIThf1yUBsbnSXT67Cc+RwEL3nUEpiTEgNGZAYFcN+d8XyqXbuSPqdVG4DzLEuRoV/W6KgOhll0VAJg8eE5RNbQVTwVb8SB+8QoI4DpfpzwznIAXujxeRUnYicFgHdzFRxy0gBgzNJXyiMhUDsJZRzAeYDvZDJhOZ51KucMBeJ9qnljA23iUq9rnybDWCGERGniU2ajUOzve0Jiwjv5qtHlOwD0dIXK1f800maOdvOpIPEAgFy/bqTBQptJaOSNKJQyj5PNOqcU1CMsJV4Y7phbiQlEAOcMPIRmNu+L9gx89IRGnJhOirlgL/DqD2soUbOqiEC3xAiA18N+KIdaMp1Yj2Hg/WEJNBpWhzBEt5Q2wEgmZpHJRH7zYFU2nJiCSSg8+UWjAyos+onR0xgxbQzKW24FYIFrpMmJZ+B+NeU1cDaBFpUDcBHPxSQSSnRiJgBOCuILX8NNW6PzvxdqX/sfAz0P4O5ZTybpo5QJ8BFVocSBBPZmWSKh9AtggmFitWtXkoJftwC4JydGAFAdLJUaHdWxK29EAH/y4hJ+7cgMaupC5U0Ad/+ORKHY77QEphiAezI5koC4SnZSAHgnj07MzABKjOpJvQGBnLCYjQNw9xL6Vlfhc/fCKcOjUOy/miW0SEMhMYc6rvtPcgUltI5oq6UIN1rKSOIk71PIXuQuEUrvUJPAsDbodqawxDRw4Vp0Hc0kEbKG7lYWFCSUdW4eGmG6jIWVR5FQvPn6EH7ZLQi4+A2fwdKgtAx7DAMnYwKzblrTiek1cAD44RsIypTo1vHejANw77wsm518eC2LwMBrYD51FmYqZeBGAko0XmoA56+7f5C6UidmqoF7CYXvokYPZWdz+PuoAF7XgYFXO3eOSCjaNzZxh/ZRKE0j6cHe/S5jq4xQCwWJE7PccRjy5h344oPiDnu+ql9g/ALdzAO4CccK53e0tHLn5OQMvBIi1JvxRbS4Vbt2JWGEdU4tDFyE/UF0sFRpFKrA0Etobvvzyhovm5lDTRlUnr4E0t0dHntPZCAQGXi3GgWgcaHNJ2onBYB3c5kwcEWjAL75iGVJy83tng1V3rnmzOubykSQuHAXYethQl3GaV4YCpIIdSIDj6ZQsZRo7jQRgiDdrJCUOmXb9EwsSQlhWWOnmE4YOJXliiQUhVENvCpsHPjL/kvjJZ+xO5kdibeOJ9T0yo+zWgG8FoqfYPYeWQzaNdBg4MbYjLUxEsoFqtGpnO0HALpUxrK2sJM2NaBPDwchfLBKAJwSx59/sQdlBcE6EQEILccogBNsY5CG/f7D3oGBY4iBgdepE1OyOHByxdRqCQxpElVnIzvoyO7T8yprlLdaBm4WFtBbTB8IAwUlWWd6mUY7eSOIZHx88pZkkE1djc8ffGvCwOt99vrccEbcthfah/kfC3Rdje3AwLlk2ewU37z/DQD3+20LNgg/2bEjSaU3WfzdpQ8T+NpbngXj2HStACEL9B0DH3pg94k4jsZVyJA1E50CGeFRKCbEYIbkKQHMxT4SY6tjnqidFABuwwidA0zbJI6m5bUFaP+itWEcB3D+QM1P2s9nJqNe+0M3Ev7m73XSv1DqyMBN4ZbWiX4uUTsAlyy1FrAMXLljpgw8btNjNYW9hNIrphMnpinLlUkoxiBrTHR1bhn4/W+1O1jojtZyTgB8hU9HlQkIihmiQSasdcKckwYU9RC+dnab5vvHcweTv01ju44pgwYe9tlcMg8HASsqd83/JHsXXoP3JKVv/e+GVQU0nJh5AHDh/m0H8EyKoNGSBL63cwYf/vbOEQklJIA4QNUSWBKTGBTHAuAlqttuC7VmNhwxYeUG2HyIQslk0mmyUT/OmjkxOYALo9E76724fvHzaXGpejR2v9tg4EJY2RMA+h7A+b4bYY/NlZ4cusJqjIEDCBOkLfsc93d4CvjqFR9NqnzqPJb1ne8JTG0+LYyhVoAUXSyVtWXg/jjunCr/nmYdqAaA+2uUsVUEaRMkFI8H8z2sqLzxidpJAeBNBp63MHAAMKQigLecWQRwSsDgqgsLvOWZEp/8ITECJpol3XAGbpzGlzABkaE0FsB7DQAXIEhln0Be6tQ0GHg4vrDL/k5nOnFiUlWNpNK3mSQzcp0os1mOpziVoKhTxxWApIVU20vfZnWGBMC9DkW1bkgo7DeDOauBtyzvjaDR7D6MAvhEPYTm2zUrxw0GIXywcgTgedkX8WJ5aaphesZc1RjOpxNH5jMVvYQiXGOQhikpQqYgSeCj37HhY+un4opMaBMr8flluwL6cgJCRhQTR5mh67KCnp1F9yLbrFdSCoJaZMiY89hIjMTIA3ZSMkzL4SBbEQsv5GGEDnz5s9Hz7dfC6lEEAA/d2tmtaUaheAD1GrdwDFxnKbP3USjDPN3f3o3AlhlKJ5osrgRrBazvbkwAHKqLa/fMY2kgsFum7QRvlGfZXRSdkaxbT7hy3qWKtB2OYJPGRDrJKZPGqa+WnRwAzhi4BJCb0WpH739mB4SUdTTN65FlI7xLQeJrF9nKY83fbfi+zVa0abgUnJg+9DABcKlQGbtuSuQQAGLuNsgwezMHVsJkTAyQFMCw1lD5JDSrXT5Yml+hhGKgmIRSS0AowpmH4k0vaoAaD6hudCdZiWklANZSzb84pOskfFIx2aJenIfQBloB1ADrMseIzt+MVunoEr16iKUexpoZLEI7oK4ay3Y+CXowKssa5eJMsp30AC4igDedmICNBS+dRktKYLZf4YwNPdx32/p4TG1CCJ5wjppa2pA2JVf+KlJVwvT7yFiDFL6SMlC261DDiTmyH5Fq4IZnk1J05pZVKmcBDQA3KQOHlExC8Ro4Y+BcfkBkqh7ApSu6pRvM2DPwYYYEwHdvFtgyC5T9OGbKTAxEUEC/nAhkpVaAUl3cdngJ1+0uA4D7czoibVkDmRUjTZ9NyztMtnluwsAXO6MEiMrVj0Q5KQC8wxg4ADxy1w3J91deKHDbWXkiobRmswmvTaYSimIlYJvc5z5fmcF/XyxwyzbnxHQMfFEOR44jhMKQuhBE6JJJZ2AByMqGfvBEI854u8SiYNw4kXXRAfC+52/GMAO+u/vyFTkxRYOB1wqQLT8sG+t13l5qJRKKgXUWCaROTMA+2Bw4+HnXi7PIBhqDYvReVUXiGrDHaWjgualQmDpp2TVSu3nQDxKKx+vBTIbhbJZGoQTNWsMszif78BqoB6BxEooSIrJgKTDXr7Gulycx41IbVM7x6rNcawVoWUDxa30UCeXIwVnAGMz24v3kAK5FjpqtMm0YYYsGLkUShcK7s2c8q7EcLWXL76t2kVc8CqUpoSQ96XR67T3QeQBXQ5+tmh5LK4FSudUG292uzbb94ODW7eGzhc0RwCsFXPK1wygR/Q5S2Zl/sP+pLBPTO0mdozIrRlZbnsUXXAZ15WQJLCS1EKMRXoPRifBE7aQAcM7AAeDlV3wp+b6WAIyAFFlS/6Fpnm3YBAv/GaDc+vKxdBp+9YEvG/ndty6yNZ6FIRjXh3EesUysN6FyVMjxG0dm8bSFpYYGbqAeNbpv7vnvNcIYh7UBsg66ZHDF+YQD6wFU9criwMkkqfRNAF/oAp98uAj1v8N4OAOX7dcx2V7ZpbglIG6V5A5T1IvIqc8YePxd3Z9H1q9tnZdmCnM++mByJg9YBq5Ipw7eBoAbFkbobfunTsXNl54KXgjJT1q1roF+molZ5O4ADAzHSSiVB3AhMDeosK6bJXVThKbAwJXMLPuWgJY5ZHIflr/ob/ywjQf/8L5Ph88SCQUKtYm9OY0Yvcb+XDgDJzaLTOnoCxi2ADhnl0tmOvlMSIlcSesXCGGEYxi4xCiAOwZumJ8AAJ6ysIhB4SWUuL/H/Ohz7XbbrWP3Nb+gcORsk0gos/2JQJa0AlRmAZzKLXhIZ1PYDgBqL/llHUg3we3ZCNxvWGKju6h5FeUSIq+BR8fxsBgNArjHMnAbBz6edtqZTiHL8qj7tbwDmjkxwZxSC2QL72wrzsIvXfwrI7/dv8llsmkKDRpe9e0/HDmOkDmGKPDS2Tk8cDhMAXzdNsjzf2xkTNxx1KP4UEgSDsC76BJhXg9RZTYRZ0VOTKKkGmGtInuYmQR+8VUZ3vMklQA2AGj2Ems1HsBDswDFaqg3GPjp1W0wgkJlu2TlsTiPbKDR744y8Log7JeN/qkNBl7oCpkxWOyyZX9jP8Q08Ka1MXAyBopl0RkBKMegiUko7U5MidpPxgqY61dY38uhGIBLJqFIpaAkYTNpGFkgkwJ9n8Xbcs05mytnrBNjlslHnIHXcDXP/TMuRauEAoFQAA5IGTgnCWW1PIAvOgDnEgpg31svoSSJUAzApYwr1WFhCVXm9GUv6RQgvHB2Ds9YWES/A5t4w65RdqZtkyhvs42RjQS6hhIAXyynYxihAmQWL95kUYTPgZSBS6nwuhcpvOnnBf7xnBfgoLFdporahGvQ5sRsq8VCw1Hp90TtJAFweVQAJ1LIZRYkkDYJ5ZH3tqBQaUoYwfb6bADAzKYfhmSzqLelCQQG7iUUn3nniUAtgVzmqByjUUhnYJl3IHNGF501nUEhYoO8hNKxAG5K1BLINK1MAycTYucBv1S3B7v2rHiCIwychZXVY7RTIAKGVjYcjXgceEiVt/vP3V3hDFwvLSD3EkrjGHUh8NzJd+Oas1iIW0MDL0wNSQaLzMHbvG9mOIiF9ZvGNXD3Yl8ktmNqKdVnM5VKKEYASjRCdwAoJYKWK43AXL+yEgrL5JucrzDziy+32+c5lCR0hYGRBZQQmIWL6Wx5djnDLlyRrsVujLYaJgw8jZCyNTpaJBSRhhFyAOckoWph4Dz5ak5vsMfz5MPtpptLDOpRCSWpXy5ivHYt7TX0AO6VzY2k8duHZzBhCHs2CuzbaPMhvE1+488w1wOKHQfDOCYoSigkCUtVN0zUtQQW2Iw36d7LMI4QhVJACoUfnC6wMCFQ/ehr0JdWs+tULM7c2J6YBIQ+pIM8ff/3bpEjTvbVsJMCwDuZWjaTyQO4VNmyGvh5W2LNZf9ACQDkmE+hCsgWZ5KCZTDSIBSn8vffP5e1ApSw4tznt74Uf1X9bKqB5x2oopvsd7EDLLGElS6TUKQHcCHQI4EatudjVq8MwAVMSIcG7DXqzdpz++758ZgcsAEkUQnLMXCvG2oJQFjxxDNwjxWGJBYygTNdFEOigfcXkQ8MBp02Bi4wqAz+8PkTeLOrsTGigesayhgs8Ap1TQmlHMbehA3jTkzv3KbaJA0ASABF6FgTV2xKtTFwERh4UQGzLQycm1KZ69wOkCogpcAl9ZPtMVoe3qQF29A666bXnxKAfcikuAp5GKv/d6yEwjOK2GSTMvDRHqU9mFD06YixEkQANM7Ay1EA5wwcTEKplXVo5i7l32R+BWG/7xDhT58t8Z4nyWSSWzecx+7NQLYU/VIdEyehTAL9ykQAV8AMa4486d5Ln6dR6ifj/1YvAIpJKPZ+5JmEcfc+r5mTmFwGoIhjHRSRGOzZCLzzhZMozjpr5DqeqJ0UAL4SBm4og2IA3gY8XI8MGXIE+CYMuSwgxCigKLKOFuvE7KPMIiNT3jGigMyFgn3mlBfhKn1BsuyVWQcqj8u2a84CfuG3Mgw68aVJJRTgmj1zuORrt6AH/+AJqBVLKKMauHAnxgG8bsgB/O+ki1DDqgYDFyRCMauQxUoSC0riHAfgnIFfcdvlyIcGw85oFIoubAKIRFpRj2vcuamhyIRSwMBoDDsNh2MZOG+K4VcmVT+9FkYAuQdw5sQULZmYigF4XgGLpca6bj4SR+wty3LbN1IQjLISytv1/8KFg0vSCobOkibIrkLmpg3bArBXCQP3AB4BsNnbEfAORO5ljud1zo4KH/zTGp2SULYw8AnEtmtzegueNzsPP4dIwQC8jYGza09SRO1ZWWKQuz6v5KNinKxXkF05K6TPwjpjMN+LfxsBbK2jBk7KFeVS8T2aL+P2U66MQS4J337GJzGQP4R/0j+JTiZDxBEBKJQM70enimzb98TkuMGdmFq256Wshp0kAK7GNmkAXOMEypCp2IWkjYHzVljC3UwJBAAvZAEhRACgxQ7wOy9WUCRCKVPTH4S41Z6JkkctASUskx/WGpWZSGKsZdHF5TuiYyhGn3ANHAkDB4A/+I+r0XO3qVaOga8okYdGJJSrfrSL9zyTcGSaMfDGiqNm7HJQjMpJ3hIAd9dMugnDX7+SBLQAzqlG2+F97ftfccfAiGRgCmkBXKiURbLtOi6emleoGxlfWa5MQnEvpVlKrwUJFoXiNXBpHZBNU0KgcoCTOQa5vpdBjev6nhWucztAquOcmAIl8talNmfgEw5Qs95kYOD8+0o0GbhIolD8ttaJySSUZt0PAKcfAup6NHqix9yfB8wmvO7wEXT9BO4jNZSMNWgSBs4BXIZ3wTNwP6Ig6bjvu2GFF6PNtADWk0mkyNcePozHL1ZRQlH+3sX36JVPvDhsP1lYYlUqQLnJFLAr/0xEAM+VQOVyOTITVw5kKAC4X8ENc1Z7RQI0prvVidpJAeCdbHkGrgMDjy9LG4BnjGEktVBc8k03szOxXz5+7JES27cJK6GoGAfu2dC0iWFytQJyd/xhbUCmm0ooRRe/87HY9y88cEzY7Wa91rTznogPnlqhBi5gUiemBPrdDm66V4PtNp2YDJz6xcokFIJ0k0oaB65hVy7nMgnFCLvV2fvtRpaBp/vWHYFBbSBFOiHz7bpunxWTDvg9rZT1+o+VUHhMul8WtwC4lMriKdPAsxYJRamUgQPAut4yDLzo2J6RkgDVCaDRZrWMyS4AMOmcimpiMvFFhO1dC7hwvWRaDdBPeiRFWu62WXkPwLo+oXLH4yvKSTKhoOIRY4MAgjPbMWYlRWykwQGcN0OQae9OPhGJFgYOAAMzEQFcAlMmBfApMgBUzAj2QOrOVUvChWdsxv952v0AANOF9T0MlYDIcig31iKToXmHDTIR0Flc8nEJRTinsa81NGBRKJaB35MBPFdjS8QCcP0wcxTZ8mGESScV/sL0T0d/5/NwwboH26/cx/4GSMMZeD+wnmkTE4K0shIM4OLMTTd54FXRS0K24gPHGHg+GfbHb8yEc5pVytZRWWkqvWLp4rUCaipCqcx/Mqfh0h27kIm0XkrN5IGSpSM3rY2BNyUUIazv4ByX4pwZ+4LOTAFnH3B6Y4sGPnNGBm0IUqQ1rbkVjoFznZ4DeK0AKquxhe8EEQ5PAW97msTuLZaBTbkFki/wZSNOpMtqZBp4CwPPpEBZuG3cZ+u6OVQ+TkIprIQiAWQFpGifiPw4uBPTA3je7cUWfCwJp4aXffz+0kxMvy+CCKsPYAyALyICOAOkaTLwIQO7yUUMuSFk7sBSCoRmVSy6KWXgKkw+tUodf+SSgTyA+2cXpJIVUY5UhpPShlL6j3w2RO3ICkkAMg+T5rSaRKWAobKhwL7kRSeTCQMHkAB46sS019mXax7mccLTArcTfJ8sAJ4J9IbLaOAKMMiQZemSu2kJG+IdQiBRzz8QRePF9DfIa7HKEHR/iTFwCoBbS6BQ9uaW2gA6ZeCy00scRgGQ2ECLfCqyGAY8XRnDnJSmZSczb/PFAoyKSSlaARV1UBBBEuH+5z0V2yqDQqbp9n75DQDIl9HA/cusAEC6VPqUgfsuJT7VWmm7v4PrgLMP2E3KggJT3H4a8IpfVTh8gauNzeL6eTceAOg4bbWW8UXhk02tAOh6LAPPtA1f+9IDJYybIDcuAGVGIbachI1plkJERyfGJPJIgRvPzfGhRwtc8Xi7kls/kUNl7RKKKnoQGdmysqoTWB8wGoRiJELJUwCYqq1TMb/m/XEZz52YTsoLE4EUSWprYPMiZeBtEsqmBYQmxhzAJ4kCA5/Bejxy8FZU7hpl7iFWAjEOn50fX02TyuL9UykD9wBOIwAenwstATzm1eALSUk2ZNg/L7EKYSRbUAWU2++U6uHfHifx5QdIC+wqArgMGrjbRxYDEeIqOkooCQP30tw9nYEffM1rcOaB8ahlBFCbHFm2fBRK1ubERLw5srGMDQBOPpEHqPbsxpxzmHAGbp2Y9s0fVnYJx5eNqjOZFs93+xbsdc07k62fd500UytADgYrYuD9bACSMXqglgID6qFLhIIIprMBB7ABqqHHaF6TYwUM3JYfkC6EkAE4kQNwCg+ZMvZeHVonAlOpWBihEcC+jSKUa1VCJU5MxQZTuAQRLaN8QELgL14I/OnP2mU51fVYDbwo47nlTtfctAAsTqbhd1LYyn6JBt4SWZJLCSElPvRYhdI5ptd18+SZ45blXWx96Cy2PHAOyFIJpcnAtWxn4B3JYpEZw63QiCeXIpGfhkFCQeIwvfnIqNa9aZ5Cv1Qum00ZAwUKjsw92ByO4RlsJiVqL5fwjkacgWdZlH9ko0ysm1AsexdBQiHKktIG6ExZOcrvRhrUyGI0VJC//IQAQOXI3edTahL/9cMSN26TgEollMxLKCGkpRucl1EDd2GEHMDzyPiNBOp7MoD3Ljh92e+1FNDIkyiUtljaJAolAWvHHMYCuGXgRQ1UN9yE61wcdc/EJJZaAYVbXg0dO9zo8PMLDxR433cOpSF6LRJKXkzjUw8T+NyDBb750Hhrei72tFKArPUKMzEpae5cK2DgGHhOgFEF9tHGkYa0FWOXKm+fCO1YIpuJk2HcVyYRmk37y+odPwfXsf2wVHoPqNPkAVwm3/EwRA7gdQBcgd2nAt+5wOmqtR4P4FX0X0x147K4P8nDC13CjRAJYLRlYkopIByCHVqwq5qNEzkU6/rOLev00NtcobuhBrLOspmYRjQ0cOdU7MiYUYw8BfAik/EZFyJ50z2AGyFg2LEGLZdq03x0YiYSijHIqNFWz12jnrs+UiLWDOPdfnTKwGP4YXyuALYiEAJQOTLYukUglUprxVQIAfSbGxE18OAkZYDKgbor7YopIwKkSpyYTQYuZBEm0zDxBCemCE7MQS4CA9cSqFfCuo7DTgoA3zj5NQDA3GT7RdASqKhAzpxLbcyR1/blPQ593O04Bi5Yg1gQ4fvnuBtvVALgHWWBdug879+7V4Gr7gX86+MlZkuV1J1oc2LKfArDQuCdP6lgugqPv4/VFjuO2dcKkBorA3DTSF1XwIC66BAhB8GIHPtpI/JGpXm//AaAbBkADy+zAiBUZN3OcuFkoMbvjQAOsSgYzsClAF596Ah+mtYDsEDJa1rzCanjMvy0YgwcImR9Wgaux0ooBUuFXjfBarizcyY4CaWhgTdbqnmT7nUSEHjBj5yDU9d1IWW7hJIVMaR0emJiWSfmpx8mRhi4AdCVLJmEaeC/8cSL8Pnf+rFEQuGVBn2nJXITU2SUoxPTxgWCdp2PmgxcUgoggYF7CUWKkAkrOAPnEkrWiROwajBwV/ETSgBuldSRBWBYxrWEjddmDFwh1cC9hKKZpAFVBKmEZI7XHzyMf9l1IDn3IpPB3+H3LmURrkPAGHLTINPAB0XMsdBSQN+TAVxMrMOL/j+JS5/SjlxGAjWNSigff0T6Ukge0sW1P4cyDz5zQ7K9r5Vsw5b8ejvHTW5BsFFniYRSOH3Mh04dWN/BX/yMxPyEwBCdZLkapZJos2UEEiUknnrxNgBA4QrvVJldfq6sIw+QJZEWwAAdTBljvfaqwD7aMKKB10wDz7JlolASJ6Z0xXzi8bpS2EgPgYQdaQl8+4K407pgqyEBvGhuHsI5ezORxTR3IZIJqfAALpnkISMj1E4DH8fAO1VMxLj3aRvC50KkRaCUFLY5gmdvAmFZ3TQZJmOB5z3CZveOjwOPAL5x/XTixOTP5h8+R+KDj5GhNjYATFZDDAugCwrskmd8PurC03HWpon4dsuUgft7x2UhIA0h9bZ5HqGpccrACRIiBXDvgHeXXEkZrz+TeJK68HknMlmRlmCmjhtoZhk4AJtt3XBiWgYefycEoeZRKD7QIEgoZBm9u6daKDx7fgHnufjz2o25k8kg5/mnSKhumEx9fZ4QRsjeZl6vxQigup0iwU8YwIUQSgjxHSHEf67GgNqs35lEv5CYEO31dH2EBQdwCOBff1zhP3+ILd2ydgnlKRdvw4df9ihsnEyXuzUcoJLC7q0Ce7YIZL/3y9BK4LcPHcET53uBddRSoJP7OHAXjWE6GLiHZEhd6zRy+w4PLaO4tYjtcRRiRbfCSSi1Y6ErcWJKkzJ1LYEl6uFXZubwpv0HcdWeJeyjjShkIwqFuAZ+dCemkQLwy1XOwKV7qCUCK7bbA3s3CfzVsyT+5yIBqLRuNYDQEkzJ1CmtEgmFAbhDbRIRUGoJiNqktVDYf3MGXvC2WQLJMaUQbqkt4zjGATgL+8h9nsEYCSWfjFnBm9dPJ05MPq3XyrFk1sV+sioxzHxii/1MKRmY9GTHPbehh59MNHAPkqE8QHiGR2/25nngjFtseI6fRDwDF9Ro7+cOp9zBlEAE8DFOTGRFIDNGITnP/rYu3v0TEgfO6wQGnosCoGyEgXMkk05C4b1q7bjdPZRIolBq97x5wubHXGQytNSLAF6kcfQCloETEjY2KIBMMplv/ALrhGw1GPgrAVy7CvsZa3OuVsHUmFnMCCehZKOJPFwCSBxKjA2/4X9djIeds3FkvzXscQVJbD9H4q9f2IV5xH0BAKfXNSpMJhJKnqUSSqUjIA/J7isw1zCuOI4BxU6+kgQ6mdfmrUYX4oxXUBfeMvDIoGsF9KmLrVrjfmWFD333APZiE1Rjaeez+ABLeo7GwG2CjZdQ4ve518BFusroCOBNUxfjsvtJ/M0zFRSN3isP4JlM7ycH8JwBeHCCQiYSCrROqhFKFlbZYU5MvjKzmbh+QnAAzrqqG4GkRCw3LqF4dtesHV6v7+Elr1TIN20On52yIQVw7sQMWm+W3ogyB3JW8xsqxlN3Cl9B0eu2IgHQAOCsPAAwCuB7N9qqlY/+ho1mGpFQGgx87+l2g/KUrhtSjAPn14ETEJHnUUKRkYEbACrL8ZmHSchcBQAvZO6iUDyrBlBMJWGEAgQNFeqH+2QlHerawEah+KgUF1zggVwHBq5GJRTVCdchJJexRB5vOiOsV3biMxLQd0UnphDiTABPA/Du1RlOu81nHsDdEkcCz/kdlXiDLYBnAS3aUuolC+lKyluOKaZfkys5SVEi8JXZCiIsiokYH6oIeREzMQFgRseXdOD2tehWzv7F46/lgomAL4RExzHwTFkAr9pxo9WksYDn2UKtLAP31tcS+2kjmlnbFaJDr8NS2ZsWdUABITPbiZ5935EEYezLIn7yz8PnJATO7p0a/laIAByAhPzElYYR8he/45e7iv1eRkaoLf1L4sB5XHynis9GppZj4HBRKJGB5+OyKx0AECHoq7IRRniE+pifEMjzOFmfun5dCuDsqQjXpEH6h7l9BiOAx4qDnU6j0to4AG9IKE0NfKELXH326O+MBKYMufcifn/dAzK84lcU+uesc4cV0QfR6PzkTWRF9GFICvHq1vntnKFCBgnl4Rsehnrxwli0TgIoJhIkU8LKIm/5mRzvfbzEwoRj2J6BK+uszJWXUDyAu3+ZhBKKmfnhqyKULSgz4MB6YMv1h62DncuyGSWhm9VdlIG/BcBrsEyqvxDil4UQlwshLj9w4MC4zZa1OSd9TJOP/RUwUjAvM1BSB0Wm4o1tYeBJRAADbTHmMtRw2rMUELBp5VXtkiiIsIheDFGSQOYmGq+BVya+pJ6BL7nyp0HrY2vbI6aHjm8dxxi4klPpb1ZgkizgcQDvMwBf1Ar7aGMLA49IkePoqfQkADgA52pFIayTS0gJdZ8nh8+NBLLuhvC3IgYkbiyeCd1n40XYlFupgSSSlmwpA4+MOQQGCILQJnFiZqy0gKQIXCph4JSQACUFpIyRNrzEbNM8AyeIAA4qa8hy7qfrJ+LkPj01MTaM0D+/81PpvbAAzhzArGRs7pOHmITCH3FfmZB3rQdG6+KUWfrMhcgjaav97TKnYo6tGpWQ2LcphoFmSgQwbHa28SbzPIbjySgtahmjfTIhAwN/zpnPQ3XkkYGBGwEg6yYFgqSwDPzwBoFP/IiEfyo8UJMEEKQxoPalKhoM3DoxUw1cZnl49vdsEviXx0tMH+ijW6ZYQ8iCZCPpLhiFIoR4OoD9RHTFctsR0TuJ6OFE9PAtrAXUsdicuxDTbp6onU7mB6+VBfA8i2FnTSYONJgWB/CW4kFABPBSEAQJGFAobp8DWEBM6TUKyPOYSg8gpOgDETwXuw4IwiHjAA9WnZCsoCADgAtlX5JjZeCZjvHDtQIWGYAvaYV9tCE4GPfbwI8EwAsabTHnLSRcCJu91pRQui7Dk6RIZQQBZN31cZzMQRz8Ce7O/vqDXo2nrf9h+x2iE7OWseYEl1CISSgk4Rg4i06glGeERhMMZHkxM1u4ykooHjB8ck+beQZuhAiALBtsXUvgIzv3oNOJsZQi7411YvrRX3k/hZf/WryOnoEHDdxEAC8Kn4npZRwJ8GeHXS8gXr+qcV6VEgmAc3DNCLj3tlOgWTcrT4R8MSspBEvkae7bfZx30tZvLMIj7icycC+vJRp41knOT7rtQi6FO8HQwNlNYH6V5Jm3bmPgDQkFKg/hrHs2CVx3Jr9vwNX3cpuRiAXECKiFgKHVd2SeCAN/NIBnCCFuAfB+AD8uhPiXVRlVw+bcKNdRzL7jpoVj4Gq0pRpnLdkYJ+Y4ADcuIaIUdlFLIAxcFtyV9b1xqXpcAB8tgazhsCIdAbwMDFyG7ZOBAtgziKnuErExrHDOzWNh4IosgPu6F96J6W3RSBzBtNVHAXzgMRLPfl0GYhp4gZjK/qFHC3zr3nw57RmQgBCZK2YVj9/xlRBlGn9vVyqs0S+JEQmlch/0chVC9kgCffezuQl2okKEl36ik4UoFJKAqClxYvLyuvx4vtm0G1Cc/GELMsmGhNIWBw5EANMAMsfAk33DRSsRAXmUy3hImz3uKANXUuDQuljhbpgJ5KwFX8ZyEpSPSPElk6VMJBQR3hGvI/tVYUOvbzBwHoWiQOhmOThy+knIl2DlYYRNv4FnsaIoWM0WCk5Mfp0VJODCMSs2SQIewLvJw5cJy7aluyDk8grKIsMgB/pTDsB9FEpwYnoGboG2yORI0pbMO9hkXQLYsykNeyQh8G/PAJ77GlcAz19/N7TapAEDq2HHDeBE9DoiOpOIzgXwHABfIKLnr9rImM25+W+dm8GaQGYkQJTZ5IXAwFMHDQBkPNrgGAC8kpbbGQClS2r4WP1juFGclzDwogngjIF7ttMEcH7sPUsqALhgDJxck9VjklAM0K1sM2b/2yHTt0vKAAgMfYy5l0TYC5khgsJ3z5f42CPjWMNYhABJ68TkAD7hK1BIJOyLpAiZj/ZrkS6HAdTuhevmMrz4JIBPPLGDv3+KxPUJ62HdZpSKlewkUJU1/vPK2F1dmTRk0j8C00WUAYRgbEsAeSZcLZQI4G2p9HZ7By6IUSjNRB4tPYC75Cxhqw9OdZh/hp1e0OmZXAgA/Y5NLY+x185ZxldMIgI4f8T9qivIf+4aV43zqlRTQolj0MUmZHnaTkk2GDh3YjYZeC3dtcxzBuBOn4bLeA0ZuVFC8f6RJJEn6yTnJ0AwUGEaJPebYVbgZS9X2H4BhfEBcVIIGriOTsxmFEpJEpvn7H/v3dh4J4X9n1YCimS4vv69KPU9tKXaHCxzmtbRccVNSzvLdrJRBp5o4Ekm5tEllB+VtnpgJazH3YAw9HUhqIAhSphAlhdJJVCqWcqhe4gGecogJLsFu5ZkZOBCBCemFscB4GQddUsdz65EYCIAULpwwXkXonjRxGlYuOnVNk3ZmUIMP5OUrma4VGVaALzjQj6FaEgoMvoK7H5HJZTS/Uc3U5G9C2DQFfj8Q2RasU7EF0XImMgDaWvGzA0i68mRAvgkGfz7rj24YPpMtr/UqZq5WiiGA/gYPVeEbSiwu2YtlFpZ/wlkjgoZtIu73zDBAJyXePWs2v3tZYylrm01Fr53gJFUdvTXRckUQBurv/gMj8ocbQzcSGDx7Cchl3kyVulLUngAFxHApUr3baQdnyw60YfBnZgikglDIkgoFbykwQia6ljfBfy1sGnzXkIJpIQUFnsiFOHKg4Ti3zN/PBZG2NDAF6p4Hgc38IxqACI60SVbWYbmyg0CsRq2KgBORF8ioqevxr7abM5UmNYGG2Ffxu1b02HbiyiD4whwLxoDWMC+TP5PkQB4+wv5GPEDAIDRU8GJ6Rm4dgDOZTaRZcg526yi1hsA3GWXdSsP1HH7/cOMSSiRgVdiChnRMWngeW3DDX3X9lqlE5V/ERacnFEUPVB1SlIfWpEMS3bu9AP4BClA0koX3E9TwEsoAmAATlIgU00Jxd8UN1YSQbqQbMLz9zLJ1hOSvUS2rK1w90VyryqAHA0GJID7lBXANHDZcGIWStooFAYYbV2bAP4cUYwDz1oYOAhGZChJwbhCZRt6HMBHJRTPwP39WOpYJv/d+9svjpw74Wqb8EvjNXCVZBl7Bs5TzWsZpQRvTQmFM/Dq3CciV1nihI+atWPOnIE3Jj0tAVISquiwFRRC5yAjYkmCA/NVCwNnk37WSZBMwOrZAcAdKfEExncSCgycxmvg3j/iScZCJfDRxwM3bXMZnkLEuv8sD4EzcH/l77kM3JRYZwwmJgzO+fGDuPU+j8Xb9+4P32sJgFQioZCwJ5cwcBlLRRID7XEM/Hxd4vF4Gm7b/RLLwkAoXWGfkjq2cWxYhpItBs+0TMMB3L0cA+dgmnD9TSUk/mvHbnxix24soIfCP+8MwAeYwFv3HcDptPIZvOeeFd+xppZptE3pHtaD01YXrzdYTdaQxF89U+J3X6iQQeAfnybx6YcKXHdmg4H7/xACJBS2HgZOPxQB8/kP3epPMHl5SQDfuCU2tuBlCqKEIqME4Rk4u0VlQ3f0rFSSzeG0oYmUFE0CgNyMAjgAoNMuoRCshMIzMUkgVLFrmmAONt+zsQngtRIoCFjUAjUUyAHTul6e7Cmen/3XF42KzUasBr7rDMKzX5dheEoXQk4FGcce3OvwKiXgHm98tT4hXFZhCwNnH8VwQ4FiYiNefP8XwxyKEUb++fIauJQxjLAZhaIlYNZ3kZ12anSqCgoAzsMItQb2uV6lVcOJaXcuEkKmQNBCBQ3ckA/vtP/6K+1XSU0G7iedbi5HQkbnKoGvPozwuy/OQjeu0JhcIAFwD/qhwUnz+VsFOykAfF4PsM45FiZOLZFlEj/aj5XTAoAnTky7nOE3WmR5ZCKq5Ylu2FAUEPn/BswEBGzfx1Jb5K2pA20oOAFJAjLL0ow6Yv2+HHMQkxYwJ9zwpZQ4u65xbl1jiDy+qJDBiblAHfxof4D1x+DFnnDldzkDly0M/MjmLfjlVyh0zrH9+ogkLrtI4qYzBBQkZtYJ/MOTVdLZnJc4ISFCiO+Zh+Lxz1ln9y+ETBi4EcA/XhZ1aSt6RHAE7AtV+BBKr0GKWI+kGiOhKA1IVyGP5GjGalNCMVICL/w4sO2B4bMmA8+Vk1CC8wxpxAizMEGyr0cAXFoJZb60k6gH8G7OJ7k2ALf/dtzCxjJwIPOleqVErdajZrVsRJBQVADDcI6IwGIcc68bK1FbnzuOhUehFFkHDz3tociGsbNNYOA+/I8xcJpKG3obARz4vSdh3YteFHMiFNPAhSUTADDX17hip+0DetNB++KEIlweJBsNmY2IUSiGFE5f3w1Sil/NeLL1F5+zK+2mBl4olUauAZivRLgXIWRVRQzwcfG5yaAdxvgrWOm7qIRye9uz7/s8vGRmNvydIY0msE5M2cLA03KokhVrTySUttKFAG6iM7FYRa06AXDTQVmbOMsrgszyRMbh5h0pG06xungvMPDUC+ILAUlY55kUwKKbCChLGeVy5hn+3ITd3yBvZ+DnmYvxO0uH8NRNj7bnwaMKGqnS3HEU0vSlwFnXXj5yfFM6ttGQbkgKaMNeCpLJCwDYKJQ8RHHERAovLZRjJBRlrNSjYB10zS5OvEeo3yfOf1xDTkv9J7myDR2CFLccgIeJikVENKJQtLLjm62kBYwsBTb7a7aKc/+5v3dvADF80ksofstMKGR5lobHBgYuw3MFIPgJEgYuRWCgvL46l1BCiJ8EOq4RcM4AXgUGbjeUwsaB75rpo3PfM/GqX1JhRWgkIHpddHvdqMIICjMVCeDWQwP3sbT+AhL4i8/c6L5PZbeUgcNV/rRfbpmexMd//UeDlBIZuP1+ZugnjTSVPs+ElYmYzZcIzNsDuWmRUDKjwoqmpw3+efdebOudgtW2kwLAH3vWY/HkpdhYtQngnoF32CxMwsIyNQE8MOajA/ghbMLAlS313WG8jlVSgbKObaWEsGxxfFU5xzKnJt0xEfabbuXYpJAQQqCTKSzoDOhtgknJwLLmJ4g9G4E/ebbE1+8rkmN51jvbOQdPX1xCWWwCEJebdiwxqqNjTArgHqOEwM2PeOLI8akchu/BOuuQiEzHfiADmwphhEYGBi5zD+AiXDTe3ACsZrgyTrFx0lazauOIhMITXcI5RzHfAriwUoAvaiTQqFvCd+euHX/mWN2UW7cAH3qsBPIJHNJTqEhBtAE4v09uX0uUTgSDDiDyybhklxJbN01heiLuz8sWMstYTEZMiiSK0ShGxFohodyDjEk/drv4eddVU8xVlPr8xMY18MVhjUf/2Rdw5Xc13jbYE0ahpZ3YeeBB0L/cscoQUiNQIQtSBxA18HCuGX9uUw1ciBydXIZnO3Of+1BPH/UUaqKYqJE3JZQfuWBrYPD+2gcNnwO4zgKAFwQ8ZFiiN8bXdiJ2UgB4034p+6/kb1uRTaGbqwYDb5FQwmQ56nxp2lAUEcBdFErlALyiLkrNgpWkZYuBOTbfcUcz5s/fhg89WuAdT3VspeEQC15sd2s6ucRQE/BrX4eYWoeVmmfgwxz47r0kqlw02L613VMPwP8a/hFmNtqlMA8jlCZmuxYsCkXLWOmQpMDVP/1SXHlRul9T2eskpLA6ebgMAhXy4EgSJAOA8jjwwmcy+hdonAYOGXRTZQQUETK4OuQGmKgGeOE1lyLXFgK4hWeDA3gLA28m8oydo90LyrGFl5797Zdm2HmqBDaei5lBhQoZ5FEYuL9utUlno7JD0MV0YNOZUDY6hq0AvYSiVArgIVLH3QMthHVk+igMNlFzzOESimfgRSYxUXjGnWrgSgr4RdBtR0qcX9Xh2mppt89YqVspKDQYsWTJ3wiJilQC4M2EvQTAiWwxKw/gMkMn4wBuLRazytwx7b/322ajvrq5GllB/dFPXxx+749YUpzwveNcmRzGY4G/ifdUCcVblVn2ukEsJp9bBu40Y3Zjm6FvGWPgyYM+Zkk8kF30K23TqSGhAVx+6wEbEeIWYpzlqawTdDWvaZ7V+SH7pY9tLibxoceq0BleNmblGIplP+9k0nb4mT4NprNyCu4Z+IDViVYtDKBTKFxF52NQOzbGogoExSBHXnfDCKAwcemRZRLDBg55CcUCOJO2pGU6eYgQkWE15PdfEmPgKjLwNg0cQsWSqoYiAxeWkb/0+5/Ac2/4PB6x9xrkTSdwugq3+2AaOGBfciUFDKKUM+558VEovKyuFOkSPCOyAL5kAVzlXTStrZhVZdJVZ1VYAPcTrBIKyLIkZNNr0RbAR4Hdo6t3YtYyR5khtJQzAkl4np+5LIBbBl4oiYnClV2GB/LIwL1pd3zes1a4VeYtZwh89iEC85sJ3RBSCNQOwAkSJbLgt0mukedPDQbONXCI3PrH4CUUkYyvCk5M+/3f/fxD8aFffaTtqNToadrJVJgMIwOPEkoYg4kMXPhn/R4N4L9/BJc97n2tX7XHgVv3mH/8jLA6WZuEMs6WRAf9StuQNveQa6qRE6F0AB4b+BJkHhm4B/CHdF+BhZt+G36+7rCkESCyFW/SPeF+MdbNVSiOFQrcr8D8XgcJgRgFnp4bpz8GDyNMAJz5E7QEcj9xCWlbZ+Xpvqn2iTwNABd2OeyXoeefsm6EgX+r+6hwHUN2q4jjT6NQMtx0gd12/znr3SqcQm3wBx50oaBCBg08RFaEYk/xnK+kC0IqvwdrKQXqhszTZv5eEgO9ZjVCRQbYeB5m+xUqKKiCSR4e3JoxcYjZgd7qAtD5OiahKIgsS4jJVM9eu+mJTrLK9Fq3BxYjFIwQGKoCv/2Lypb5hZPKuBOEga+veJgriUnXQi4yb/83m4gas6WWcYLpTwi86ykKQhI6jIHH+G2Jf9VPxO9XvxD313xvmhKKyODfAikK22A7hBHa7fKRKBT7YK3r5vihc62k6PvccuMALnhBMSEwDHJejJEPV+GeGkYIAJBy5GXwZpwG3s3TYlYC8YUzwsoSXqcTY7qqcOuLLgalRpH5wpmEB6obbRlP/3CQ19mALO+EpX/XMcj+UICqzWGfnbwB4I1zaoZidTKJYW1gDGG+ji+xXgZI+HdDRtqlVDhSbEu29RPNwBWH4k5MQTKc35MWl/C4vi0jYAQws9Fe6dnNtn1XVaQDigxcWgkl3AeBCiokU2ye6GHdpCu1KyV+btuncIs6m2ngLgpFxn2k6csS+7YKPPt1GWZOm4IkQsbCO89YPAjASikewBc96W2RUA5ifSz87xdrwiYr8c/aLEzGCaVvrLAIjoGXeD+eAvXwF4fvpjue5cfxhNDKFgCv8+nwAkshIZRKy7Y6DVxlWdB9AVbvxstgZgu0mYSBxJ7NItbnFiIFcGckY4LSyx53L/zio89zY/ASkmPijIH7lV1QE2TcLnTwQWwsRCKG/REkbqCz8B/mUXEMYUJ1YMoSppSTUIKbxslwz3iQbbKR+5T/MYk83Np6mmYeQ+DYfgBwYODnKVOEwIXQoPweDeCwWlabRQlFshAw4ZZS/m/7kHsGPq4gEbclUVgGntmsLg2BSgA5X1V6OVHYF6aTpwx8YZjqrtlk6oluyhphQvAaeKYwqDR2zfSxMIwHrpaZf3jkwJAXYBQSf3efS/AY/fbwWZOBE5NQyESnzJMXl/ALc/MA7AN7/X0Ir32xwq0XTiGTAlWTgTsN3E+YXFeuKdYsyWWO9Q7Ajcvcq2oKE2HGJBRvnIEbkUXNWFitPmcp5t6msRu07koAsbwAWpa+WmTh71iHRIYJe0UAzq1xfzMQsOk8zCxV+ELvJ4D7/1QcY9d3G44HCaGVDQkFOaHKp+C9C0pIIGto4MyPIKFw2ynAFfcSAcCFC0HUyCzgeSD10SkSabXKUHmTAig+6yFn4EkXnea+Thl41iKh8CJUIezQOxXJVR6EXSX5ay78u82MGIgCsKsP/9/wUSj+A3vDn3jRmf7SJeOLAD4qUXZaGnJ4Bi6cSBOjaASGQc4rAhkKI79HSyjA2JKUljGkTkwIJEXWw3I4hBEeXY5YEt0goQi4KBQhUHCNMwC4DfvzwOMZZBPAO8VE8neTgfuH2etqnoEPa5O82G1ZmV4a4ADui1kJskvavpzAPrMxfO9fDM/ANZdQTJRQFBGkiCVcezC4eZuAco7bEQnFM3CVMi/rxMwCA89kHpa/RgCVIQy1Qe6bWfgXSCIAawrgEghsTOKls7N4w8HDUed1tnHyfyA6ewEwAA/4Hbetwdry+XOXdmLTYnkAL1zBpR6fPZpLfQIweQpm+xXWdVPA+D9Pux+6uUzkARJ2mU6NHIBMkGXg3uktbNmBlIE7bTqzjZlf/UsZ3vhsFfw3vrhXDRtCGLb34XEiTZcIE5sEwHp9ruvleMjZG7CuW4SxAKkGHiON7M5rJWK9b7/qBAVC40kZYNn7Tz3kTPzmE+8d9ucLcPl7qFidIxtGmEWQd9mumf/XTxihoYPX51vYdtYC4GHigYs5d19IkTBwfx8NKby0+yZgw9kj+zpRO7kAfAwDt8tlgW5SD1wkUSihdGhbIs8Y66OLQWVQZDI4MSshbDEiZ96JSU5f98CdKQvo84MUwItGk9tRALe/1+4h6+aWgQ9rHbqeA8A37yPw3fNSNPGsnIN7yYI4rA6IpEKfXykMK6+Bs8qBRoaXWYAlLkig5+s8C4VcCdRZej2TKBTwidRJKOQBPAuZmkYCtTaoahMmQt8kQ8gYyeLDCA1gM2opIDHOr2o8bDhEt5Ne14lBZJaLriZ7WzgJB3CwZ8b4jNFlAPzeOBOvPXQYPz3HVllS4RM/LPAXPx1BClkP84Ma63rp8/yTF2/DdX/4kyNOTAkgqVMA61Qus+kwHCWsBs6jMfwqU2U5Mp4D4JJ9lGP1FWyUzUThP7fb2Zrc8bgkOYDH/eVK4qO/9mhsdklqPA48nEcjycn7pIC46lQEkCsgVwue2q8w3c3CuwW0ODEZubMMPAvvkHClG3xEiZdA4gQjUJIKGjg31dKU2l9LCaBLtpmL3Y1A3+9TF0HDJxK4TtwrFDBbTTu6EHwXsrEMXNhIgYz1V4TwNzLdNty0FUgoffcwFUpCCJsaWwnBIijie0WwbbQ6WXTg5EqOMPBuo7hRJhVeVP4OHnP+euAmuHRsE3Tsbq5waLHEsDZJ4aDL7isw3xN48Pa4tK4U0EOsLDjImUZIFq+MoaRGto8gWCzbJBTpVgQEAwHlGbgAuqGehESmJOrmEreyy0XPwGP0QerEzGQeIk2MtEkUtSEUDqRV3oEBkOeska17YbQCHn3+qdi1Zzds0yp2sxvgPDGMwNQPfqkWAGeSjH+WOpnCknaOteUkFCnx83ML+HKXM3CF9z6BsWIAyLuYH1Y4dbr9haYGr3KjAQB86WKB8/YSCiJUWYwDl0Ji80t+EfXMTPhdx4UodrNeItUdnpgEcBhHNjgJgWyxrsmOY9AhwachoTSkpaY1MzE5RwoSiv+baeCetGQgaNEDsGirFbK8+qlOFt4tO4ZU/moCOAmJObkewEwA8E3dDXjh7Bwe48o885XXDjoVB7OtI+fUVicpY7+b0qyglQD6nvWbAkO3Kji0QSbNTlbTTioA515tbgQZAJxr4AoikVAAJnuuxInpyq96DRwABkIkGnh8vu0kEpowCMvGRxh4NsrAv2wehF5nK4C9IdzLA3ivcAy8MhAsycZI5jxx1mTgA+7ABEJmHLezN1lJ59ZDNjSTFzTSTELRIi7XjAR6oWqibU1VjQFwX/jJ8meB9chRMydmpvIE5CttUOmogauiwDt/QmLDhROAsPvULNEkl3nwF5AQwPmPB27+4kj1u8lhBCYvoaiWl6pCkawWAOCibevwxRtttMHRANxek1HQ8yYJQNbFXL/Gvba0P4M8CsUIL2nYfb7t6fb+PKJfY5hFJ6aSCr0HPzjZz4UTW7AbwFmTm3HDfBzHpac9CDc8dxf2bDoX2AUc7K5HritMdDpxjPAMnJ+Ku09juI9yDDaUgeXOYVoPTfF91CpKLUE7J6CmCRgsQkuBycEGvPLwDHYtbcRUJwuymr0ujetapO8Vl1AyNy6pCvz24RlgOnXkA8CTyr/Ao6a24EXNc2ppn2clFA0JgQkD1P7B4vkCpoMj01384XMkJtZ1QcPbB8FPKgnFFNOtn2tpEwIyKZnWOhqFAkQGLlYiobjstyKTIXb0yk4Hm3TamgsAuh27xAs1PKRn4KnjotdI3PAdP/ohYSgF8IlcYamsrZORVQfUcrRbjte+/b+JAxPCAXiqpZ69aQK9XOH6fa4BK2fgOgI4wequgJdQfA0OK6FUecpUfBih12QnHeBPUYEKijmScogsgxZAndkJptKGJUQpfOZhEoub48n4lG4L4FlkUkIAL/wY8IZZCJbiPVtMYmKA0EnFp3NnLfX1a/bie3vI2RtAzgdCYvyL6CerpP1jY18ZCMg6mB9UmO6OkQQbGrjNDk3vW0GEUk2FUMBmvDnAWKnKo6xBhMXBj+KaLT+Cw7PPAwD8/cXPxP97xC+EsMOggcvGQsZH54x5dTzzDho4++236D74keHfhWvrE3kAxBR8ECqaRp251RgyvHR2DoIKTHUzdNg72wxLbDZesFKgXyW66+Al2BZns4Ec8ZvYTVtkFSah9IxE5ZeT/F6bLgxluOo8CZ3FhKbVtpMKwNGZxrmDfxv5WDv9WbmsPwDwaa3NLvVBl1uBE3MAH+sq8JjhBO5dliiI8LpDR8I2/r1Szgse+lgKgUKJ4Bz09sAtF+MU/WQ8zpXKzNxL5gHcO0i8k6ZXKPRLjbI2IFKxJrMYlYeuOVvgq/cT2L/efrEFNZ43ayNHJNlLUzcq9CklcK9TJ3HjPrsdz8TUOoYRaojYCEAAHa9bCmXjwLMGgJcewN2EGSZSW7zKs99MFZBZhj97tsRlD5SoNaGsTZgI/QukRLy3KQPPUgbujDPww911mBxQACZfI101AjsAoBb5SF/VB5yxHoCy13sZBu4BvPlSfWrHLvy/A7bSlySAVMdq4N32xKxHXRCbPvtw2Cg+WDtFawzUJNPAR1/l7vnbMLltgOKMLYlu28sngAM/B6otIapUhmFWYMozcB6FwnfbKPvbtAnnJOxlsSt9NIED2JAQKhGcgU5CIaDGepSZ/d6vBjUkpjtZiPCy18Vr6o5l500GrkIWiGfgISJoTOZ1pc3ohy04kTFZpaclQolw7rQ13eBPyoCkN+tq2kkF4KEJaYMCaKGCR3lcKn0MCfMMfCUSimfgCmfUJT60ay8u3bkb59SRugUnXyP6REokSz5vRaZwFp6NrY6YBwbuNGj/onlw7hUK/UoHDdxHYLRJKLs3Cfz1s1RwXIqMQtq0cAy8+ZAqIXDvU6exVHo0YyxHS7xgtoMLhyXuV5axqYAEej6bTXknZuN6umvUlDL8xOT3lasCSma48nyJxQmJ2hiU2rDr6ELe+LicPl4rIJcFWDhJ2IazqcPdaUwyDXxpGQZuELN5/TPTzRU2T3XHSgfeAoA3AO6MWmO9X7GAMKACtaEYNtgwXs/ECH+tUgDYVtcYqqmogbcATb55Pc7+scNQU1OQ0keeEHqFQqVN7FfpbLJrgfcDj5W47L4C/3ORSByRJKOzuc0eOXEG3r53P86fsGGFzTIRQLymNpHHTc7uDBUIpdyESnkAd2QGElPdLMhq9rq499ifP5MmP6V/CNdNPBSjDDwF/aY1yY3d1v52mr03GZuou1qh9FJ9I5rJ9wtVRLcbAz+pNHD/MPXRwTRicasaaiTeWDSKWY0A+JiIFm59VwWwUBJdPQ8F4PP1I/B1c7+wzWDSxSm75XARlv6itTJh7uQe/7r5OGfPwM/XXQDzOEdsBGDjtCtNWBzWAKmgc7cx8NAc1l+D6RgUaCWUUZYhJXDBqSy5iMcgQ+KCKseHd9vwuz5b/ha+NKewYYTNJAgqK5vo4Jfxnnn5LDYuoTBGFBg4k1AAyzBD5x4fhSKBQvK4bc7AmdOusw73mY2M2yfyZC0MXINJKOz6FlmOvRuBw+tHfxOO6WOAW/DBlwlWEJh3ju1xEkqzYYQg//+iba01KhEnr9aa9gG4FKBchAiAiTzDnnqQ1K4HgOleB5gFZqYE/uqn/P1l2/hxjSkGk8nMlnkO8eCj2wQnphqVXAwp9LNTULk+ESG8D9aJWbJENmpKKCxe+7XVS/EjnfNArjxE7t91z4LHMfA2lBUSb917ABdWMQlHCQWQfadykwUAB4CXzMziE1OTqEkFBp66b1fXTioG7p+bPlIdWYsMZQOYSFon5jgJRWSjjKVpnoF3MomithrxO+un4V/0k8I233rCVrzjJyWOnBuL+wBRA+djl8K+5JmMRaJ8uUrPwB9UT+Ezt+3CQ5RNOvCFgmb7FYhk0hVlBMDd32ccdKx7WxmrJVK7E1MKgW3reTQEWwYiw1cnHh/+9o5HG9rmgFVJBwQNDbLpxPTMyzNwd0Myx8Dt8RA08FgLxZUs4MXHPAOXlsFHds6igxg4He6uw0QJZJ6BewBvY+BCjTwzgGVxr39hhi8/YvQ3fBugEbnhvwv/itDmbTyAs1BOp4EP9z0dWydilMTWukYJFSJWTJuu4SdVmQHK1hGSBHQLhboRjZQrgcmiLeaZM/DlNfAwYbCu9KNjsv8kmZju34oK6GwyMHCfYLNt4yTO3zKVRKGIyNQApPHaBiIh2dkKAbweI6E8rt/H6XWc7U93bQjnBaHQecxchcBvHpnF53fsRg0FY3ymKdY0cCCy536jtGYlVGCW8aVbnoGPbUzLrM808K62URqzmEy20Z0MX3iwDN5q/pB5EOpk0lU2c1qfEkFbzh1T9FUPhVTYpnUALB+nfWSpAlG2rITigeecA/bffGsZMvWEe6jbJJQikXrik68h8d/TTwdeb9PRJaxz1dbI8Nl2rv6Li5CZcZcnxIH7GO/Sga6XXtwxMtVJyrAOaw1DiE5M9/JJpoEXwi5bQxRKWE6z+HzGwGc7k5AETA7s9yEKZQwDD6ozQ4GQ5LIMk5It4wjf+RUHgPmBndzGaeA8GcdLgXrxQnz6py4Nn2+rtSvuNKr/sx25fxVE7gAcwKQjBZXmAC7RawFwmchS/litw2Ya82giDz8fwEU1NVqwlVSAiklcdj+Bb99LBAb+iifcF+t7eRoH3tgvz6cw8GF7LNcAAHwy1BgA12MYeNMuVtZ3sFsCGQNw/mtbv9ze3wy0poED8WFaajBwI/K4vAqALSB5GGHYh9tsBQx86DLWisx2pAeAeUozKX14oe/c4R+yWhMKxwInOxbkcnfwTMoAk/53S6W2Yws1N+y/PtV9tl+CmISS6VEG7h+kjz5SYK4HFBOxsomPQqkaOp9k2aP+jML+IJFnCr6hrISfOETUL2Vmi/pThme/VuFPf9YBdmVZpq/HkU3bK7iw3l4/GRh4J5lMvdM3ODGZo8vfu56QqDIn5TAGLlikBtfeZ1zLtC2z9u9F58SUYxh4M5EHQKgLPQ677LXwDuzR7/zqRa6EgTda0L3i8Cw6mUw+36o1hpTFxr9tA2MSinCVPBXFVR23IpOY6LYBeLyOYWIZV0+3wcA5gPtjcgklMnB7bYfUgSmm8VP3OoBXbtkbs4LdfjmAewbuJ2+fZQk4AHf/Z79z41p/pl2N/PjrW4ff6sRsmRjvn0Ud7ScuPCu8dzxQqIaCDhmvGPE3rJadtBo4NzKdsESxTIRsQX+Mj0JZiRPTv65FJvGmU/4Qp+z4LA5jurGFf2mzsC1giw95FjnhYrn9fJlJzsDtTe5X2lZTDD3/4m8B4MhiBYIKEkpetzBw9/f7HqfwvscBn7mNYrVEH0bYZOBSoJO3T2YaMqlnIeCcSwJO9ijRyW0XIrKtd0KYXTOR59QnDjFRz+GrvUcBfQbgWRFC4PgjHjvyuMlDCBS5AkqgJ+xKRCugozI8td6CnbQHj5q8IPyeg90t62zc7/l77RF8Io+sR18qw7X25Dr5MLzWS2W3d5l2pZoc+S7MCRQZ+DgnJq8h/j+37kCWKfxJoRIw6RKhggqJV8tLKAqimAJq6yjsFaPPvq0q2AhxJS+buKWKL4swjvYFBu5WWQ0AXyp14sT0E4K/tiUVEJ0pPMCVYQg5Cay0crAgobihsXht7Rg4OdqV+2zKYgL4fdb3r2FNeXGcTWW98N/r88kA4EXiL8hReydmUmB4de2kYuD+gViihga+eH74b86elo1CWcaJedbjDuHMx8QbXSiFW8SZeJt+JpoczC+XsyCheACnBMBzxRr1yqjaFk3JxYcRuoe36wB8xjHwQ+ucA0/Eyck7UXTjbkZ+ZhmLlVAaYYQiZeDveP7Dwn9ryBFHrJduurm9B1umejb+3sRsSgAgF4USdOEukHUNSnf4AOCy0ypnBQ3cJ2EIicdP2L6dZ6luYOB51sEmdPHePfuwUcTnQrHrumP6FNQSOMsqQSHBqdn0GLDRDc0wQiA6m5d7YQ6c9hj8efVz+OxZr0y/+Nn3YNfWx7mdAr/+b98BgJFUem9cA5fCOux8xuyzZxfxADcBlKxtV6usEZyxCjKzqxAFwkTLhF0oiamGhKIIoZkzwBj4mCiOxGmKVD/vedbvPuLFrHy6eo0C1IlNS0K0mRsDZ+Ay6Nn+OYoATg0RKxuTwd201igUb9xJr3K8ed8B/Omwh/X5VHjvplkkVp7nQUJRhNstE/MkA3D7bx9pCnK9cNHoxq5w1VgNfBkJZWrrENNnDMPfRSZDrZBRc9q1SJd5OgHwzNVG8Rp4jK/mqfW2KXP0vAMIL9tsv4ahDO98isS7nixx4xnxnA6uB971ZIlvXpi+WBnFCWZcIo+U6Ytx+oZ4bYdUJD0PATdxyPjC2C5EAkS++bDdzifyeDY5Wc8AAA5p31LOHjPPOsGJyXmKT9oQIVJB4iUbLsZXbt2Js9Ukysw6MTuqAAK4xnPjmqjc8lXs2mT/rmWskS5OHU1lN0KGmh/c2jLympZnGd6mn4m6aHROuv9PYfL0C90B4sdjGTgbu4Bl1x4AX3V4Ce/dvQ8AUFK2PANnCSzSJcEpYmDKx65EqOvtLQMSDTg4hscycJn8yxn4ZBEbYgA+kccDvf2uNjlUN0ZEhYxUmUZ4ATEoZ1AbzA2qFgZO8GuofAURZwBGAiGCPesdwMu/Ef/ubcSTlvp4eiUwzQCcr85e/dT743H3tSs/taaBW/N615/Wz01C+czgdL6R/fcoUShyBRKK31WhhK0G2LYNea03RqwAlun6eh6egfuwrUzGLpxdxnqKTMaXzlWK8y/bzFIJDYV+V+CzD5Wwabvu/AXw2YdKLPaaAE4xCgUCUrYzcL405U6r7bQ1OF69kXAvn4pp07mSMG656Me08/BOu7/Gcv2bta0oFyWUTsI4veW+FkqQUCSkVNhgDLqysMkeTgMPTjPWK1UyNiR7O3DLqXYbLYFhIfD65ytMPjdWt/OmhRypNQ1ER9hyTkwPWG3x/z4uXYKDWjuJkAyMpLDg7KW0GgqZE1tLqFCytHVUTEKRHavbKlCrBp4riYnGuLOkhBnXwFuHvawG3mtq4DLKmL6xQoUCWTdKlPUyEoq/u4YI2w8sJq3PooTS0MCPYq1RKADw4OcCp7BnZattP4iDN6JTMABnHO8BZ52CrdPTbqy0xsCBuCS7mU7Hc8rX41WP/XX87QOfBX4aUUJxUSj+iwYDb1YBbDN/0YtMJjGoyTaegTdYQq1NwsALJVmN66iBF1kWxlRkkYGTY8oTAcCrIFN4Cy3OxtzFGKNgGa8UYuQ8eP0WIF0d30hnBgZuXvYNXFI/KUg3ftkrpEKmBIxj4B7AJxach3AqZbnXkiupyZ2YLQWDCrfsFdJlB3Y3BmAoVI6ZSYH5CWGdV6EoEmPgfIVFCrs229923LCuP0tgYqKNgQsmPcTPc5fpt5wG7q9V3ubk8+fjfv+yx91rbGu2pDCTAIhEcGbzWjVDykLpg3YNPDoBlZMmFFmnetN6hUJ33anJZ4qQCN6hmNw4J2YjCkW2ODFjnZVYTrZw962kPNHnb6HTcHPvYmDrAwCkEV7+kSUhsFjWUJLLP37daf9/oUads222rITCzQM4aeR5L7x/SfMilYfIJZvIs+bEHAlLum7Tubhu07nJZ7xRbRKF0mDgWQtoBPupvwfKBWz+VIFDi6WVUMYAuGd9IYwwjxJKwTRwC3Kcgdv/7mQ5lBDQsJILueWkMBZpfBhhv9Iw7MWzjVvtf7dGIMAy8GYmJpdQLFalYYScgd9CW/FI70w87b74inkgLpRXuyy6HNA+CkWCGgx8wyKw0I0lBrzVIblBAjDIsl5g4FxC8WDowUyeej9gYMfSlR285X/ZM7sk76AKDsZxAC5xeOPo/eu2yCJGyDFx4EePQuES2YiFol4C3Vzid55y37H7kUzP9RKKB0AO4CWp8Dd/Bqqqws6dOzEw5wBP/Rgw00VneivectFbkBEwkffxrmc0OjNlEnt3HMZbLnpLPGcAkxcJVI+w9+WxUxmqh9d4TA+49tprRweu7gc8+YPAXA+49lpsqHQ4Ti+XePFF27DxyX+LygBP6QBzi5tw7bXX4on3+XU8EhW698mRdwa49skfBAD8JW3B3s7fYbi3D+y9FkQU9jdtXofq2TXOywC5uBez04+F+buHAgDe1duIbi7xfPw+CBrr8nXt4wWS6yDFmPNqGpE9TwDU3YTHv+StqJ4PnNZVuFZa6dXsn8G5Wx6At2x6CyaNwI+YDSvad7fbxZlnnhkIw9HspAJwOW7mTyyyJyFEKArlocG/W8t25HnQcwAAT9/9fVxy2a04uFDGvpQN6xUAyvjSeeZYmdiUYLLjZAaHIVYD9+NxjZZ1qoEHCYU5nAyrRphhZQw8SijSZWJGkPQrmnEAXiELoGR3IiMDVwVQIVQjDAzcbT41AHZvjBPl/9z7Nfj7q3kUgQO7RAOPFp2Y7F/PwLMC8xNu7KqDvi9yxgA5KW5EEns3jjKgTsskzhtE8OVIkR3diRkkspY4wgunrMz3yAWBb4+J+vHm5R+DyMC9E9MDdkUKlQHutTSFb04v4P5T54Tf79y5E9PT0zj33HMDy19cWkKxtB0FETZN3Bs7j/STY67v5Th9QwFxJI49B7CxUpg6ZMnE4W0TGAyXsCETOPvU+2HElg4DM8rKDcUkFgYV1EGbP3HeKZPYfnAR5y3sRFYDh6aBU7eeg6IzjX0HcxzEEFNVD1PTZ2DznKtTbs7D5qkOzthgoz6ICPWuWQDAafoA1s0P0e8KyNPvjUkzg1u2HoARwGnVGZju5ljCTgA1Tuudg1Mmp0bHC6DaORP+WwiB+52xTKott93OR7bhHOzevxsbF4ByOsf63LYd1KfeF/NLc9hV7sHmGjhQn4n7nbn8vokIhw4dws6dO3HeeeetaBgnlYSyEvyOjFsuG4WiVuCZfsEjzwUA3PvUqbFJF37ZrlrjwKOEkssYhZJJgYv7Ai+ZmcVZE6eGUL08k3EZGiSUCETeUQi4CIGjMHCByBgn8mykFoqfEPnStHmNEyemkDYDlGngUubIlEBt7NhmWATd3AQCOF9/zvPwFfNAuxsR43gz1W2NCPLXzq9UOIB3VJQ+OlmBa85/Cd5U/Sx2nv+z8fow5zBBYe/G0euTtxTrN0LEMEJ26lmIhR+/FPaTXd5CDs6eOA3f2X4bfmRBonuUHIQg7wXyEZ2YtVuhVcgwqDTOWdyEb96yAxdNx24vg8EAmzdvTiQaIRWEU/fbMiTbSjULiGRWFRAYFAJjo1BGdhC3m+pkeOCZG+L+RDzBUCTNrRJvMqfjBnOG/S7ZHTsf9rk2BCGEdWy3XNrWjFBn3DFKxyJznHIhsPkC8IbdyVGEsH4bUGvZ4jYTQmDz5s0YDAYrHsZxA7gQ4iwhxBeFENcIIa4WQrzy6L86MRtXD7zNhBTLSihtumvTLjh1Cle94SfwMw87E5f84g/jRY88J/n+4jPWQzupQ8jRMEIPzBOFwsbJHBsmfGsniWkt8ZtHZqFkFoG0hYFzfdoYFiYHxsCXuSy+R9G6buHiwJdn4E1dlocRGki8//9v77zjrCjPvv+9Z07ZylK2sBRZXJG6y4rAoigBiYCIEKwQC1iwBZIHy2MhKvGxRWyxvZEkCkkQNZigJGpsEBVQmqsgqCCgAioIIixsO+fc7x9Tzpw5c8r2kvnx4bPnzNwzc819Zq655ne1nyi8foKCR+/WrSoqXkUhqGfH1ngFP+q5TofThJkmbX0QtEvx4guppIRCqHWwwP2esAL3qn6EN5XHg5MRlg7iHm+kBV4RGXmqwcEPolEo0Ra4116sywHxLHAULx40B5vfG/+2M/pNmg9oC4ViOC2DwsN3h6o4Kr1abXZ7/RT7vWJGk0hHhaYoIiJph/Boyz4i/jgILiIGiohVkVtJywAjdPCITEURWqJepV7GItYtb10ckmDPmLRGoYjYEnN850z65reLuT4mfOngz4w8rvVhJwSK8NCrugZPyJt0JHgsv0gs1McCDwDXSyn7AcOAXwghHOL5Gg5+jxrxg/76zL6svPm0iDHWTh1WBW5PLrDXD46FzBQtVbt7xzTTIgd4eeZwXrjqJKSM5MCtYYRG67I0n4d7JhfxwHkDAe0GDxfjUSwWuDCVitFwVlHCDiwZocDDlmK8KnnCpGq0ubNy4Kbz1FazxQqrMqoKQWVhNT9NP2SpUaJVIwxayhvs0++HQ2nhCAArFZOV6mXIjxk8881ehDUKxXJss6aM2ek8PDcRCtzjtzimwzvwRPDbgqBTHW8HhRURhWJZ7vVE1sp2gvmG5cSBq2EKJKEFrtpCMi0WuFE0LKR42XOwIlxWIoFBYhR30yxwh2MqAlVR8YW6kxYy3n4soXzEV4TGiESIUOpGWQXjfBFRNGkshRbUH4LVXkFIyriyxVOKihC1Mgwddm5a/UaRNX0FIdXP16HO7JY52vpGcGTWWYFLKb+RUm7QPx8GtgBdG0owJ2SlefnzZUMZX6QV9WmX6jX5MVMuU2E7OzHVWljgdlityJ7Z6aT6VIKdCrX9tdesc58ZRhgyrd1Ur0KnDD85mZoC9lj6O6KoEYrUSKE3LHAIh2AFZaQFDnptkrgWuH4YoUSl0hv3ivW87NaZ3QJ/+tu9XHvwR9R22k+tZuajKpEKfL9ej9xKoVgfBO1SPfhCPi3jTvGYcc9OmZiqopKTmkN+er75EParqZZxfnPfVr1pWOBBAUIECAlYMlyrl24iBgdub9cF4DOiUKK2sBzTpFCcLHA91hk1oQVulEa1loFI80Zy4FLx8c2PlRwNGbWDE1j1IlxAzdECN+8LxfwhBMJRgce83HxpkJJFRsdc/Zja4pdeeJaZM2dGDLXSU2HLX0TJFku3Br0qO3MFlSmKpsDjKGHrGiklp512GocOHdL3L7jkkovN9YFAgJycHCZMmBBzf5E7VziaopVyrsmwGA0603SYVDNCaOrUqWzdujW5/SaJBuHAhRAFwAnABw7rrhRCrBNCrNu3b1+9j3VqrxzTgnF8clriwAfgo7tek8OkUExrrfanHmHhGYV9srTsQCWtE2ChUILStHbtccGqIsKvpkIJxw+rSrjEplWB6xb4lkA4FlU1Y5XDtZGdYHC2qlCj2oCaWamW7e03kDX6xtqtR9EVuMjsrDmLLfy8USzqUJoIlxiwaNd2KV69EBOgei1RF2EVbqVQ/n3uv5lUOClMoXgjFbhZHsFafEq3mA9kAnpkwAsjVH73M4vSdurMEiMT06fvL56tZsynowWubxmUyXDgkRE9lfjCFIrxRuLx8mNFjUk1JGpQYiU3nIIBzAQ3G88ckgrfdtAUlEN0pW0nPuh4bNQxIyAtf8zfzSKnCEdMaftwPlpI8RJSoEZ69Dojse9n63XxyiuvMHDgQNq1014T09PT2bRpE5UVmlP3jTfeoGvXWtihQovsr/RFyuok9dVXX83999+f/L6TQL2jUIQQGcCLwP9IKQ/Z10sp5wPzAQYPHtwg7xBGTKXxu/y89BiOzbbVn1AEs0RHPj68AyydQAyln0wcuB0R5WH1j0Fd0RpWhJVCqTbqEdtuaK+qWFpCqah6VSWfR6G4a0f4EgYfE/ZYd+uQyu6DFRyRGWYlFkV4UGW1Y1VCK8xol/RclIORl5VTtTghYE7pHDZ/5efPW+BQRbglnDXW2GwQoCuUQChsgVdZriqPgwWeleqlxoioUbxmiF4EB26ZM9PZmJ4Dqh9/asfw/lWv+TC1PtC9fj+PjlfY1EMglHBWbQQC0c6iYAwL3OtJ7MQ0fmd79ioAuq8kGQ7c2pzg4ZpzWBY6icvMRB7deaw/UMy6QDHeKH+z7BM27zmElJLKwFEE4PeWc9TebNurvQlWBYJIWYXWPkQgpEpQaGO9wkONDNAn18+88+Oego7oebjq1jmcOeInDD9/DAJBRkYGu/fvZs3KNTxx3x/onJ1N2ccfM37iZAYWF7Ng/pNUVlaydOlSCgsLWfHGq/zh0QcJBato1yGDex9/nMKuXfjN3ffx8fZP2fXlLr7ZtY/LrvoFF1x5VpQUixYt4sorr4yQafz48Wx8fzlTLzifBxYvZurUqbz77rsAHDlyhFmzZrFp0yZqamqYO3cukyZNYufOnVx88cUcKS+nKljFLffdyhknn8KKVeuY+9BTZHcp4OONG+nVr5h7Hp2PEIJTTj2VSy+9lEAggCcJn0oyqJcFLoTwoinvRVLKvzeIREnAuIWMp/Q9k4u44lTtyR+++RSMZB7tu75NLRJ57LAWdjKURUjnqs24btVQaKGwBa5GK04nDjzD7zFf1X0WsrVfF8PJYlEoaM2VnRo7PPjdPp7f/Y12vsYxvWlRr5mxnFlT+kxhQMfBQKQCD1oul9yUTihCITs1Wztfwgr8kB7iFxIWBW7jwM0sO9XrSKEYdFMEep8J/7MRf3o44USoXmcOXPGwfKDCvvZxFHh1edSiSAolvNwoeRCPAz+mYxqn9cnlhO4doleGtAd9EDUi6scJhgIPCfhd8By2yy5RFrihwCtlchQKegxKKCIX1HmcFU6nm4gxrqiooKSkhGFDTuT8safy5IP3RG0rLd/8qh+kyuebP+GxJ55k2fI1vLTkefbu2snatWu54ooreOyxxwCYNHY0q1ev5t/vLmfcz8bxzBNPEdRryO/YtoP5L8xn0bK3eOyBe6kxmzCEJV65ciUnnhiu9wMwZcoUXn3p7yihAB9//DGlpeGi73fffTennXYaa9asYfny5dx4440cOXKE3Nxc3njjDTas/YCH/zCPe2+919zmw02f8cgjj7CubCO7vvqSD9e+D4CiKBx33HF89NFHCWYwedT5MSA0bfAnYIuU8qEGkygJGEXDHJkDY5miRS0YY8JhhMbf2j+7rK/GhrIwHBPGA8GwwEMyXJ4y2gIXYWvWknySm+mHoVfCvs9g+C/N5f0cvOTnyFxyDm3VXiVt85AfCNJP70lpHFnjwCPHOVngxqKsVE0x/GhR4NJyIxzX7hjenfIu7fS6HzWWS2lZqcAXELxVIrjYIQolK9VGoVgcjjmZfuadW2wmMEUKp0BmHn5LNTgs7fSsDyRrcSPUGGFZ1UeiFoVE+LfxWguNeRNn86X6VJ6ePsR5pW6BB5KxwBUvQSIpnDSbE9Pj0x5wYQrFeZ93nNUf0K7Fzw5sBanSP6eQjXo8tYHjcjNI83nY/cNRqqu/4qgaJAOVmqosqvwHAMj1ZLE38CMdY5Yj1JCamkpZWRmVNUE+/+4wL73wLN9t3xw9UD+/FE8KoZqO9B84iC5d8jm6t5xjCnoyZswYAIqKili+fDkAh/Z/x4zpF/HV7l1UVVXQpXsPQlJSFQgx4qcj8Pl9dOjYiU7ZORz8NkR219SIVPoDBw6QmRlZUbS4uJidO3eyePFixo8fH7Hu9ddf5+WXX+aBBx7Q5ruykq+++oouXbowc+ZMyso+JCAD7Nz+JSDY78mluOQEunXrRnlVgN79BrBn11cMGnoSSMjNzWXPnj1RD5G6oj52/HDgYmCjEKJMX3arlPKVekuVAAaF4tzxw8KHqn4zqdaMQjEplNqfulUJGdasQaEYFrg1JM9wGHpsN5eqhCMdkCFTSeZk+iElC875Y8T4sAUeRgFpDKmBz0V0eU+lSwnsXKN9tjxg7PMVz5k1QE9oGDsg3AUmIl1b8ZrKG6DacilV+QTPjtL9FCaFYuHAU73UEKZQrJmHa+f8NEomO3xWBa6oqEbWpkU8awGj2lrghjXrsfyWhgKvu9c/bIEn4sAVNVqBGynmRpsur67A14b68EWXsyjMcUissUD7WbUMYCeHX9i5b11nc2LaX2cTwGmUx+MhFAppvpuQpLo63KrM6/NptA0CRVHwG02WFYWAXt1y1qxZXHfddQwZdQqvvf0ST/52PiG92p/PH37IKqpKMKASqs6JOF/j+Pa2dRMnTuSGG25gxYoV7N8frkQqpeTFF1+kd+/eEePnzp1LXl4eH334IZ/v+4QB3Qdr1T6FD8WXZp6/JkfYn1VZWUlqamTgRX1QnyiU96SUQkpZLKUs0f83uvIGGNxDe0Xtaee9LRCKAqNvQynWyTo7haJ6uO9chVumJU+l2BUxWCxwWxeS0/rkMrRA42oLsiObQHitYYRIfjhqKPDo2hwAvXIzo5YpioJQPI4UijLhEfi15jCuvQWuLevaPpWtd5/BeSd2M9dZKRRsD8AAzvNo8NvWyIwMv8fRiZmsg8QaRohQ6ZSu3bgd08M3sNUCF4pmgd+5bz/rd3wV3rYqBoXiEPDs0xVmnW+YXmN4v9047qiZRkoiC9wWBw5EUyiqn8wUDwdox5qSe7QIkDgQCAh5weJstiLSoR12LoYsv6sZkx/3SJZjOmjwY7p04cPNm5HAP5f9k5qayDdFI3ojFn788Ue6du2KIgQvP/cyoBl0UYUSYlxMvXv3Zvv27VHLL7vsMu644w6Kiooilo8dO5bHHnvMvM8//PBDU478/HwU1cPSvy0jGIw05Jwggc8//5wBAwbEPsFaokGiUJoa008u4N3/HWVaiVYY/KVQFOhchMjRIjfMVHrDAldVNvRS+KJLctYERHLgBuxOTIAPbh3NkxcO4vJTevLu/46iT+dIC1oR4booVgrFkfdFs+pX33IaD50/0Fx2OGcwYsC5EU5MxXBwKR7QOVI1JcuUz255xaNQQKN+rNvIOArcSqGkWWLNPQ4WeFaql38Hh3Bk2GwQIqlSrRFyW4sTKSrF3drzzo2j6JUXftBZq9MJVbPAU6TEB1zf/WwW7vkuhgUuHBN5UnSnk6hrLK/Hz5Lut/ItnRJy4KotjBDCkUghPaoHRaW3fr5O16UdQkCopgNKqL3jerMhc0TeTqQFblJVSZW00PZgx6XnnMO769dz5unnsPr9D0hP14wwI1RWCCMSxRlz587lvPPOY8ypo2jfSTuXUChGtT8HqvXMM89kxYoVUUO7devGL3/5y6jlt912GzU1NRQXF9O/f39uu03r5nPttdeycOFCBpaUsGPrDlLTUrXzjTM13333LampqXTu3Dn2oFqiVdVCMWAk1jjBdEAZFoWq5SLWpSemHU4XrpFhZVXgee3CFqKTnMGQjM2Bx0B+VioZlmJW5fmnoAwoISReNC21FI+Po4FAWOletwXluzWw6jYtjNAmvpOlEy/tONICj5y/aulh6a49ZAVDZIdCTDy+mB01B82WaNYolDOKOpOf9XPSj9VCL4VDMau4sB5b/3xMJ9tbjkOavEe/y6f3ngjvPAK+aAUeJOzEtD68/PW1wAmHmCZrgVtnw1BwX3t7MLRqFbTrSq/MTNZ9+QPltogSJwiHv9b9Kw4UikCE8xIsWydS3+Xl5RHjJp3/cy2NHsjLzuY/ixaxpyMU5PVm3jyNWz7vrHGcNe50vd2gYPFLr5lv2CNHjmTkyJHaviZNYtKkSRyqOsTXh78mRelIZaVk1vX/S5VvrynDK++soSYYoiYYWafxiiuu4JJLLuGKK66IkNUK6/FSU1N56qmnosb06tWLjz/+GIDt+zbxyzuuRwAnnzKCASeeZI679a555ufFixdz1VVXJZi92qFVWuBxYXViYlHUNgol2RrBiRDUowvUWiQGVQdDFg48fBvFssANWPl1VWgXekgJv2ob7dlMWdp1QfFpRXyMRB4rnOLo4ynwSA7cboGrFNYEyNat7xRdcVfq1Rqt9UH8HpVSXXlDmGaJUY05GtZjx5h3jxLtdPQaU53SXvub1ilqTEhYQzwtFnh9OXDCNWcSW+DasZycmC9mTWe0+gxMfNT0jew9HIPjt0DLxAxbiMbv3KV9KgWdwlSkIkRU/ZPw5+hP8Q8af6V136oizDK3QjhnizpBe5uFWKW87YLk5+czY8YMM5GnIRAlagzZs7KymDZtWoMdF1qpBR4PYetJTwc2bnaD+66HE9MJRhihUx2JWKixKnDL3eIYeWGBz6MgQ16EUmNSIkaHHACfYtSPCF9BZtsqEe3EdIwDj3Ma8TjwGtulNCylM1uqD5CpN9N1rA9i7CrJes2WDZw/W+DzeHn0u328mp7GqxmaDF5jrlM7wNl/hILhUdsFFUs9Gsv8pOgOteQJt2gYlnciC1xVvYSwKXA9E1NVBIeVdqB6OX9wN3b9cJQrTz3WeUc2GEkygNZgIijxe5SIzkDaKRuWtpa8Y38vqjuBQjiRR8QaAF2yUhyvzYh9W94YpJQREVKJcP75SQWxJ43wgyh+sYHp0y+NkeRVd7Q5BW5eFUY1OzUyxrihLfDrB19PVbCK0vzSxIN11ARCfCnzgI3gzwL2JtoE0Jx/hgL3qAqKUHjjBIXduiFp0AaKRdGG+w6qUZRJvCgUJ8i4Fnjk91+1G8CkzW/SfahWB8IxucXYlUMxq7iwHjvWg1iojDpaQZklMsGgUBAKFJ/nuJm1GmGkBa4r8HrUswhb4AkUuPDoGbbhZQaFoghhctF+j8otZ8SPPrHC4Je1/TiPUSy5E0KIiGvGTsPUBZFmi/OeMmJU/nSCcb3G+1XqI28yCM8XOg2eHNXUEGh7Cty4Mk0LPJJCMcOlkmx0mggFWQXMHzO/VtsEQpJ7Axfh6/1TLug+hJ+XboyIt46FThl+M4rA4ApfOimsDNK8Gg+cYonSqK0FHs/wibDAbcXAnr7sZHjWsloGKawJmBayUwSPeUwHzjcukqBQjDF+y05NCiXOa8bx+Vl0+V7n0y3z5fN5EVLWj0IxLfDEYYRShC1wjxJuuqEq0QWfkoWVtAinqtuObVPYThRK4qJW+rh4w0Ty+4mHsAK378viUWlkTWrt/ZWZEk4ssx+3MXrytDkFHv7RdA7crOxmUCfaak+S1QgbA8OO7UQVPgqGa69y90wuSrCFhk7pPi0UDI0iskeV/LTHT5l94mw6p4e93NZyrPb73kkR1JUDH358nm2w7lhzcGLaodaaQrFa4DFUqpFYZbGYvcbnOG9fz109nBVP/FUfZ1FeQkGlfrog7MRMVAtFS84yFLi1CbEikos6cYJmgYepB3NhxBgr/SbiK+FEx0sQUlefnZuVDJWwAm8fCuKV8K2+/1SvyuGgY7O5BoX1NLJSvWYSXFOgzSlwQ2EYPGa4j5/2JydTi5/1J9myqDEwoGsWO+87s9bbpXhVs3WZlMGoGyTVk8qIbiMilkVY4LYb30mnxrunrMWsYlIX5mA9ecHMxIxTbKiWceAoHp76dq/ZV9R5pzpdYVHgHiwUSsx9qwizbZPVHNVmuz4WuKG4E1EoihJpgadFKPD6WOC2133AHn/nVSOtdOuRak0NNAGHYM2o7qonzHwLIOGYjqlU1PgbnHe2I2yBNwVpEok2F4VidscwejkqkTG15wzqxoobRuJrRgu8XtAplCBVUY5Tp0gYY5ljFEqtLfBaKPCgTgkZfSLjKB3DAk8+CkXl5IpKBlXGCZ9TohV4mEKJbQELVQm327M/8GRtXGXRSNoCNygU/bu1K1OnDD/Z6fGjlWLBymmHa/lEjvF5VPOBEStrM1HTgZ07dzJgwIC4cyVF7BKwS5cuZfPmcOr9+++/T2lpKSUlJfTt25e5c+cCsPy15Tz24O+0/TkcTVWUiNDbpkaURI3AobRSLRYHJidms8D15R5Vq839/Q8Nw4E3NWRQS8MNykD4NVJKgsK5o0okB25fVzsFHsGBJwqbNCkUnQOPYwXVPgrFSCuPb0mDXYEnplBQVdNvYlcwAszSBHVBsk5MwwI3nJjWvqi/PrNv8t3TbYiwpvUvTt3Srfx4pAUevZ+GRiAQYOnSpUyYMIF+/bT+MNOmTeOFF15g4MCBBINBPvvsM4QQjBo3iq4ZXfnqe2cF3lQQeqiOPY/BsQtRA6PtKXD9h5RmIk90txcgflPjFozKPefh7fgefTsWmxeIJwkFnsgCz0zxcLgyEN9qMuOjldjcswFDgSvRxayiZKxtSGcyCryOFIpQwhZ4VOYqDRNG6E/IgXtiUijptbUoX70Zvt0IQLeagFaL0KvSORCkU1BqMtl+y3aBClJkCJ+ikhbyEkIrRZCi+igMVuHN7Qc/+39xDxsIBLjoootYvWYdhcf34aW/LWbLli38z1VXUX70KOl5Hfjbs0vIz89n5MiRlJSU8N577zF58mRefvll/vOf/3DXXXfx4osvsnfvXvLzte7xqqrSr18/jtQcYenipezcvJNZc+bx69nX0iW1hg83fcqu/Ye59+EnufefS1i9ejWlpaUsWLCgdvNWCxiGlF1Bp3hVenRMozoo+ebHCocR9UebU+DS9AAbNSN0zjjKgdc6Tz3D04HD+87Ap8uvSIkHqMKZQrEq8Hip9C/9Yjgrt30fl19dcPkwWAQ4ZDkCkD9QK+j/yT+inZimUozezEzkSVY7mg0P4lngeiMJiwJPj1vG0hBGtUQuRe5fyPpxjicVduJXo3tRnKA7uaKoMZ2Y9YE1gcjnURBCJoy31iUCBEKopEgJnsTFmD777DP+9Kc/cdNv+3P79TN54okn+Mc//sFzDz1ETseO/H7Va8yZM4enn34agOrqatatWwfA1q1bmTBhAueeey4As2fPpnfv3owcOZJx48ZpyTBmcFlY/h9+PMTqZQt59LVPuWbaFFavWskf//hHhgwZQllZGSUlJUnNU20RS4EDZKX5OHCk2mFNw6B1arF4sKfS6zey3eFXl5ZqLQHt070crgqET5NwfHNcCkWJpFC0bLfwgmNzMjg2JyPusft37aDvNMZlc9U78OMuTYEbpVot5WSvOKUnEwZ2idrMaOKbtIGin1MoRgEtTUZtXTs9M/Rnh8tpH0rMsgshYnLgCvULI0zzeZh9+vEJxylCiSj/kFYfBX7GfeZHe1J8LOLq8A/b+SZYQVdvJgdrOnGosoY0n8pxDkXVYqF79+4MHz6cj3cd5Myzz+f5PzzKpk2bmKA3U6gUQQq69zTHX3DBBTH3dfvtt3PhhRfy+uuv8+yzz7J48WJeeSO6bt5Zp49ACEGvPv3IzskxC1P179+fnTt3Np4Cj0GhmOv1vy6FkgSy0jQHT6cMzUowlENUCeNWqsA7pPn4+kAFhywOPI/RdcfJAseZQvGqSpLWlwVmhmKcyyY9FxBwaLc+Nswn/3qCc89rRY9bl8k6loUggGr2h3Qeo60bVFnFC3sP0efIweT2jeWhb3cSU79EnqSPL4SNQmna29SJK68teWR/28vMzKR///68/Yc/APBlLvTJDVflM4paxUJhYSHXXHMNM2bMICcnJ6Lka9f2qXhVBb9Pz0RWFLN6JBBRjrYxYFrgsS6NRtTgrZMIjgMzVMunZyWarYts1lQDZWI2Na7+SSGgdX8BUCR0CQQ4//jzGZo/NGq8UffYIzzhHCehlXeN57B0hKnA48ydxwcZuXDw68ht4sCguQbkDEwwMowgagIKRafQgL5HDtZK/cSywDsEg3RIwopvCFjLBDcUhVJbCCHiJmDFw1dffcXq1asBWP6vvzNs2DD27dvHB2VlANQEavjkk08ct83MzOTw4cPm93/9619mOdetW7eiqirt27c313fK8JOR0oy2aBLZoI2FNmeBmw4ZPfpEsdVCCQ+r26lnpXqTyppsLIwvyo+IIRdIPEhuO+k2x/HWMELDKlKFQFUEtQ6PTcYCB2jXBfZ8qI9NrHy8ipdnxz9LQVZB0qIEhZpUFEpdoBhcse0B90zJDaTmNVwt53iIsMATOD0bGuFoE2E2y6gtevfuzRNPPMH69evp168fs2bNYuzYscy8/HIOlZdzVAS56fqb6N+/f9S2U6ZMYcaMGTz66KMsWbKEv/zlL8yePZu0tDQ8Hg+LFi0ye5Q2RDZnfSFMBe5SKPWGMMvF6tSB0SDWHoVSRwrlg1tH11m2xoDmXooNM9TQkkqvKIYCr+XFbyjFhAq8a1iBJ1nkqygnuWxUAyHUcNEpJ9TDSW1SKDbrs8OQGXXeZ20RKwqlKSEIx++H7AHjcVBQUMCnn34atbykpIQ3Fi4EYGdn6JutPQzt9bmHDx8eEQf+3HPPOR7nV1f9inSvRr0sWLDAvOa6dj+Gt1evM8c1ZgQKJH6IZKR46JWXGVFNtKHQ5hS4/TVfMcMIbTG9dXw1TJSE0dTQYpNjr49M5DGWCVQlOqww8cGMt5skFLi5TePMVxAlKQ68LhBWrqmZYCTyKEJrQdeUsFrghgIPNjD33xCWc4bP2el+fCMpy1gwfCWxZsijKDSWOG1OgZtJCKpdgdvHtQ36X3hSUDr0ibk+IgrF0r1dVZwzMeMfLFkL3BJp0ki+hpBIZIHXg0IxnZjNrMAFLLh0KAP1ZghNDSHCb2mBWljgyaAx+eKmNrLaqSl8V1NOe0/8tnaNgTanwE3L24h+MEqV2m5GpY34bxXVj5LWMfb6iDhwbZmqaM4pp4YOcZE0B974Fnhaih+/N048cj0UuPHwb85kL8OJOeL4nCY/tjXj0iwW1cAWeNO4gpsGPsVL/+pqSG/6+kptToELe/y33t0kXtW11gwRIwPTgFM5WVURzDi1Z8K47+idJanAs4+zCli7YyQJv88Hvjgp+PWiUAzHd/MpcCsH3vQH1/4I6uAnSYCQT0GpbiT1nd270d744sK8/5r+B2tzCjzcSi2ynKywR6G0FQpFOBccMuBUTlYRgunDe8bcJv4BlcQ3SX5J5PjGgOKJL4d9nScVAhXJ7Vp1jgNvSlgTeZoaqUKhXSiET3ga/CEWzPbzVaCyQfdpwtf0FAYQNlKa4fdqG1rMArOVmh7/bXY8t0ehtIDwo4aAov+LhSx/FmmeNLpmdLVY4PU4oFATW+BCwM9fgM7FkJEXf2xdkVCB22RMz05+1yaF0nzXSEg0T1wxgFcodA8EUIVIujdlslCEMFsAthk044O+rU2lhULRb8JYUSj/JRRKpi+TVVNXMSx/GEeqtHrJHdJqWf0v4oBKciF6x4+Fq99tPKtI8cSnSezrUuLXH4nYNEbuQFOiWSkUI7rDm9og98ncuXN54IEHuP3221mxYlXMcfYysrfffjtvvvkmACNHjjRrpRQUFFBUVERJSQlFRUW89NJLtZbpkUce4ejRo+b3p59+mqKiIoqLixkwYIC5T6sMMeFSKA2HcDNj3ZlpcuDNJVHjQhBfgYMWgQLQPk17G/nNxOjkiaShqLGLWTUlFDWBBW6bE3/8Oh6pJSVU6FmCLcGJqZWTbaaLNq2jNl9q+HeudcipA+68804qf9jJF8EjjuvtZWTvvPPOmPtavnw52dnZfPbZZ4wZM4ZJkyYlLUcwGOSRRx7hoosuIi0tjV27dnH33XezYcMGsrKyKC8vZ9++fQllMOFNg/bHgL+WPqUGQJtT4CYHrlMoZqGkNsJ526Fx28md25UjjmVSSRd6dIpfdyIukuHAmwKKJ/6bgH1dAgXe49lFZjELk0Jpxno5oQaywH+75rd8eiA6qaY2MHtBa1Vp6dOxDzcNvSnuNnfffTcLFy4kNzeX7t27c+KJJzJ9+nTGjSql6MwRPHznw6x6cxUej4cxY8Zw9tlnR5WR/b//+7+IqoROOHToEB06dDC///Wvf+XRRx+lurqa0tJSnnzySVRVJSMjg6uuuoo333yTc845hz179jBq1Ciys7N58MEHyczMJCNDU8AZGRnm5+nTp5syFBQUMHXqVF599VU8Hg/z58/nlltuYdu2bdx4441cffXV9ZjluqHNKXCzlrMZBx7Z1LitIRkL3ECKV62f8obkOPCmgOKJ/1C2K19/O7jsdTj8jfNwi7VtdHFqzkQempNCsaG207B+/Xqee+45ysrKCAQCDBo0iBNPPBEABcHBAwd565W32L51O0IIDh48SPv27Zk4cWJChW1g1KhRSCnZvn07L7zwAgBbtmzh+eefZ+XKlXi9Xq699loWLVrEJZdcwpEjRygtLeXBBx8ENMrEsOKDwSB5eXn07NmT0aNHc/bZZ3PWWWc5HveYY46hrKyM2bNnM336dFauXEllZSUDBgxofQpcCDEO+B1aobY/SinvS7BJ48OMQglz3yFBs/KZjQkhRNM6ZIVoGQq84FTwxGkt5mSBH1Oa1K6FR2v9IJrxTaOhnJiJLOXGwLvvvsvkyZNJS9P8HxMnTjTXCdVLRrsMfCk+Lr/8ciZMmMCECRNqfQxD+X7xxReMHj2akSNH8tZbb7F+/XqGDBkCQEVFBbm5uYDWCOKcc85x3Jeqqrz22musXbuWt956i9mzZ7N+/XqzdZsVxrkUFRVRXl5OZmYmmZmZ+P1+80HUlKjznSi098sngNOBXcBaIcTLUsrN8bdsXJgcuCXdOyTajtPSjtpQKA1zwBZigf/0jvjr7co3AYUSsamiEtQ+1FqshkKzOjEbEcKXjsfj4bl/P8c3H33DkiVLePzxx3n77bfrtL/CwkLy8vLYvHkzUkqmTZvGvffeGzUuJSUFVY39QBZCMHToUIYOHcrpp5/OpZde6qjA/X7NaFAUxfxsfG/MkrWxUJ8rdCiwTUq5XUpZDTwHJO9JaCQo9hoo6DdCG1XgtaFQGuaALYQDTwT7nKS2T3pTw/Hd/E7MZjt8vTBixAiWLl1KRUUFhw8fZtmyZeGVioej5Uc5fOgw48eP5+GHH+ajjz4CosvIJoO9e/eyY8cOevTowejRo1myZAl79+4F4MCBA3z55ZeO21mPtWfPHjZs2GCuKysro0ePHrWSo7lQH1OqK/C15fsuIOodVQhxJXAlaPxRY6NzyansznmfQb2Hmct29/CQ2Td2vZDWDI/iwdOUFrFQIqITWiysc3LidBh6VdKb5h5fyrocQZeS4Q0vV5I42CMNJSNx67KWiEGDBnHBBRcwcOBAcnNzTUrDwJHyI8y6ZBYiIJBS8tBDDwHRZWTjYdSoUaiqSk1NDffddx95eXnk5eVx1113MWbMGEKhEF6vlyeeeMJRGV955ZWMGzeOLl26sGDBAm644Qb27NlDSkoKOTk5/P73v2+4CWlEiLrWOBBCnAuMk1JeoX+/GCiVUs6Mtc3gwYOlEcvZElC0UCthunHaxmaWpO5YtXsVOWk59OrQq2kO+GBfKBgO5/yxaY5XV1QfgXu6aIr89v2Jx7chbNmyhb59+za3GDHxyfdaI4f+2fUIZ23DcPr9hBDrpZSD7WPrY7rtBrpbvnfTl7loQpzc9eSmPaDiaRlx4Alh1A1oAXy9CxeNhPpc3WuBXkKInmiKewrw8waRykXLxdi7oH0r4Ae9qTD4MhjoXpIu2i7qrMCllAEhxEzg32hhhE9LKZ2b3LloO+jX7H7q5CAETHi4uaVw4YDj2h9HqE0VlG0+1Ov9Ukr5CvBKA8niwoWL/wL448Xvu6gV2mZ+uQsXLlz8F8BV4C5cuHDRSuEqcBcuXDQ5VqxYUacUeisWLFjAnj17Eo6bPn06PXv2pKSkhD59+vCb3/ym1seyl7p9//33KS0tpaSkhL59+5pZmy+//DL33dd0FUVcBe7ChYtGg5SSUKjhHZbBYDBpBQ4wb948ysrKKCsrY+HChezYsSPpYwUCgSgFPm3aNObPn09ZWRmbNm3i/PPPB7RaKTfffHPtTqYecINkXbhow/j2nnuo2lK/crJ2+Pv2ofOtt8Zcv3PnTsaOHUtpaSnr169n6NChrF27FiEEv/71r7ngggsArRTsmWeeybZt2xg1ahRPPvkkiqLw+uuvc8cdd1BVVUVhYSHPPPMMGRkZFBQUcMEFF/DGG29w3XXXsW7dOi688EJSU1NZvXo18+bNY9myZVRUVHDyySfz1FNPRdVAqqzU2rmlp2tVOdevX891111HeXk52dnZLFiwgPz8fEaOHElJSQnvvfcekydPjip1u3fvXvLz8wGtGJZRw3zBggWsW7eOxx9/nOnTp5OamsqHH37I3r17efrpp/nzn//M6tWrKS0tZcGCBfX+LVwL3IULFw2OrVu3cu2113LnnXeya9cuPvroI958801uvPFGvvlGK+m7Zs0aHnvsMTZv3swXX3zB3//+d77//nvuuusu3nzzTTZs2MDgwYPNVHuATp06sWHDBi666CIGDx7MokWLKCsrIzU1lZkzZ7J27Vo2bdpERUUF//znP83tbrzxRkpKSujWrRtTpkwhNzeXmpoaZs2axZIlS1i/fj2XXXYZc+bMMbeprq5m3bp1zJkzh4kTJ5pWfGFhIbNnz6Z3795MnjyZp556ynww2PHDDz+wevVqHn74YSZOnMjs2bP55JNP2LhxI2V6A5H6wLXAXbhow4hnKTcmevTowbBhw5g9ezZTp05FVVXy8vL4yU9+wtq1a2nXrh1Dhw7l2GOPBWDq1Km89957pKSksHnzZoYP1+rQVFdXc9JJJ5n7Nax3Jyxfvpz777+fo0ePcuDAAfr372/W9Z43bx7nnnsu5eXljB49mlWrVtGuXTs2bdrE6aefDmi0jGFVJzrW7bffzoUXXsjrr7/Os88+y+LFi1mxYkXUuLPOOgshBEVFReTl5VFUpJXv6N+/Pzt37qSkpCS5CY0BV4G7cOGiwWFQFPFgpzeE0IpbnX766SxevLhW+62srOTaa69l3bp1dO/enblz5zpaxRkZGYwcOZL33nuPM844g/79+7N69eo6nUNhYSHXXHMNM2bMICcnh/37o2vuNHb5WZdCceHCRaPh1FNP5fnnnycYDLJv3z7eeecdhg4dCmgUyo4dOwiFQjz//POccsopDBs2jJUrV7Jt2zYAjhw5wueff+64b2tJWENZZ2dnU15eHrOaYSAQ4IMPPqCwsJDevXuzb98+U4HX1NTwySfOyeT2Urf/+te/MAoBbt26FVVVm7yZA7gK3IULF42IyZMnU1xczMCBAznttNO4//776dy5MwBDhgxh5syZ9O3bl549ezJ58mRycnJYsGABU6dOpbi4mJNOOolPP3V2wk6fPp2rr76akpIS/H4/M2bMYMCAAYwdOzaqhK3BgRcXF1NUVMTZZ5+Nz+djyZIl3HTTTQwcOJCSkhJWrVrleKwpU6Ywb948TjjhBL744gv+8pe/0Lt3b0pKSrj44otZtGhR3IYRjYU6l5OtC1paOdllXywjLy2PoflDm1sUFy4aDC29nKyL+GiqcrKtHmcVOjcudeHChYvWAJdCceHChYtWCleBu3DRBtGU1KiLhkNtfzdXgbtw0caQkpLC/v37XSXeyiClZP/+/aSkpCS9zX81B+7CRVtEt27d2LVrF/v27WtuUVzUEikpKXTr1i3p8a4Cd+GijcHr9dKzZ8/mFsNFE8ClUFy4cOGilcJV4C5cuHDRSuEqcBcuXLhopWjSTEwhxD7gyzpung1834DiNDZceRsPrUlWcOVtTLQmWaHu8vaQUubYFzapAq8PhBDrnFJJWypceRsPrUlWcOVtTLQmWaHh5XUpFBcuXLhopXAVuAsXLly0UrQmBT6/uQWoJVx5Gw+tSVZw5W1MtCZZoYHlbTUcuAsXLly4iERrssBduHDhwoUFrgJ34cKFi1aKVqHAhRDjhBCfCSG2CSFubm557BBC7BRCbBRClAkh1unLOgoh3hBCbNX/dmhG+Z4WQuwVQmyyLHOUT2h4VJ/rj4UQg1qIvHOFELv1OS4TQoy3rLtFl/czIcTYJpa1uxBiuRBisxDiEyHEr/TlLXJ+48jbUuc3RQixRgjxkS7vb/TlPYUQH+hyPS+E8OnL/fr3bfr6ghYg6wIhxA7L3Jboy+t/LUgpW/R/QAW+AI4FfMBHQL/mlssm404g27bsfuBm/fPNwG+bUb4RwCBgUyL5gPHAq4AAhgEftBB55wI3OIztp18TfqCnfq2oTShrPjBI/5wJfK7L1CLnN468LXV+BZChf/YCH+jz9gIwRV/+e+Aa/fO1wO/1z1OA51uArAuAcx3G1/taaA0W+FBgm5Ryu5SyGngOmNTMMiWDScBC/fNC4GfNJYiU8h3ggG1xLPkmAX+WGt4H2gsh8ptEUB0x5I2FScBzUsoqKeUOYBvaNdMkkFJ+I6XcoH8+DGwButJC5zeOvLHQ3PMrpZTl+lev/l8CpwFG63n7/BrzvgQYLYQQzSxrLNT7WmgNCrwr8LXl+y7iX3DNAQm8LoRYL4S4Ul+WJ6X8Rv/8LZDXPKLFRCz5WvJ8z9RfNZ+2UFItRl79df0ENMurxc+vTV5oofMrhFCFEGXAXuANtLeAg1LKgINMprz6+h+BTs0lq5TSmNu79bl9WAjht8uqo9Zz2xoUeGvAKVLKQcAZwC+EECOsK6X2vtRi4zVbunw6/h9QCJQA3wAPNqs0NgghMoAXgf+RUh6yrmuJ8+sgb4udXyllUEpZAnRDs/77NK9EsWGXVQgxALgFTeYhQEfgpoY6XmtQ4LuB7pbv3fRlLQZSyt36373AP9Ausu+M1yH9797mk9ARseRrkfMtpfxOvzlCwB8Iv8Y3u7xCCC+aMlwkpfy7vrjFzq+TvC15fg1IKQ8Cy4GT0OgGoyGNVSZTXn19FrC/aSWNkHWcTltJKWUV8AwNOLetQYGvBXrpXmcfmmPi5WaWyYQQIl0IkWl8BsYAm9BknKYPmwa81DwSxkQs+V4GLtE95MOAHy1UQLPBxg1ORptj0OSdokcf9AR6AWuaUC4B/AnYIqV8yLKqRc5vLHlb8PzmCCHa659TgdPRePvlwLn6MPv8GvN+LvC2/gbUXLJ+anmQCzSu3jq39bsWmspDW5//aN7az9G4rznNLY9NtmPRvPQfAZ8Y8qHxbm8BW4E3gY7NKONitNfiGjSe7fJY8qF5xJ/Q53ojMLiFyPsXXZ6P9Qs/3zJ+ji7vZ8AZTSzrKWj0yMdAmf5/fEud3zjyttT5LQY+1OXaBNyuLz8W7UGyDfgb4NeXp+jft+nrj20Bsr6tz+0m4K+EI1XqfS24qfQuXLhw0UrRGigUFy5cuHDhAFeBu3DhwkUrhavAXbhw4aKVwlXgLly4cNFK4SpwFy5cuGilcBW4CxcuXLRSuArchQsXLlop/j+bObxeOWf4gAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "appointed-tablet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# cosine_similarity([encodingsMap['Q847337']], [encodingsMap['Q551800']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "adaptive-bride",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "beautiful-wages",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "equivalent-wireless",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37556037504702056, pvalue=1.7903693658447951e-25)"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "informal-error",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3529686820781901, pvalue=1.0899920454225404e-22)"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "allied-vulnerability",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3156122362936971, pvalue=1.8520533516011967e-18)"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "victorian-seeking",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3583059753020537, pvalue=2.481962318957373e-23)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fallen-action",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "smart-musician",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "stock-agent",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5316995237579449, pvalue=7.350680368606645e-27)"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "wrapped-batman",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5043971026064306, pvalue=6.35665360448366e-24)"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "fallen-professor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4564962071637959, pvalue=2.286865863469491e-19)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "fewer-stock",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5120576203985243, pvalue=1.0133475563672188e-24)"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-dressing",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "royal-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "threatened-roman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "flexible-metropolitan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "superb-machinery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    maxVal = 0\n",
-    "    for elem1 in [pT.strip().split('_')[-1] for pT in row['G_qnode'].split('|')]:\n",
-    "        for elem2 in [pT.strip().split('_')[-1] for pT in row['P_qnode'].split('|')]:\n",
-    "#             print(elem1)\n",
-    "#             print(elem2)\n",
-    "            if elem1 in encodingsMap and elem2 in encodingsMap:\n",
-    "                maxVal = max(maxVal, cosine_similarity([encodingsMap[elem1]], [encodingsMap[elem2]]))\n",
-    "    return maxVal\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "compatible-stick",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.7211705]], dtype=float32)"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max(cosine_similarity([bertLabelsEncMap['Q2285706']], [bertLabelsEncMap['Q17221']]), \\\n",
-    "   cosine_similarity([bertLabelsEncMap['Q48352']], [bertLabelsEncMap['Q17221']]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "excess-sight",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.16066622734069824 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "# wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "metropolitan-sunglasses",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(118, 118, 1.0)"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "entire-branch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "positive-clone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "choice-alfred",
-   "metadata": {},
-   "source": [
-    "# Amandeep's file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "attached-persian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# q1 = \"kgtk query -i ../data/wikidataos.for.text-embedding.tsv.gz --match '(node1)-[]->(node2)' --where 'node1 in [\" + \",\".join(['\"'+word1+'\"' for word1 in wordsimQNodesListWAbstractsLabelsDF2.index.to_list()]) + \"]' -o ../data/wikidataos.for.text-embedding.subset2.tsv\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "fleet-finder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import os\n",
-    "# os.system(\"screen -dm \"+q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "fancy-personality",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSDF = pd.read_csv('../data/wikidataos.for.text-embedding.subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "broke-grave",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q104021-P1245-868aab-b3f18d7b-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1245</td>\n",
-       "      <td>1412</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q104021-P1296-7a59c3-5f49b5a7-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1296</td>\n",
-       "      <td>0108342</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q104021-P1343-Q19180675-e305e6f3-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q19180675</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q104021-P1343-Q2657718-2d01d189-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q2657718</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q104021-P1343-Q4091875-50bd890f-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q4091875</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   id    node1  label      node2    rank  \\\n",
-       "0     Q104021-P1245-868aab-b3f18d7b-0  Q104021  P1245       1412  normal   \n",
-       "1     Q104021-P1296-7a59c3-5f49b5a7-0  Q104021  P1296    0108342  normal   \n",
-       "2  Q104021-P1343-Q19180675-e305e6f3-0  Q104021  P1343  Q19180675  normal   \n",
-       "3   Q104021-P1343-Q2657718-2d01d189-0  Q104021  P1343   Q2657718  normal   \n",
-       "4   Q104021-P1343-Q4091875-50bd890f-0  Q104021  P1343   Q4091875  normal   \n",
-       "\n",
-       "  node2;wikidatatype  \n",
-       "0        external-id  \n",
-       "1        external-id  \n",
-       "2      wikibase-item  \n",
-       "3      wikibase-item  \n",
-       "4      wikibase-item  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "behavioral-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk query -i ../data/wikidataos.for.text-embedding.subset2.tsv \\\n",
-    "#     --match '(node1)-[]->()' \\\n",
-    "#     --return 'count(distinct node1)'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "resistant-jungle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset2.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "humanitarian-divide",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsLabelsDF2.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "unique-accuracy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF = pd.read_csv('../data/wikidataos-text-embeddings-subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "smooth-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF1 = wikidataOSEmbDF[wikidataOSEmbDF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "wired-surveillance",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.50568223,-0.10802102,-0.3486761,0.78598565,-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.4146536,0.36972183,-0.9731038,0.72235125,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>0.085437864,0.50590533,-0.7758463,0.575578,1.1...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.50568223,-0.10802102,-0.3486761,0.78598565,-...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.4146536,0.36972183,-0.9731038,0.72235125,0....\n",
-       "8      Q9635  0.085437864,0.50590533,-0.7758463,0.575578,1.1..."
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSEmbDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "loved-serial",
-   "metadata": {},
-   "source": [
-    "# Version 2 - Comparison of cosine similarity measures with Amandeep's original file new pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "novel-milwaukee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "computational-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-28-6aed93388a22>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = amanEncSim * 10\n",
-      "<ipython-input-28-6aed93388a22>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "amanEncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataOSEmbDF1.iterrows()}\n",
-    "amanEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(amanEncMap, ))\n",
-    "wordsimDF1['amanSim'] = amanEncSim * 10\n",
-    "wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "virtual-sodium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(amanEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "dated-delta",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3654839025870469, pvalue=3.731075991381434e-24)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "realistic-belize",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5075204852629356, pvalue=3.0234963712614295e-24)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-armor",
-   "metadata": {},
-   "source": [
-    "# Version 3 - P31, P279 edges from lexicalized version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "empty-laugh",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279  \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset3.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "biological-enhancement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = pd.read_csv('../data/wikidataos-text-embeddings-subset3.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "silent-logging",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = wikidataV3DF[wikidataV3DF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "color-sword",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.60533667,-0.32207027,-0.19160181,0.70176995,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.3510175,0.29355207,-1.0211854,0.35043457,1....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>-0.010522888,0.4303582,-0.76446587,0.26379517,...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.60533667,-0.32207027,-0.19160181,0.70176995,...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.3510175,0.29355207,-1.0211854,0.35043457,1....\n",
-       "8      Q9635  -0.010522888,0.4303582,-0.76446587,0.26379517,..."
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataV3DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "essential-virtue",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-36-7dbb17313628>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-      "<ipython-input-36-7dbb17313628>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "wikiV3EncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataV3DF.iterrows()}\n",
-    "wikiV3EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(wikiV3EncMap, ))\n",
-    "wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-    "wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "charming-indicator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(wikiV3EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "dependent-sensitivity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.33429987563573715, pvalue=1.805960166533804e-20)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "fresh-correspondence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4737260118146468, pvalue=6.343179394616253e-21)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "specified-tomorrow",
-   "metadata": {},
-   "source": [
-    "# Version 4 - First sentence of abstract vs human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "opposite-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "single-antigua",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "structured-specialist",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "unnecessary-outdoors",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF1.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "breathing-purchase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q213449</th>\n",
-       "      <td>Eating</td>\n",
-       "      <td>Q213449-wikipedia_sitelink-1e88be-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Eating</td>\n",
-       "      <td>104645.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Eating&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "      <td>eating</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              index                                    id               label  \\\n",
-       "node1                                                                           \n",
-       "Q8068         Flood     Q8068-wikipedia_sitelink-410669-0  wikipedia_sitelink   \n",
-       "Q1368         Money     Q1368-wikipedia_sitelink-59ade8-0  wikipedia_sitelink   \n",
-       "Q872          Radio      Q872-wikipedia_sitelink-1e49e4-0  wikipedia_sitelink   \n",
-       "Q5070802  Emergency  Q5070802-wikipedia_sitelink-41cebc-0  wikipedia_sitelink   \n",
-       "Q213449      Eating   Q213449-wikipedia_sitelink-1e88be-0  wikipedia_sitelink   \n",
-       "\n",
-       "                                           node2     ignore  \\\n",
-       "node1                                                         \n",
-       "Q8068         http://en.wikipedia.org/wiki/Flood    23841.0   \n",
-       "Q1368         http://en.wikipedia.org/wiki/Money  1312979.0   \n",
-       "Q872          http://en.wikipedia.org/wiki/Radio  1866867.0   \n",
-       "Q5070802  http://en.wikipedia.org/wiki/Emergency    80429.0   \n",
-       "Q213449      http://en.wikipedia.org/wiki/Eating   104645.0   \n",
-       "\n",
-       "                                              url  \\\n",
-       "node1                                               \n",
-       "Q8068         <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368         <http://dbpedia.org/resource/Money>   \n",
-       "Q872          <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802  <http://dbpedia.org/resource/Emergency>   \n",
-       "Q213449      <http://dbpedia.org/resource/Eating>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q213449   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract node2_labelsDF  \\\n",
-       "node1                                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...          flood   \n",
-       "Q1368     Money is any item or verifiable record that is...          money   \n",
-       "Q872      Radio is the technology of signaling and commu...          radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...      emergency   \n",
-       "Q213449   Eating (also known as consuming) is the ingest...         eating   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  \n",
-       "Q213449   Eating (also known as consuming) is the ingest...  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "surgical-title",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "grateful-harassment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getFirstSentEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getFirstSentMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "owned-helen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.5808649063110352 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-47-9075dca18092>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-      "<ipython-input-47-9075dca18092>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV4Enc = getFirstSentEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV4EncMap = getFirstSentMap(bertV4Enc)\n",
-    "bertV4EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV4EncMap, ))\n",
-    "wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-    "wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV4EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "christian-median",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(239, 349, 0.6848137535816619)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV4EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "nervous-cycling",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548\n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040\n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217\n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000\n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "retired-cookie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "future-boring",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3633652092919623, pvalue=2.1070516667335453e-22)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "composite-lebanon",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.49261494527923566, pvalue=9.798975953928462e-23)"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupational-rough",
-   "metadata": {},
-   "source": [
-    "# Version 5 - DBPedia's short abstracts vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "effective-elements",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "graduate-congress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "little-yukon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "choice-seating",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.15574932098388672 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV5Enc = getAbsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV5EncMap = getAbsMap(bertV5Enc)\n",
-    "bertV5EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV5EncMap, ))\n",
-    "wordsimDF1['bertV5EncSim'] = bertV5EncSim\n",
-    "wordsimDF1['bertV5EncSim'] = wordsimDF1.bertV5EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV5EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "computational-crime",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(58, 118, 0.4915254237288136)"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV5EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "durable-import",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>g_qnode</th>\n",
-       "      <th>p_qnode</th>\n",
-       "      <th>q1_label</th>\n",
-       "      <th>q2_label</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q17221</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>spokesperson</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>5.670399</td>\n",
-       "      <td>4.803427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>4.471632</td>\n",
-       "      <td>5.585540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q2285706</td>\n",
-       "      <td>Q82955</td>\n",
-       "      <td>head of government</td>\n",
-       "      <td>politician</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>7.259848</td>\n",
-       "      <td>6.529090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Q107711</td>\n",
-       "      <td>Q189290</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>military officer</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.78</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>4.595971</td>\n",
-       "      <td>4.234194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q702269</td>\n",
-       "      <td>Q48352</td>\n",
-       "      <td>professional</td>\n",
-       "      <td>head of state</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>5.130080</td>\n",
-       "      <td>4.503454</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index   g_qnode   p_qnode            q1_label            q2_label  complex  \\\n",
-       "0     11    Q17221   Q189290        spokesperson    military officer     0.25   \n",
-       "1      3   Q702269  Q2285706        professional  head of government     0.29   \n",
-       "2      5  Q2285706    Q82955  head of government          politician     0.36   \n",
-       "3     11   Q107711   Q189290         firefighter    military officer     0.29   \n",
-       "4      3   Q702269    Q48352        professional       head of state     0.30   \n",
-       "\n",
-       "   transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  bertV5EncSim  \n",
-       "0    0.11  0.62   0.65         0.27   0.68   0.45       5.670399      4.803427  \n",
-       "1    0.39  0.59   0.57         0.13   0.47   0.29       4.471632      5.585540  \n",
-       "2    0.26  0.84   0.55         0.05   0.61   0.64       7.259848      6.529090  \n",
-       "3    0.22  0.58   0.52         0.34   0.78   0.49       4.595971      4.234194  \n",
-       "4    0.27  0.61   0.52         0.04   0.49   0.33       5.130080      4.503454  "
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "champion-waste",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "pretty-observation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "adverse-calculator",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sexual-leone",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "elder-refund",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>chief of police</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>firefighter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>government</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>group of humans</td>\n",
-       "      <td>group of humans</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q2855502   A chief of police is the title given to an app...   \n",
-       "Q107711    A firefighter (also known as a fireman or fire...   \n",
-       "Q7188      A government is the system or group of people ...   \n",
-       "Q16334295                                    group of humans   \n",
-       "Q11499147                                 political activist   \n",
-       "\n",
-       "               node2_labelsDF  \n",
-       "node1                          \n",
-       "Q2855502      chief of police  \n",
-       "Q107711           firefighter  \n",
-       "Q7188              government  \n",
-       "Q16334295     group of humans  \n",
-       "Q11499147  political activist  "
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "inclusive-gender",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "dental-choice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "understanding-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    maxVal = 0\n",
-    "    for elem1 in [pT.strip().split('_')[-1] for pT in row['G_qnode'].split('|')]:\n",
-    "        for elem2 in [pT.strip().split('_')[-1] for pT in row['P_qnode'].split('|')]:\n",
-    "#             print(elem1)\n",
-    "#             print(elem2)\n",
-    "            if elem1 in encodingsMap and elem2 in encodingsMap:\n",
-    "                maxVal = max(maxVal, cosine_similarity([encodingsMap[elem1]], [encodingsMap[elem2]]))\n",
-    "    return maxVal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "political-manufacturer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.22603774070739746 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "oriental-olympus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 24, 0.0)"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "simplified-recipient",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>G_token</th>\n",
-       "      <th>P_token</th>\n",
-       "      <th>G_entity</th>\n",
-       "      <th>G_qnode</th>\n",
-       "      <th>P_entity</th>\n",
-       "      <th>P_qnode</th>\n",
-       "      <th>token_mismatch_type</th>\n",
-       "      <th>P_event_confidence</th>\n",
-       "      <th>Original_Text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.spokesperson</td>\n",
-       "      <td>spokesperson_Q17221</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.721170</td>\n",
-       "      <td>4.817058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.minister</td>\n",
-       "      <td>minister_Q83307</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>0.73</td>\n",
-       "      <td>0.803506</td>\n",
-       "      <td>7.783970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition</td>\n",
-       "      <td>professional_Q702269 | firefighter_Q107711 | s...</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.008333</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.721170</td>\n",
-       "      <td>5.585539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.militarypersonnel</td>\n",
-       "      <td>military personnel_Q47064</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.401134</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>0.579380</td>\n",
-       "      <td>5.473495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.politician</td>\n",
-       "      <td>politician_Q82955</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.099668</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0.731348</td>\n",
-       "      <td>6.529088</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index         G_token         P_token                         G_entity  \\\n",
-       "0      1           Putin           Putin  per.politician.headofgovernment   \n",
-       "1      2           Putin           Putin  per.politician.headofgovernment   \n",
-       "2      3           Putin           Putin  per.politician.headofgovernment   \n",
-       "3      4  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "4      5  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "\n",
-       "                                             G_qnode  \\\n",
-       "0  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "1  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "2  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "3  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "4  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "\n",
-       "                                P_entity  \\\n",
-       "0  per.professionalposition.spokesperson   \n",
-       "1      per.professionalposition.minister   \n",
-       "2               per.professionalposition   \n",
-       "3                  per.militarypersonnel   \n",
-       "4                         per.politician   \n",
-       "\n",
-       "                                             P_qnode token_mismatch_type  \\\n",
-       "0                                spokesperson_Q17221                same   \n",
-       "1                                    minister_Q83307                same   \n",
-       "2  professional_Q702269 | firefighter_Q107711 | s...                same   \n",
-       "3                          military personnel_Q47064                same   \n",
-       "4                                  politician_Q82955                same   \n",
-       "\n",
-       "   P_event_confidence                                      Original_Text  \\\n",
-       "0            0.016665  Why not be more accurate and state that those ...   \n",
-       "1            0.016665  Why not be more accurate and state that those ...   \n",
-       "2            0.008333  Why not be more accurate and state that those ...   \n",
-       "3            0.401134  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "4            0.099668  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "\n",
-       "  complex  transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \\\n",
-       "0       x     NaN   NaN    NaN          NaN   0.52   0.42       0.721170   \n",
-       "1       x     NaN   NaN    NaN          NaN   0.79   0.73       0.803506   \n",
-       "2       x     NaN   NaN    NaN          NaN   0.54   0.42       0.721170   \n",
-       "3       x     NaN   NaN    NaN          NaN   0.70   0.44       0.579380   \n",
-       "4       x     NaN   NaN    NaN          NaN   0.61   0.64       0.731348   \n",
-       "\n",
-       "   bertV6EncSim  \n",
-       "0      4.817058  \n",
-       "1      7.783970  \n",
-       "2      5.585539  \n",
-       "3      5.473495  \n",
-       "4      6.529088  "
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "convenient-glory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "split-doctor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "criminal-tragedy",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "certified-compact",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "billion-southwest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "relevant-looking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "based-duplicate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q2855502</th>\n",
-       "      <td>Chief_of_police</td>\n",
-       "      <td>Q2855502-wikipedia_sitelink-2cf037-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Chief_of_police</td>\n",
-       "      <td>248977.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Chief_of_police&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "      <td>chief of police</td>\n",
-       "      <td>A chief of police is the title given to an app...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q107711</th>\n",
-       "      <td>Firefighter</td>\n",
-       "      <td>Q107711-wikipedia_sitelink-4a93a6-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Firefighter</td>\n",
-       "      <td>80424.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Firefighter&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "      <td>firefighter</td>\n",
-       "      <td>A firefighter (also known as a fireman or fire...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q7188</th>\n",
-       "      <td>Government</td>\n",
-       "      <td>Q7188-wikipedia_sitelink-481ab9-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Government</td>\n",
-       "      <td>5382.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Government&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "      <td>government</td>\n",
-       "      <td>A government is the system or group of people ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q16334295</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>group of humans</td>\n",
-       "      <td>group of humans</td>\n",
-       "      <td>group of humans</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q11499147</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "      <td>political activist</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     index                                    id  \\\n",
-       "node1                                                              \n",
-       "Q2855502   Chief_of_police  Q2855502-wikipedia_sitelink-2cf037-0   \n",
-       "Q107711        Firefighter   Q107711-wikipedia_sitelink-4a93a6-0   \n",
-       "Q7188           Government     Q7188-wikipedia_sitelink-481ab9-0   \n",
-       "Q16334295              NaN                                   NaN   \n",
-       "Q11499147              NaN                                   NaN   \n",
-       "\n",
-       "                        label                                         node2  \\\n",
-       "node1                                                                         \n",
-       "Q2855502   wikipedia_sitelink  http://en.wikipedia.org/wiki/Chief_of_police   \n",
-       "Q107711    wikipedia_sitelink      http://en.wikipedia.org/wiki/Firefighter   \n",
-       "Q7188      wikipedia_sitelink       http://en.wikipedia.org/wiki/Government   \n",
-       "Q16334295                 NaN                                           NaN   \n",
-       "Q11499147                 NaN                                           NaN   \n",
-       "\n",
-       "             ignore                                            url  \\\n",
-       "node1                                                                \n",
-       "Q2855502   248977.0  <http://dbpedia.org/resource/Chief_of_police>   \n",
-       "Q107711     80424.0      <http://dbpedia.org/resource/Firefighter>   \n",
-       "Q7188        5382.0       <http://dbpedia.org/resource/Government>   \n",
-       "Q16334295       NaN                                            NaN   \n",
-       "Q11499147       NaN                                            NaN   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q2855502   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q107711    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q7188      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q16334295                                             NaN   \n",
-       "Q11499147                                             NaN   \n",
-       "\n",
-       "                                                    abstract  \\\n",
-       "node1                                                          \n",
-       "Q2855502   A chief of police is the title given to an app...   \n",
-       "Q107711    A firefighter (also known as a fireman or fire...   \n",
-       "Q7188      A government is the system or group of people ...   \n",
-       "Q16334295                                    group of humans   \n",
-       "Q11499147                                 political activist   \n",
-       "\n",
-       "               node2_labelsDF  \\\n",
-       "node1                           \n",
-       "Q2855502      chief of police   \n",
-       "Q107711           firefighter   \n",
-       "Q7188              government   \n",
-       "Q16334295     group of humans   \n",
-       "Q11499147  political activist   \n",
-       "\n",
-       "                                           abstractFirstSent  \n",
-       "node1                                                         \n",
-       "Q2855502   A chief of police is the title given to an app...  \n",
-       "Q107711    A firefighter (also known as a fireman or fire...  \n",
-       "Q7188      A government is the system or group of people ...  \n",
-       "Q16334295                                    group of humans  \n",
-       "Q11499147                                 political activist  "
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "starting-parcel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    maxVal = 0\n",
-    "    for elem1 in [pT.strip().split('_')[-1] for pT in row['G_qnode'].split('|')]:\n",
-    "        for elem2 in [pT.strip().split('_')[-1] for pT in row['P_qnode'].split('|')]:\n",
-    "#             print(elem1)\n",
-    "#             print(elem2)\n",
-    "            if elem1 in encodingsMap and elem2 in encodingsMap:\n",
-    "                maxVal = max(maxVal, cosine_similarity([encodingsMap[elem1]], [encodingsMap[elem2]]))\n",
-    "    return maxVal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "independent-consultancy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.16777896881103516 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "incomplete-dispute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 24, 0.0)"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "pregnant-message",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>G_token</th>\n",
-       "      <th>P_token</th>\n",
-       "      <th>G_entity</th>\n",
-       "      <th>G_qnode</th>\n",
-       "      <th>P_entity</th>\n",
-       "      <th>P_qnode</th>\n",
-       "      <th>token_mismatch_type</th>\n",
-       "      <th>P_event_confidence</th>\n",
-       "      <th>Original_Text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.spokesperson</td>\n",
-       "      <td>spokesperson_Q17221</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.721170</td>\n",
-       "      <td>4.817058</td>\n",
-       "      <td>6.096483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.minister</td>\n",
-       "      <td>minister_Q83307</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>0.73</td>\n",
-       "      <td>0.803506</td>\n",
-       "      <td>7.783970</td>\n",
-       "      <td>7.349318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition</td>\n",
-       "      <td>professional_Q702269 | firefighter_Q107711 | s...</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.008333</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.721170</td>\n",
-       "      <td>5.585539</td>\n",
-       "      <td>6.096483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.militarypersonnel</td>\n",
-       "      <td>military personnel_Q47064</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.401134</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>0.579380</td>\n",
-       "      <td>5.473495</td>\n",
-       "      <td>6.397786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.politician</td>\n",
-       "      <td>politician_Q82955</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.099668</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0.731348</td>\n",
-       "      <td>6.529088</td>\n",
-       "      <td>5.945703</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index         G_token         P_token                         G_entity  \\\n",
-       "0      1           Putin           Putin  per.politician.headofgovernment   \n",
-       "1      2           Putin           Putin  per.politician.headofgovernment   \n",
-       "2      3           Putin           Putin  per.politician.headofgovernment   \n",
-       "3      4  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "4      5  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "\n",
-       "                                             G_qnode  \\\n",
-       "0  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "1  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "2  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "3  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "4  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "\n",
-       "                                P_entity  \\\n",
-       "0  per.professionalposition.spokesperson   \n",
-       "1      per.professionalposition.minister   \n",
-       "2               per.professionalposition   \n",
-       "3                  per.militarypersonnel   \n",
-       "4                         per.politician   \n",
-       "\n",
-       "                                             P_qnode token_mismatch_type  \\\n",
-       "0                                spokesperson_Q17221                same   \n",
-       "1                                    minister_Q83307                same   \n",
-       "2  professional_Q702269 | firefighter_Q107711 | s...                same   \n",
-       "3                          military personnel_Q47064                same   \n",
-       "4                                  politician_Q82955                same   \n",
-       "\n",
-       "   P_event_confidence                                      Original_Text  \\\n",
-       "0            0.016665  Why not be more accurate and state that those ...   \n",
-       "1            0.016665  Why not be more accurate and state that those ...   \n",
-       "2            0.008333  Why not be more accurate and state that those ...   \n",
-       "3            0.401134  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "4            0.099668  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "\n",
-       "  complex  transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \\\n",
-       "0       x     NaN   NaN    NaN          NaN   0.52   0.42       0.721170   \n",
-       "1       x     NaN   NaN    NaN          NaN   0.79   0.73       0.803506   \n",
-       "2       x     NaN   NaN    NaN          NaN   0.54   0.42       0.721170   \n",
-       "3       x     NaN   NaN    NaN          NaN   0.70   0.44       0.579380   \n",
-       "4       x     NaN   NaN    NaN          NaN   0.61   0.64       0.731348   \n",
-       "\n",
-       "   bertV6EncSim  bertV7EncSim  \n",
-       "0      4.817058      6.096483  \n",
-       "1      7.783970      7.349318  \n",
-       "2      5.585539      6.096483  \n",
-       "3      5.473495      6.397786  \n",
-       "4      6.529088      5.945703  "
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "broke-oregon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "recent-moment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "varying-prescription",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accessible-serum",
-   "metadata": {},
-   "source": [
-    "# Version 8, 9, 10 - complex, transe, text, class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "japanese-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-82-99910acabed9>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-      "<ipython-input-82-99910acabed9>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-      "<ipython-input-82-99910acabed9>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['text'] = wordsimDF.text * 10\n",
-      "<ipython-input-82-99910acabed9>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['class'] = wordsimDF['class'] * 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-    "wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-    "wordsimDF1['text'] = wordsimDF.text * 10\n",
-    "wordsimDF1['class'] = wordsimDF['class'] * 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "fitting-jamaica",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "educational-phoenix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF1.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "bored-northwest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1['bertLabelsSim'] /= 10\n",
-    "wordsimDF1['bertV5EncSim'] /= 10\n",
-    "wordsimDF1['bertV6EncSim'] /= 10\n",
-    "wordsimDF1['bertV7EncSim'] /= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "classical-bandwidth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>G_token</th>\n",
-       "      <th>P_token</th>\n",
-       "      <th>G_entity</th>\n",
-       "      <th>G_qnode</th>\n",
-       "      <th>P_entity</th>\n",
-       "      <th>P_qnode</th>\n",
-       "      <th>token_mismatch_type</th>\n",
-       "      <th>P_event_confidence</th>\n",
-       "      <th>Original_Text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.spokesperson</td>\n",
-       "      <td>spokesperson_Q17221</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.minister</td>\n",
-       "      <td>minister_Q83307</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>0.73</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition</td>\n",
-       "      <td>professional_Q702269 | firefighter_Q107711 | s...</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.008333</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.militarypersonnel</td>\n",
-       "      <td>military personnel_Q47064</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.401134</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.politician</td>\n",
-       "      <td>politician_Q82955</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.099668</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index         G_token         P_token                         G_entity  \\\n",
-       "0      1           Putin           Putin  per.politician.headofgovernment   \n",
-       "1      2           Putin           Putin  per.politician.headofgovernment   \n",
-       "2      3           Putin           Putin  per.politician.headofgovernment   \n",
-       "3      4  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "4      5  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "\n",
-       "                                             G_qnode  \\\n",
-       "0  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "1  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "2  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "3  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "4  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "\n",
-       "                                P_entity  \\\n",
-       "0  per.professionalposition.spokesperson   \n",
-       "1      per.professionalposition.minister   \n",
-       "2               per.professionalposition   \n",
-       "3                  per.militarypersonnel   \n",
-       "4                         per.politician   \n",
-       "\n",
-       "                                             P_qnode token_mismatch_type  \\\n",
-       "0                                spokesperson_Q17221                same   \n",
-       "1                                    minister_Q83307                same   \n",
-       "2  professional_Q702269 | firefighter_Q107711 | s...                same   \n",
-       "3                          military personnel_Q47064                same   \n",
-       "4                                  politician_Q82955                same   \n",
-       "\n",
-       "   P_event_confidence                                      Original_Text  \\\n",
-       "0            0.016665  Why not be more accurate and state that those ...   \n",
-       "1            0.016665  Why not be more accurate and state that those ...   \n",
-       "2            0.008333  Why not be more accurate and state that those ...   \n",
-       "3            0.401134  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "4            0.099668  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "\n",
-       "  complex  transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \n",
-       "0       x     NaN   NaN    NaN          NaN   0.52   0.42              0  \n",
-       "1       x     NaN   NaN    NaN          NaN   0.79   0.73              0  \n",
-       "2       x     NaN   NaN    NaN          NaN   0.54   0.42              0  \n",
-       "3       x     NaN   NaN    NaN          NaN   0.70   0.44              0  \n",
-       "4       x     NaN   NaN    NaN          NaN   0.61   0.64              0  "
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "powered-excess",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1.to_csv('../data/SimIP_new_max_OP.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "amazing-expense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.052735</td>\n",
-       "      <td>6.361162</td>\n",
-       "      <td>5.509827</td>\n",
-       "      <td>5.600105</td>\n",
-       "      <td>3.272164</td>\n",
-       "      <td>3.733567</td>\n",
-       "      <td>4.655997</td>\n",
-       "      <td>4.464247</td>\n",
-       "      <td>4.286958</td>\n",
-       "      <td>3.438595</td>\n",
-       "      <td>5.578697</td>\n",
-       "      <td>1.937397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.044971</td>\n",
-       "      <td>1.601204</td>\n",
-       "      <td>2.015174</td>\n",
-       "      <td>1.995942</td>\n",
-       "      <td>2.736918</td>\n",
-       "      <td>2.912649</td>\n",
-       "      <td>2.279742</td>\n",
-       "      <td>2.077533</td>\n",
-       "      <td>1.645163</td>\n",
-       "      <td>2.033467</td>\n",
-       "      <td>2.021548</td>\n",
-       "      <td>2.753170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.312500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.483480</td>\n",
-       "      <td>-0.526745</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1.436633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.375000</td>\n",
-       "      <td>5.304373</td>\n",
-       "      <td>4.548204</td>\n",
-       "      <td>4.739625</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.015642</td>\n",
-       "      <td>3.120310</td>\n",
-       "      <td>3.317798</td>\n",
-       "      <td>2.151662</td>\n",
-       "      <td>4.626498</td>\n",
-       "      <td>0.145023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.937500</td>\n",
-       "      <td>6.461515</td>\n",
-       "      <td>5.597513</td>\n",
-       "      <td>5.787425</td>\n",
-       "      <td>3.463857</td>\n",
-       "      <td>4.437084</td>\n",
-       "      <td>4.774273</td>\n",
-       "      <td>4.206756</td>\n",
-       "      <td>4.171686</td>\n",
-       "      <td>3.240109</td>\n",
-       "      <td>5.767745</td>\n",
-       "      <td>0.639914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5.375000</td>\n",
-       "      <td>7.399760</td>\n",
-       "      <td>6.735863</td>\n",
-       "      <td>6.744476</td>\n",
-       "      <td>5.183337</td>\n",
-       "      <td>5.999169</td>\n",
-       "      <td>6.218609</td>\n",
-       "      <td>5.675579</td>\n",
-       "      <td>4.996030</td>\n",
-       "      <td>4.731990</td>\n",
-       "      <td>6.812977</td>\n",
-       "      <td>2.350565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000001</td>\n",
-       "      <td>10.000001</td>\n",
-       "      <td>10.000002</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Human (Mean)  bertLabelsSim     amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "count    349.000000     349.000000  349.000000    349.000000    349.000000   \n",
-       "mean       4.052735       6.361162    5.509827      5.600105      3.272164   \n",
-       "std        2.044971       1.601204    2.015174      1.995942      2.736918   \n",
-       "min        0.312500       0.000000    0.000000      0.000000      0.000000   \n",
-       "25%        2.375000       5.304373    4.548204      4.739625      0.000000   \n",
-       "50%        3.937500       6.461515    5.597513      5.787425      3.463857   \n",
-       "75%        5.375000       7.399760    6.735863      6.744476      5.183337   \n",
-       "max       10.000000      10.000002   10.000000     10.000000     10.000002   \n",
-       "\n",
-       "       bertV5EncSim  bertV6EncSim  bertV7EncSim     complex      transe  \\\n",
-       "count    349.000000    349.000000    349.000000  349.000000  349.000000   \n",
-       "mean       3.733567      4.655997      4.464247    4.286958    3.438595   \n",
-       "std        2.912649      2.279742      2.077533    1.645163    2.033467   \n",
-       "min        0.000000     -0.483480     -0.526745    0.000000   -1.436633   \n",
-       "25%        0.000000      3.015642      3.120310    3.317798    2.151662   \n",
-       "50%        4.437084      4.774273      4.206756    4.171686    3.240109   \n",
-       "75%        5.999169      6.218609      5.675579    4.996030    4.731990   \n",
-       "max       10.000001     10.000001     10.000002   10.000000   10.000000   \n",
-       "\n",
-       "             text       class  \n",
-       "count  349.000000  349.000000  \n",
-       "mean     5.578697    1.937397  \n",
-       "std      2.021548    2.753170  \n",
-       "min      0.000000    0.000000  \n",
-       "25%      4.626498    0.145023  \n",
-       "50%      5.767745    0.639914  \n",
-       "75%      6.812977    2.350565  \n",
-       "max     10.000000   10.000000  "
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "headed-reality",
-   "metadata": {},
-   "source": [
-    "# Agreement Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "typical-highland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "versionNames = ['bertLabelsSim', 'amanSim', 'wikiV3EncSim', 'bertV4EncSim', 'bertV5EncSim', 'bertV6EncSim', 'bertV7EncSim', 'complex', 'transe', 'text', 'class']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "irish-yesterday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAM = []\n",
-    "spearmanAM = []\n",
-    "for ver in versionNames:\n",
-    "    kAMTemp = []\n",
-    "    sAMTemp = []\n",
-    "    for ver2 in versionNames:\n",
-    "        kAMTemp.append(stats.kendalltau(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "        sAMTemp.append(stats.spearmanr(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "    kendallAM.append(kAMTemp)\n",
-    "    spearmanAM.append(sAMTemp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "massive-optimum",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF = pd.DataFrame(kendallAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "higher-shirt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
-       "      <th>Version 2</th>\n",
-       "      <th>Version 3</th>\n",
-       "      <th>Version 4</th>\n",
-       "      <th>Version 5</th>\n",
-       "      <th>Version 6</th>\n",
-       "      <th>Version 7</th>\n",
-       "      <th>Version 8</th>\n",
-       "      <th>Version 9</th>\n",
-       "      <th>Version 10</th>\n",
-       "      <th>Version 11</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Version 1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.174948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 2</th>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.238586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 3</th>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.235619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 4</th>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.259318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 5</th>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.248043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 6</th>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.220891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 7</th>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.241190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.334855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>0.255377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 10</th>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.232037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 11</th>\n",
-       "      <td>0.174948</td>\n",
-       "      <td>0.238586</td>\n",
-       "      <td>0.235619</td>\n",
-       "      <td>0.259318</td>\n",
-       "      <td>0.248043</td>\n",
-       "      <td>0.220891</td>\n",
-       "      <td>0.241190</td>\n",
-       "      <td>0.334855</td>\n",
-       "      <td>0.255377</td>\n",
-       "      <td>0.232037</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
-       "Version 1    1.000000   0.412089   0.383523   0.160567   0.146498   0.287018   \n",
-       "Version 2    0.412089   1.000000   0.838947   0.296893   0.266232   0.369277   \n",
-       "Version 3    0.383523   0.838947   1.000000   0.275386   0.250581   0.351252   \n",
-       "Version 4    0.160567   0.296893   0.275386   1.000000   0.820504   0.611740   \n",
-       "Version 5    0.146498   0.266232   0.250581   0.820504   1.000000   0.782150   \n",
-       "Version 6    0.287018   0.369277   0.351252   0.611740   0.782150   1.000000   \n",
-       "Version 7    0.383673   0.458566   0.429853   0.619886   0.449513   0.650565   \n",
-       "Version 8    0.208911   0.309217   0.304272   0.229670   0.246373   0.265199   \n",
-       "Version 9    0.209933   0.247839   0.249097   0.135532   0.143346   0.201692   \n",
-       "Version 10   0.400400   0.912377   0.839894   0.299409   0.269472   0.368089   \n",
-       "Version 11   0.174948   0.238586   0.235619   0.259318   0.248043   0.220891   \n",
-       "\n",
-       "            Version 7  Version 8  Version 9  Version 10  Version 11  \n",
-       "Version 1    0.383673   0.208911   0.209933    0.400400    0.174948  \n",
-       "Version 2    0.458566   0.309217   0.247839    0.912377    0.238586  \n",
-       "Version 3    0.429853   0.304272   0.249097    0.839894    0.235619  \n",
-       "Version 4    0.619886   0.229670   0.135532    0.299409    0.259318  \n",
-       "Version 5    0.449513   0.246373   0.143346    0.269472    0.248043  \n",
-       "Version 6    0.650565   0.265199   0.201692    0.368089    0.220891  \n",
-       "Version 7    1.000000   0.260021   0.239819    0.453371    0.241190  \n",
-       "Version 8    0.260021   1.000000   0.447656    0.309583    0.334855  \n",
-       "Version 9    0.239819   0.447656   1.000000    0.244593    0.255377  \n",
-       "Version 10   0.453371   0.309583   0.244593    1.000000    0.232037  \n",
-       "Version 11   0.241190   0.334855   0.255377    0.232037    1.000000  "
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kendallAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "distant-level",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spearmanAMDF = pd.DataFrame(spearmanAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "skilled-battery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
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-       "      <td>0.539416</td>\n",
-       "      <td>0.213744</td>\n",
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-       "      <td>0.350908</td>\n",
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-       "      <th>Version 3</th>\n",
-       "      <td>0.539416</td>\n",
-       "      <td>0.959002</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.371106</td>\n",
-       "      <td>0.337087</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.596281</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.352982</td>\n",
-       "      <td>0.959131</td>\n",
-       "      <td>0.336037</td>\n",
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-       "      <th>Version 4</th>\n",
-       "      <td>0.213744</td>\n",
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-       "      <td>0.946527</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.356645</td>\n",
-       "      <td>0.343931</td>\n",
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-       "      <th>Version 6</th>\n",
-       "      <td>0.400162</td>\n",
-       "      <td>0.515853</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.733577</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.298756</td>\n",
-       "      <td>0.513084</td>\n",
-       "      <td>0.321041</td>\n",
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-       "      <td>0.628222</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>0.347957</td>\n",
-       "      <td>0.624048</td>\n",
-       "      <td>0.345162</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.301924</td>\n",
-       "      <td>0.440326</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.325008</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.619056</td>\n",
-       "      <td>0.439102</td>\n",
-       "      <td>0.477997</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
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-       "      <td>0.189124</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.298756</td>\n",
-       "      <td>0.347957</td>\n",
-       "      <td>0.619056</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>0.364509</td>\n",
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-       "      <th>Version 10</th>\n",
-       "      <td>0.563628</td>\n",
-       "      <td>0.985972</td>\n",
-       "      <td>0.959131</td>\n",
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-       "      <td>0.356645</td>\n",
-       "      <td>0.513084</td>\n",
-       "      <td>0.624048</td>\n",
-       "      <td>0.439102</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.333301</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 11</th>\n",
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-       "      <td>0.364509</td>\n",
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-       "      <td>1.000000</td>\n",
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-      "text/plain": [
-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
-       "Version 1    1.000000   0.576425   0.539416   0.213744   0.192074   0.400162   \n",
-       "Version 2    0.576425   1.000000   0.959002   0.392378   0.350908   0.515853   \n",
-       "Version 3    0.539416   0.959002   1.000000   0.371106   0.337087   0.493746   \n",
-       "Version 4    0.213744   0.392378   0.371106   1.000000   0.946527   0.733577   \n",
-       "Version 5    0.192074   0.350908   0.337087   0.946527   1.000000   0.793177   \n",
-       "Version 6    0.400162   0.515853   0.493746   0.733577   0.793177   1.000000   \n",
-       "Version 7    0.532950   0.628626   0.596281   0.628222   0.556256   0.829959   \n",
-       "Version 8    0.301924   0.440326   0.430792   0.325008   0.343765   0.381097   \n",
-       "Version 9    0.305490   0.352366   0.352982   0.189124   0.203396   0.298756   \n",
-       "Version 10   0.563628   0.985972   0.959131   0.399380   0.356645   0.513084   \n",
-       "Version 11   0.253590   0.339260   0.336037   0.355801   0.343931   0.321041   \n",
-       "\n",
-       "            Version 7  Version 8  Version 9  Version 10  Version 11  \n",
-       "Version 1    0.532950   0.301924   0.305490    0.563628    0.253590  \n",
-       "Version 2    0.628626   0.440326   0.352366    0.985972    0.339260  \n",
-       "Version 3    0.596281   0.430792   0.352982    0.959131    0.336037  \n",
-       "Version 4    0.628222   0.325008   0.189124    0.399380    0.355801  \n",
-       "Version 5    0.556256   0.343765   0.203396    0.356645    0.343931  \n",
-       "Version 6    0.829959   0.381097   0.298756    0.513084    0.321041  \n",
-       "Version 7    1.000000   0.373446   0.347957    0.624048    0.345162  \n",
-       "Version 8    0.373446   1.000000   0.619056    0.439102    0.477997  \n",
-       "Version 9    0.347957   0.619056   1.000000    0.346778    0.364509  \n",
-       "Version 10   0.624048   0.439102   0.346778    1.000000    0.333301  \n",
-       "Version 11   0.345162   0.477997   0.364509    0.333301    1.000000  "
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spearmanAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "indoor-coordinator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF.to_csv('../data/kendallAMDF.csv')\n",
-    "spearmanAMDF.to_csv('../data/spearmanAMDF.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "innocent-prerequisite",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "active-leisure",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "boolean-impossible",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suitable-brazil",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "desirable-collect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for ver in versionNames:\n",
-    "    wordsimDF[ver] = wordsimDF1[ver]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "flexible-fifth",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th></th>\n",
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-       "      <td>admission</td>\n",
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-       "      <td>4.168040</td>\n",
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-       "      <td>4.168040</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.944618</td>\n",
-       "      <td>4.292894</td>\n",
-       "      <td>6.267841</td>\n",
-       "      <td>3.936571</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>3.248249</td>\n",
-       "      <td>3.790767</td>\n",
-       "      <td>6.186442</td>\n",
-       "      <td>2.088125</td>\n",
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-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
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-       "      <td>news</td>\n",
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-       "      <td>7.1875</td>\n",
-       "      <td>3.612144</td>\n",
-       "      <td>3.603834</td>\n",
-       "      <td>8.638458</td>\n",
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-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
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-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  bertLabelsSim  \\\n",
-       "0        5.5360  3.611154  4.466324  4.856529  0.000000       6.809672   \n",
-       "1        4.1250  3.021635  2.743883  4.793135  0.058511       4.554877   \n",
-       "2        6.6250  6.944618  4.292894  6.267841  3.936571       7.643815   \n",
-       "3        2.0625  3.248249  3.790767  6.186442  2.088125       7.194899   \n",
-       "4        7.1875  3.612144  3.603834  8.638458  8.886211       8.137879   \n",
-       "\n",
-       "    amanSim  wikiV3EncSim  bertV4EncSim  bertV5EncSim  bertV6EncSim  \\\n",
-       "0  4.949738      5.145601      3.770548      4.268633      4.268633   \n",
-       "1  4.818231      3.987047      4.168040      3.399217      3.399217   \n",
-       "2  6.407051      5.669631      4.997217      6.865782      6.865782   \n",
-       "3  6.186497      6.186497      0.000000      0.000000      2.961755   \n",
-       "4  8.556664      9.098408      0.000000      0.000000      3.408112   \n",
-       "\n",
-       "   bertV7EncSim  \n",
-       "0      3.770548  \n",
-       "1      4.168040  \n",
-       "2      4.997217  \n",
-       "3      5.296330  \n",
-       "4      5.621890  "
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "ambient-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF.to_csv('../data/wordsim_updated.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "tested-screening",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assigned-identifier",
-   "metadata": {},
-   "source": [
-    "# Part 2 - Pedro's updated file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "casual-research",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDFNew = pd.read_csv('../data/SimIP_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "capable-brisbane",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(set(wordsimDFNew['G_qnode'].to_list() + wordsimDFNew['P_qnode'].to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valid-agriculture",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "worst-posting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "quick-asbestos",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "democratic-assignment",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.580217361450195 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-13-d93d87c23bc8>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-      "<ipython-input-13-d93d87c23bc8>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "extraordinary-motivation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "recovered-assignment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.25719783185089823, pvalue=9.11953585838357e-13)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "southern-berry",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.37036443456409784, pvalue=8.677074016866423e-13)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "public-arizona",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "breathing-bennett",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                     school holiday  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "communist-destination",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "cardiac-coral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "original-combat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "fossil-blame",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.902165412902832 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-66-ef38b58ef6d6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-      "<ipython-input-66-ef38b58ef6d6>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "touched-response",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "senior-chase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  \n",
-       "0      4.268633      4.268633  \n",
-       "1      3.399217      3.399217  \n",
-       "2      6.865782      6.865782  \n",
-       "3      0.000000      2.961755  \n",
-       "4      0.000000      3.408112  "
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "considerable-assignment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "cutting-floor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "centered-chosen",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unable-humor",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "quarterly-amendment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "latest-fountain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "experienced-uganda",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \\\n",
-       "node1                                                                         \n",
-       "Q1514190                                     school holiday  school holiday   \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood   \n",
-       "Q1368     Money is any item or verifiable record that is...           money   \n",
-       "Q872      Radio is the technology of signaling and commu...           radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q1514190                                     school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  "
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "adequate-stocks",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['G_qnode'] in encodingsMap and row['P_qnode'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['G_qnode']]], [encodingsMap[row['P_qnode']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "static-partition",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48781871795654297 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-76-ef0e3d1b2360>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-      "<ipython-input-76-ef0e3d1b2360>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "approved-sharp",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "banner-techno",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Index</th>\n",
-       "      <th>G_token</th>\n",
-       "      <th>P_token</th>\n",
-       "      <th>G_entity</th>\n",
-       "      <th>G_qnode</th>\n",
-       "      <th>P_entity</th>\n",
-       "      <th>P_qnode</th>\n",
-       "      <th>token_mismatch_type</th>\n",
-       "      <th>P_event_confidence</th>\n",
-       "      <th>Original_Text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>node2vec-v0</th>\n",
-       "      <th>H_3x6</th>\n",
-       "      <th>H_5x8</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.spokesperson</td>\n",
-       "      <td>spokesperson_Q17221</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition.minister</td>\n",
-       "      <td>minister_Q83307</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.016665</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>0.73</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.professionalposition</td>\n",
-       "      <td>professional_Q702269 | firefighter_Q107711 | s...</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.008333</td>\n",
-       "      <td>Why not be more accurate and state that those ...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.militarypersonnel</td>\n",
-       "      <td>military personnel_Q47064</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.401134</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>Vladimir Putin</td>\n",
-       "      <td>per.politician.headofgovernment</td>\n",
-       "      <td>head_of_government_Q2285706 | head_of state_Q4...</td>\n",
-       "      <td>per.politician</td>\n",
-       "      <td>politician_Q82955</td>\n",
-       "      <td>same</td>\n",
-       "      <td>0.099668</td>\n",
-       "      <td>Their leader, Vyacheslav Ponomaryov, a soap ma...</td>\n",
-       "      <td>x</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Index         G_token         P_token                         G_entity  \\\n",
-       "0      1           Putin           Putin  per.politician.headofgovernment   \n",
-       "1      2           Putin           Putin  per.politician.headofgovernment   \n",
-       "2      3           Putin           Putin  per.politician.headofgovernment   \n",
-       "3      4  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "4      5  Vladimir Putin  Vladimir Putin  per.politician.headofgovernment   \n",
-       "\n",
-       "                                             G_qnode  \\\n",
-       "0  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "1  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "2  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "3  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "4  head_of_government_Q2285706 | head_of state_Q4...   \n",
-       "\n",
-       "                                P_entity  \\\n",
-       "0  per.professionalposition.spokesperson   \n",
-       "1      per.professionalposition.minister   \n",
-       "2               per.professionalposition   \n",
-       "3                  per.militarypersonnel   \n",
-       "4                         per.politician   \n",
-       "\n",
-       "                                             P_qnode token_mismatch_type  \\\n",
-       "0                                spokesperson_Q17221                same   \n",
-       "1                                    minister_Q83307                same   \n",
-       "2  professional_Q702269 | firefighter_Q107711 | s...                same   \n",
-       "3                          military personnel_Q47064                same   \n",
-       "4                                  politician_Q82955                same   \n",
-       "\n",
-       "   P_event_confidence                                      Original_Text  \\\n",
-       "0            0.016665  Why not be more accurate and state that those ...   \n",
-       "1            0.016665  Why not be more accurate and state that those ...   \n",
-       "2            0.008333  Why not be more accurate and state that those ...   \n",
-       "3            0.401134  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "4            0.099668  Their leader, Vyacheslav Ponomaryov, a soap ma...   \n",
-       "\n",
-       "  complex  transe  text  class  node2vec-v0  H_3x6  H_5x8  bertLabelsSim  \n",
-       "0       x     NaN   NaN    NaN          NaN   0.52   0.42              0  \n",
-       "1       x     NaN   NaN    NaN          NaN   0.79   0.73              0  \n",
-       "2       x     NaN   NaN    NaN          NaN   0.54   0.42              0  \n",
-       "3       x     NaN   NaN    NaN          NaN   0.70   0.44              0  \n",
-       "4       x     NaN   NaN    NaN          NaN   0.61   0.64              0  "
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "aggregate-lighter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "functioning-donor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3793557735858387, pvalue=5.886665529373508e-26)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "adjusted-commission",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5330950015966213, pvalue=5.11634460488722e-27)"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "presidential-penetration",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "after-reward",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "outer-symposium",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "165px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Similarity Work.ipynb b/Similarity Work.ipynb
deleted file mode 100644
index 67f4860..0000000
--- a/Similarity Work.ipynb	
+++ /dev/null
@@ -1,9864 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "protecting-craft",
-   "metadata": {},
-   "source": [
-    "Downloaded dataset from [DBPedia Short Abstracts - 2020.07.01](https://downloads.dbpedia.org/repo/dbpedia/text/short-abstracts/2020.07.01/short-abstracts_lang=en.ttl.bz2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "indirect-kingdom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "import pandas as pd\n",
-    "from time import time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "elder-russian",
-   "metadata": {},
-   "source": [
-    "# Process and format DBPedia dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "characteristic-orientation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3db8345a6c34dbe8f54427985216b25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cnt = 0\n",
-    "\n",
-    "p1s = []\n",
-    "p11s = []\n",
-    "p2s = []\n",
-    "lines = []\n",
-    "with open(\"../data/short-abstracts_lang=en.ttl\", 'r', encoding='utf-8') as f:\n",
-    "    for line in tqdm(f):\n",
-    "        p1 = line[:line.find(\" \")]\n",
-    "#         p11 = p1.split(\"/\")[-1][:-1]\n",
-    "        p11 = p1[len(\"<http://dbpedia.org/resource/\"):][:-1]\n",
-    "        line = line[line.find(\" \")+1:]\n",
-    "        p2 = line[:line.find(\" \")]\n",
-    "        line = line[line.find(\" \")+1:line.rfind(\" \")][1:-4]\n",
-    "#         print(p1, p2, line)\n",
-    "        p1s.append(p1)\n",
-    "        p11s.append(p11)\n",
-    "        p2s.append(p2)\n",
-    "        lines.append(line)\n",
-    "#         except UnicodeDecodeError:\n",
-    "#             cnt += 1\n",
-    "# print(\"Encoding error:\", cnt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "prostate-master",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame({'urlComp': p11s, 'url':p1s, 'p2': p2s, 'abstract': lines})\n",
-    "df1.to_csv('../data/short-abstracts_lang=en.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "occupational-sheriff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "environmental-question",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "dirty-sample",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df1.set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "defensive-switch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Animalia_(book)</th>\n",
-       "      <td>1</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Animalia_(book)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Animalia is an illustrated children's book by ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Agricultural_science</th>\n",
-       "      <td>2</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Agricultural_scie...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Agricultural science is a broad multidisciplin...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albedo</th>\n",
-       "      <td>3</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Albedo&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Albedo () (Latin: albedo, meaning 'whiteness')...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alain_Connes</th>\n",
-       "      <td>4</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Alain_Connes&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Alain Connes (French: [alɛ̃ kɔn]; born 1 April...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      ignore  \\\n",
-       "node1                          \n",
-       "NaN                        0   \n",
-       "Animalia_(book)            1   \n",
-       "Agricultural_science       2   \n",
-       "Albedo                     3   \n",
-       "Alain_Connes               4   \n",
-       "\n",
-       "                                                                    url  \\\n",
-       "node1                                                                     \n",
-       "NaN                                                                   #   \n",
-       "Animalia_(book)           <http://dbpedia.org/resource/Animalia_(book)>   \n",
-       "Agricultural_science  <http://dbpedia.org/resource/Agricultural_scie...   \n",
-       "Albedo                             <http://dbpedia.org/resource/Albedo>   \n",
-       "Alain_Connes                 <http://dbpedia.org/resource/Alain_Connes>   \n",
-       "\n",
-       "                                                             ignore2  \\\n",
-       "node1                                                                  \n",
-       "NaN                                                          started   \n",
-       "Animalia_(book)       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Agricultural_science  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Albedo                <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Alain_Connes          <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                               abstract  \n",
-       "node1                                                                    \n",
-       "NaN                                                                 NaN  \n",
-       "Animalia_(book)       Animalia is an illustrated children's book by ...  \n",
-       "Agricultural_science  Agricultural science is a broad multidisciplin...  \n",
-       "Albedo                Albedo () (Latin: albedo, meaning 'whiteness')...  \n",
-       "Alain_Connes          Alain Connes (French: [alɛ̃ kɔn]; born 1 April...  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "specified-emission",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore url  ignore2 abstract\n",
-       "node1                              \n",
-       "NaN         0   #  started      NaN"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "streaming-watch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBPedia dataset has 5732949 records with unique 5732947 index values\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "clean-brook",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>0</td>\n",
-       "      <td>#</td>\n",
-       "      <td>started</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>23399</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/NaN&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>In computing, NaN, standing for Not a Number, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ignore                                url  \\\n",
-       "node1                                              \n",
-       "NaN         0                                  #   \n",
-       "NaN     23399  <http://dbpedia.org/resource/NaN>   \n",
-       "\n",
-       "                                              ignore2  \\\n",
-       "node1                                                   \n",
-       "NaN                                           started   \n",
-       "NaN    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                abstract  \n",
-       "node1                                                     \n",
-       "NaN                                                  NaN  \n",
-       "NaN    In computing, NaN, standing for Not a Number, ...  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "residential-bunch",
-   "metadata": {},
-   "source": [
-    "There are many duplicate element names with different abstracts. For example, 390 corresponds to year 390, ibm 390 systemss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "whole-retirement",
-   "metadata": {},
-   "source": [
-    "# Wikidata Sitelinks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "usual-albuquerque",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "classical-tamil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "undefined-gospel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "backed-criminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "armed-gardening",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Universe</th>\n",
-       "      <td>Q1-wikipedia_sitelink-5e459a-0</td>\n",
-       "      <td>Q1</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Universe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Boston</th>\n",
-       "      <td>Q100-wikipedia_sitelink-c612f2-0</td>\n",
-       "      <td>Q100</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Boston</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gabon</th>\n",
-       "      <td>Q1000-wikipedia_sitelink-b7f1ee-0</td>\n",
-       "      <td>Q1000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Gabon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dutch_Wikipedia</th>\n",
-       "      <td>Q10000-wikipedia_sitelink-16adbd-0</td>\n",
-       "      <td>Q10000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Dutch_Wikipedia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cadier_en_Keer</th>\n",
-       "      <td>Q100000-wikipedia_sitelink-d81a0a-0</td>\n",
-       "      <td>Q100000</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Cadier_en_Keer</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  id    node1  \\\n",
-       "trimmedNode2                                                    \n",
-       "Universe              Q1-wikipedia_sitelink-5e459a-0       Q1   \n",
-       "Boston              Q100-wikipedia_sitelink-c612f2-0     Q100   \n",
-       "Gabon              Q1000-wikipedia_sitelink-b7f1ee-0    Q1000   \n",
-       "Dutch_Wikipedia   Q10000-wikipedia_sitelink-16adbd-0   Q10000   \n",
-       "Cadier_en_Keer   Q100000-wikipedia_sitelink-d81a0a-0  Q100000   \n",
-       "\n",
-       "                              label  \\\n",
-       "trimmedNode2                          \n",
-       "Universe         wikipedia_sitelink   \n",
-       "Boston           wikipedia_sitelink   \n",
-       "Gabon            wikipedia_sitelink   \n",
-       "Dutch_Wikipedia  wikipedia_sitelink   \n",
-       "Cadier_en_Keer   wikipedia_sitelink   \n",
-       "\n",
-       "                                                        node2  \n",
-       "trimmedNode2                                                   \n",
-       "Universe                http://en.wikipedia.org/wiki/Universe  \n",
-       "Boston                    http://en.wikipedia.org/wiki/Boston  \n",
-       "Gabon                      http://en.wikipedia.org/wiki/Gabon  \n",
-       "Dutch_Wikipedia  http://en.wikipedia.org/wiki/Dutch_Wikipedia  \n",
-       "Cadier_en_Keer    http://en.wikipedia.org/wiki/Cadier_en_Keer  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sitelinksDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "thousand-marriage",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 8472828 sitelinks present in the dataset corresponding to 8472828 unique node1s (Qxxx), 8401883 unique labels (text)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "alpha-sport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trimmedNode2</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A-2_(Michigan_county_highway)</th>\n",
-       "      <td>Q100149995-wikipedia_sitelink-029972-0</td>\n",
-       "      <td>Q100149995</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/A-2_(Michigan_cou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A-2_(Michigan_county_highway)</th>\n",
-       "      <td>Q100150013-wikipedia_sitelink-14ff5e-0</td>\n",
-       "      <td>Q100150013</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231096-wikipedia_sitelink-832b42-0</td>\n",
-       "      <td>Q100231096</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Communis...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231099-wikipedia_sitelink-f5327d-0</td>\n",
-       "      <td>Q100231099</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Movement...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>color</th>\n",
-       "      <td>Q100231125-wikipedia_sitelink-b3524d-0</td>\n",
-       "      <td>Q100231125</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Serbian_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_47</th>\n",
-       "      <td>Q999979-wikipedia_sitelink-069c8c-0</td>\n",
-       "      <td>Q999979</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_12</th>\n",
-       "      <td>Q26346274-wikipedia_sitelink-bd20c7-0</td>\n",
-       "      <td>Q26346274</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_12</th>\n",
-       "      <td>Q999984-wikipedia_sitelink-d64e95-0</td>\n",
-       "      <td>Q999984</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_13</th>\n",
-       "      <td>Q26346384-wikipedia_sitelink-61fe02-0</td>\n",
-       "      <td>Q26346384</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Template:Attached...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>New_Hampshire_Route_13</th>\n",
-       "      <td>Q999992-wikipedia_sitelink-025e0c-0</td>\n",
-       "      <td>Q999992</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/New_Hampshire_Rou...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>22572010 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                   id  \\\n",
-       "trimmedNode2                                                            \n",
-       "A-2_(Michigan_county_highway)  Q100149995-wikipedia_sitelink-029972-0   \n",
-       "A-2_(Michigan_county_highway)  Q100150013-wikipedia_sitelink-14ff5e-0   \n",
-       "color                          Q100231096-wikipedia_sitelink-832b42-0   \n",
-       "color                          Q100231099-wikipedia_sitelink-f5327d-0   \n",
-       "color                          Q100231125-wikipedia_sitelink-b3524d-0   \n",
-       "...                                                               ...   \n",
-       "New_Hampshire_Route_47            Q999979-wikipedia_sitelink-069c8c-0   \n",
-       "New_Hampshire_Route_12          Q26346274-wikipedia_sitelink-bd20c7-0   \n",
-       "New_Hampshire_Route_12            Q999984-wikipedia_sitelink-d64e95-0   \n",
-       "New_Hampshire_Route_13          Q26346384-wikipedia_sitelink-61fe02-0   \n",
-       "New_Hampshire_Route_13            Q999992-wikipedia_sitelink-025e0c-0   \n",
-       "\n",
-       "                                    node1               label  \\\n",
-       "trimmedNode2                                                    \n",
-       "A-2_(Michigan_county_highway)  Q100149995  wikipedia_sitelink   \n",
-       "A-2_(Michigan_county_highway)  Q100150013  wikipedia_sitelink   \n",
-       "color                          Q100231096  wikipedia_sitelink   \n",
-       "color                          Q100231099  wikipedia_sitelink   \n",
-       "color                          Q100231125  wikipedia_sitelink   \n",
-       "...                                   ...                 ...   \n",
-       "New_Hampshire_Route_47            Q999979  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_12          Q26346274  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_12            Q999984  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_13          Q26346384  wikipedia_sitelink   \n",
-       "New_Hampshire_Route_13            Q999992  wikipedia_sitelink   \n",
-       "\n",
-       "                                                                           node2  \n",
-       "trimmedNode2                                                                      \n",
-       "A-2_(Michigan_county_highway)  http://en.wikipedia.org/wiki/A-2_(Michigan_cou...  \n",
-       "A-2_(Michigan_county_highway)  http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Communis...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Movement...  \n",
-       "color                          http://en.wikipedia.org/wiki/Template:Serbian_...  \n",
-       "...                                                                          ...  \n",
-       "New_Hampshire_Route_47         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "New_Hampshire_Route_12         http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "New_Hampshire_Route_12         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "New_Hampshire_Route_13         http://en.wikipedia.org/wiki/Template:Attached...  \n",
-       "New_Hampshire_Route_13         http://en.wikipedia.org/wiki/New_Hampshire_Rou...  \n",
-       "\n",
-       "[22572010 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "editorial-cache",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "naval-morrison",
-   "metadata": {},
-   "source": [
-    "# Join DBPedia file with sitelinks - File X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "incorrect-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF = df1.join(sitelinksDF2).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "answering-rhythm",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>211415</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>Q371</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>584428</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>Q2705922</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>!Action_Pact!</td>\n",
-       "      <td>650560</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!Action_Pact!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!Action Pact! was a London-based punk rock ban...</td>\n",
-       "      <td>Q343686-wikipedia_sitelink-d81157-0</td>\n",
-       "      <td>Q343686</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!Action_Pact!</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>!Arriba!_La_Pachanga</td>\n",
-       "      <td>4498318</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!Arriba!_La_Pacha...</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!Arriba! La Pachanga is an album by Mongo Sant...</td>\n",
-       "      <td>Q15411145-wikipedia_sitelink-9fed64-0</td>\n",
-       "      <td>Q15411145</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!Arriba!_La_Pachanga</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>!Hero</td>\n",
-       "      <td>474306</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!Hero&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!HERO is a 2003 Christian rock opera about Jes...</td>\n",
-       "      <td>Q3596099-wikipedia_sitelink-5956bd-0</td>\n",
-       "      <td>Q3596099</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!Hero</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  index   ignore  \\\n",
-       "0                   !!!   211415   \n",
-       "1           !!!_(album)   584428   \n",
-       "2         !Action_Pact!   650560   \n",
-       "3  !Arriba!_La_Pachanga  4498318   \n",
-       "4                 !Hero   474306   \n",
-       "\n",
-       "                                                 url  \\\n",
-       "0                  <http://dbpedia.org/resource/!!!>   \n",
-       "1          <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "2        <http://dbpedia.org/resource/!Action_Pact!>   \n",
-       "3  <http://dbpedia.org/resource/!Arriba!_La_Pacha...   \n",
-       "4                <http://dbpedia.org/resource/!Hero>   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "1  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "4  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  !!! ( chk-chk-chk) is an American dance-punk b...   \n",
-       "1  !!! is the eponymous debut studio album by dan...   \n",
-       "2  !Action Pact! was a London-based punk rock ban...   \n",
-       "3  !Arriba! La Pachanga is an album by Mongo Sant...   \n",
-       "4  !HERO is a 2003 Christian rock opera about Jes...   \n",
-       "\n",
-       "                                      id      node1               label  \\\n",
-       "0       Q371-wikipedia_sitelink-628937-0       Q371  wikipedia_sitelink   \n",
-       "1   Q2705922-wikipedia_sitelink-e96003-0   Q2705922  wikipedia_sitelink   \n",
-       "2    Q343686-wikipedia_sitelink-d81157-0    Q343686  wikipedia_sitelink   \n",
-       "3  Q15411145-wikipedia_sitelink-9fed64-0  Q15411145  wikipedia_sitelink   \n",
-       "4   Q3596099-wikipedia_sitelink-5956bd-0   Q3596099  wikipedia_sitelink   \n",
-       "\n",
-       "                                               node2  \n",
-       "0                   http://en.wikipedia.org/wiki/!!!  \n",
-       "1           http://en.wikipedia.org/wiki/!!!_(album)  \n",
-       "2         http://en.wikipedia.org/wiki/!Action_Pact!  \n",
-       "3  http://en.wikipedia.org/wiki/!Arriba!_La_Pachanga  \n",
-       "4                 http://en.wikipedia.org/wiki/!Hero  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "peripheral-edinburgh",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5785371, 5785371, 5731254)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF), abstractsDF.index.nunique(), abstractsDF.node1.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "first-encoding",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 5785371 Qnodes, there are 54117 dbpedia statements which do not have a sitelink i.e 5731254 have a sitelink\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From {len(abstractsDF)} Qnodes, there are {abstractsDF.node2.isna().sum()} dbpedia statements which do not have a sitelink i.e {len(abstractsDF) - abstractsDF.node2.isna().sum()} have a sitelink\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modular-spirituality",
-   "metadata": {},
-   "source": [
-    "# Similarity file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "wired-hayes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF = pd.read_csv(\"../data/wordsim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "referenced-newport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "persistent-electronics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesList = set(wordsimDF.word1_kg_id.to_list() + wordsimDF.word2_kg_id.to_list())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "billion-syndrome",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From the similarity file having 349 records, we have list1 with 200 unique Qnodes and list2 with 286 unique Qnodes. Taking their union, we get 422 unique Qnodes and by taking their intersection, we get 64 unique Qnodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From the similarity file having {len(wordsimDF)} records, we have list1 with {len(set(wordsimDF.word1_kg_id.to_list()))} unique Qnodes and list2 with {len(set(wordsimDF.word2_kg_id.to_list()))} unique Qnodes. Taking their union, we get {len(set(wordsimDF.word1_kg_id.to_list() + wordsimDF.word2_kg_id.to_list()))} unique Qnodes and by taking their intersection, we get {len(set(wordsimDF.word1_kg_id.to_list()).intersection(set(wordsimDF.word2_kg_id.to_list())))} unique Qnodes\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "affecting-people",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListDF1 = pd.DataFrame({'node1': list(wordsimQNodesList)}).set_index('node1')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-document",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Filtering File X Qnodes using similarity file Qnodes list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "universal-romance",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF1 = abstractsDF[abstractsDF.node1.apply(lambda p:p in wordsimQNodesList)]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "secure-default",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4002</th>\n",
-       "      <td>100</td>\n",
-       "      <td>159716</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/100&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>100 or one hundred (Roman numeral: C) is the n...</td>\n",
-       "      <td>Q37413-wikipedia_sitelink-c0dbd3-0</td>\n",
-       "      <td>Q37413</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>306492</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1074621</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/5&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>5 (five) is a number, numeral, and glyph. It i...</td>\n",
-       "      <td>Q203-wikipedia_sitelink-c8e811-0</td>\n",
-       "      <td>Q203</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>312986</th>\n",
-       "      <td>7</td>\n",
-       "      <td>103187</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/7&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>7 (seven) is the natural number following 6 an...</td>\n",
-       "      <td>Q23350-wikipedia_sitelink-6dd502-0</td>\n",
-       "      <td>Q23350</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363000</th>\n",
-       "      <td>Abuse</td>\n",
-       "      <td>165447</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Abuse&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Abuse is the improper usage or treatment of a ...</td>\n",
-       "      <td>Q600571-wikipedia_sitelink-5719bc-0</td>\n",
-       "      <td>Q600571</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Abuse</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364696</th>\n",
-       "      <td>Academic_discipline</td>\n",
-       "      <td>1830099</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Academic_discipline&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An academic discipline or academic field is a ...</td>\n",
-       "      <td>Q11862829-wikipedia_sitelink-cdefe0-0</td>\n",
-       "      <td>Q11862829</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Academic_discipline</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      index   ignore  \\\n",
-       "4002                    100   159716   \n",
-       "306492                    5  1074621   \n",
-       "312986                    7   103187   \n",
-       "363000                Abuse   165447   \n",
-       "364696  Academic_discipline  1830099   \n",
-       "\n",
-       "                                                      url  \\\n",
-       "4002                    <http://dbpedia.org/resource/100>   \n",
-       "306492                    <http://dbpedia.org/resource/5>   \n",
-       "312986                    <http://dbpedia.org/resource/7>   \n",
-       "363000                <http://dbpedia.org/resource/Abuse>   \n",
-       "364696  <http://dbpedia.org/resource/Academic_discipline>   \n",
-       "\n",
-       "                                               ignore2  \\\n",
-       "4002    <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "306492  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "312986  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "363000  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "364696  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                 abstract  \\\n",
-       "4002    100 or one hundred (Roman numeral: C) is the n...   \n",
-       "306492  5 (five) is a number, numeral, and glyph. It i...   \n",
-       "312986  7 (seven) is the natural number following 6 an...   \n",
-       "363000  Abuse is the improper usage or treatment of a ...   \n",
-       "364696  An academic discipline or academic field is a ...   \n",
-       "\n",
-       "                                           id      node1               label  \\\n",
-       "4002       Q37413-wikipedia_sitelink-c0dbd3-0     Q37413  wikipedia_sitelink   \n",
-       "306492       Q203-wikipedia_sitelink-c8e811-0       Q203  wikipedia_sitelink   \n",
-       "312986     Q23350-wikipedia_sitelink-6dd502-0     Q23350  wikipedia_sitelink   \n",
-       "363000    Q600571-wikipedia_sitelink-5719bc-0    Q600571  wikipedia_sitelink   \n",
-       "364696  Q11862829-wikipedia_sitelink-cdefe0-0  Q11862829  wikipedia_sitelink   \n",
-       "\n",
-       "                                                   node2  \n",
-       "4002                    http://en.wikipedia.org/wiki/100  \n",
-       "306492                    http://en.wikipedia.org/wiki/5  \n",
-       "312986                    http://en.wikipedia.org/wiki/7  \n",
-       "363000                http://en.wikipedia.org/wiki/Abuse  \n",
-       "364696  http://en.wikipedia.org/wiki/Academic_discipline  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "abroad-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initially, there were 5732949 short abstracts. From 5785371 Qnodes with short abstracts, there are 324 Qnodes which have some similarity measure\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Initially, there were {len(df1)} short abstracts. From {len(abstractsDF)} Qnodes with short abstracts, there are {len(abstractsDF1)} Qnodes which have some similarity measure\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "extensive-giving",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "absDFLabelList = abstractsDF1['index'].to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "charged-valuable",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF1['index'].nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recent-kansas",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "terminal-offer",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join sitelinks with DBPedia file - File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "particular-nerve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "abstractsDF2 = sitelinksDF2.join(df1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "dimensional-preserve",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>Q956244</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>Q353153</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>Q371</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>Q91868926</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>Q2705922</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         index                                     id      node1  \\\n",
-       "0                 Q956244-wikipedia_sitelink-539183-0    Q956244   \n",
-       "1           !!    Q353153-wikipedia_sitelink-d716fb-0    Q353153   \n",
-       "2          !!!       Q371-wikipedia_sitelink-628937-0       Q371   \n",
-       "3      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0  Q91868926   \n",
-       "4  !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   Q2705922   \n",
-       "\n",
-       "                label                                     node2    ignore  \\\n",
-       "0  wikipedia_sitelink           http://en.wikipedia.org/wiki///       NaN   \n",
-       "1  wikipedia_sitelink           http://en.wikipedia.org/wiki/!!       NaN   \n",
-       "2  wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!  211415.0   \n",
-       "3  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!       NaN   \n",
-       "4  wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)  584428.0   \n",
-       "\n",
-       "                                         url  \\\n",
-       "0                                        NaN   \n",
-       "1                                        NaN   \n",
-       "2          <http://dbpedia.org/resource/!!!>   \n",
-       "3                                        NaN   \n",
-       "4  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                          ignore2  \\\n",
-       "0                                             NaN   \n",
-       "1                                             NaN   \n",
-       "2  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "3                                             NaN   \n",
-       "4  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                            abstract  \n",
-       "0                                                NaN  \n",
-       "1                                                NaN  \n",
-       "2  !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "3                                                NaN  \n",
-       "4  !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "existing-disabled",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From 8472828 Qnodes, there are 2741574 sitelink Qnodes which do not have a short abstract i.e 5731254 have a short abstract\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "normal-rachel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attempted-scotland",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Join list of similarity file Qnodes with File Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "premier-attack",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "temp1 = sitelinksDF2.join(df1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "disturbed-windsor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "temp2 = temp1.reset_index().set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "southern-cookbook",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q956244</th>\n",
-       "      <td></td>\n",
-       "      <td>Q956244-wikipedia_sitelink-539183-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki///</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q353153</th>\n",
-       "      <td>!!</td>\n",
-       "      <td>Q353153-wikipedia_sitelink-d716fb-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q371</th>\n",
-       "      <td>!!!</td>\n",
-       "      <td>Q371-wikipedia_sitelink-628937-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!</td>\n",
-       "      <td>211415.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! ( chk-chk-chk) is an American dance-punk b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q91868926</th>\n",
-       "      <td>!!!!!!!</td>\n",
-       "      <td>Q91868926-wikipedia_sitelink-d58178-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!!!!!</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q2705922</th>\n",
-       "      <td>!!!_(album)</td>\n",
-       "      <td>Q2705922-wikipedia_sitelink-e96003-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/!!!_(album)</td>\n",
-       "      <td>584428.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/!!!_(album)&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>!!! is the eponymous debut studio album by dan...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 index                                     id  \\\n",
-       "node1                                                           \n",
-       "Q956244                   Q956244-wikipedia_sitelink-539183-0   \n",
-       "Q353153             !!    Q353153-wikipedia_sitelink-d716fb-0   \n",
-       "Q371               !!!       Q371-wikipedia_sitelink-628937-0   \n",
-       "Q91868926      !!!!!!!  Q91868926-wikipedia_sitelink-d58178-0   \n",
-       "Q2705922   !!!_(album)   Q2705922-wikipedia_sitelink-e96003-0   \n",
-       "\n",
-       "                        label                                     node2  \\\n",
-       "node1                                                                     \n",
-       "Q956244    wikipedia_sitelink           http://en.wikipedia.org/wiki///   \n",
-       "Q353153    wikipedia_sitelink           http://en.wikipedia.org/wiki/!!   \n",
-       "Q371       wikipedia_sitelink          http://en.wikipedia.org/wiki/!!!   \n",
-       "Q91868926  wikipedia_sitelink      http://en.wikipedia.org/wiki/!!!!!!!   \n",
-       "Q2705922   wikipedia_sitelink  http://en.wikipedia.org/wiki/!!!_(album)   \n",
-       "\n",
-       "             ignore                                        url  \\\n",
-       "node1                                                            \n",
-       "Q956244         NaN                                        NaN   \n",
-       "Q353153         NaN                                        NaN   \n",
-       "Q371       211415.0          <http://dbpedia.org/resource/!!!>   \n",
-       "Q91868926       NaN                                        NaN   \n",
-       "Q2705922   584428.0  <http://dbpedia.org/resource/!!!_(album)>   \n",
-       "\n",
-       "                                                  ignore2  \\\n",
-       "node1                                                       \n",
-       "Q956244                                               NaN   \n",
-       "Q353153                                               NaN   \n",
-       "Q371       <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q91868926                                             NaN   \n",
-       "Q2705922   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                    abstract  \n",
-       "node1                                                         \n",
-       "Q956244                                                  NaN  \n",
-       "Q353153                                                  NaN  \n",
-       "Q371       !!! ( chk-chk-chk) is an American dance-punk b...  \n",
-       "Q91868926                                                NaN  \n",
-       "Q2705922   !!! is the eponymous debut studio album by dan...  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "ordinary-connectivity",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsDF = wordsimQNodesListDF1.join(temp2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "married-prior",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  \n",
-       "node1                                                        \n",
-       "Q1514190                                                NaN  \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  "
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "durable-keeping",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "98"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsDF.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "oriental-devices",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF[~wordsimQNodesListWAbstractsDF.abstract.isna()].index.to_list()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dominican-illinois",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "afraid-asian",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "422"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsDF.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beneficial-dynamics",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Adding labels from labels.en"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "satisfactory-nickname",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "potential-vegetable",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P10-label-en</td>\n",
-       "      <td>P10</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'video'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1000-label-en</td>\n",
-       "      <td>P1000</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'record held'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1001-label-en</td>\n",
-       "      <td>P1001</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'applies to jurisdiction'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1002-label-en</td>\n",
-       "      <td>P1002</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'engine configuration'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1003-label-en</td>\n",
-       "      <td>P1003</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'National Library of Romania ID'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id  node1  label                                node2\n",
-       "0    P10-label-en    P10  label                           'video'@en\n",
-       "1  P1000-label-en  P1000  label                     'record held'@en\n",
-       "2  P1001-label-en  P1001  label         'applies to jurisdiction'@en\n",
-       "3  P1002-label-en  P1002  label            'engine configuration'@en\n",
-       "4  P1003-label-en  P1003  label  'National Library of Romania ID'@en"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "significant-cross",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsDF.join(labelsDF.set_index('node1'), rsuffix='_labelsDF')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "bigger-separation",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>id_labelsDF</th>\n",
-       "      <th>label_labelsDF</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q1514190-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'school holiday'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>Q8068-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'flood'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>Q1368-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'money'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>Q872-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'radio'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>Q5070802-label-en</td>\n",
-       "      <td>label</td>\n",
-       "      <td>'emergency'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  \\\n",
-       "node1                                                         \n",
-       "Q1514190                                                NaN   \n",
-       "Q8068     A flood is an overflow of water that submerges...   \n",
-       "Q1368     Money is any item or verifiable record that is...   \n",
-       "Q872      Radio is the technology of signaling and commu...   \n",
-       "Q5070802  An emergency is a situation that poses an imme...   \n",
-       "\n",
-       "                id_labelsDF label_labelsDF       node2_labelsDF  \n",
-       "node1                                                            \n",
-       "Q1514190  Q1514190-label-en          label  'school holiday'@en  \n",
-       "Q8068        Q8068-label-en          label           'flood'@en  \n",
-       "Q1368        Q1368-label-en          label           'money'@en  \n",
-       "Q872          Q872-label-en          label           'radio'@en  \n",
-       "Q5070802  Q5070802-label-en          label       'emergency'@en  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "native-british",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF = wordsimQNodesListWAbstractsLabelsDF.drop(columns=['id_labelsDF', 'label_labelsDF'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "popular-harmony",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "temp123 = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: type(p))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "adolescent-panama",
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-       "           index   id label node2  ignore  url ignore2 abstract node2_labelsDF\n",
-       "node1                                                                         \n",
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-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF[temp123 != str]"
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-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "twenty-purpose",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF = wordsimQNodesListWAbstractsLabelsDF.node2_labelsDF.apply(lambda p: p[1:-4] if type(p) == str else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
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-   "metadata": {
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-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
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-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
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-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
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-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                                NaN  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
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-    "wordsimQNodesListWAbstractsLabelsDF.head()"
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-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "fifteen-patent",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = wordsimQNodesListWAbstractsLabelsDF[~wordsimQNodesListWAbstractsLabelsDF.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "structured-archive",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "shared-secretariat",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "fossil-courtesy",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
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-       "      <td>A flood is an overflow of water that submerges...</td>\n",
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-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q213449</th>\n",
-       "      <td>Eating</td>\n",
-       "      <td>Q213449-wikipedia_sitelink-1e88be-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Eating</td>\n",
-       "      <td>104645.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Eating&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "      <td>eating</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-       "              index                                    id               label  \\\n",
-       "node1                                                                           \n",
-       "Q8068         Flood     Q8068-wikipedia_sitelink-410669-0  wikipedia_sitelink   \n",
-       "Q1368         Money     Q1368-wikipedia_sitelink-59ade8-0  wikipedia_sitelink   \n",
-       "Q872          Radio      Q872-wikipedia_sitelink-1e49e4-0  wikipedia_sitelink   \n",
-       "Q5070802  Emergency  Q5070802-wikipedia_sitelink-41cebc-0  wikipedia_sitelink   \n",
-       "Q213449      Eating   Q213449-wikipedia_sitelink-1e88be-0  wikipedia_sitelink   \n",
-       "\n",
-       "                                           node2     ignore  \\\n",
-       "node1                                                         \n",
-       "Q8068         http://en.wikipedia.org/wiki/Flood    23841.0   \n",
-       "Q1368         http://en.wikipedia.org/wiki/Money  1312979.0   \n",
-       "Q872          http://en.wikipedia.org/wiki/Radio  1866867.0   \n",
-       "Q5070802  http://en.wikipedia.org/wiki/Emergency    80429.0   \n",
-       "Q213449      http://en.wikipedia.org/wiki/Eating   104645.0   \n",
-       "\n",
-       "                                              url  \\\n",
-       "node1                                               \n",
-       "Q8068         <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368         <http://dbpedia.org/resource/Money>   \n",
-       "Q872          <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802  <http://dbpedia.org/resource/Emergency>   \n",
-       "Q213449      <http://dbpedia.org/resource/Eating>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q213449   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract node2_labelsDF  \n",
-       "node1                                                                       \n",
-       "Q8068     A flood is an overflow of water that submerges...          flood  \n",
-       "Q1368     Money is any item or verifiable record that is...          money  \n",
-       "Q872      Radio is the technology of signaling and commu...          radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...      emergency  \n",
-       "Q213449   Eating (also known as consuming) is the ingest...         eating  "
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "compatible-september",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "subject-access",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "silver-structure",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "dramatic-projection",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstract'] = wordsimQNodesListWAbstractsLabelsDF2.apply(lambda row: row.node2_labelsDF if pd.isna(row.abstract) else row.abstract, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "accessible-trunk",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                     school holiday  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "colored-imagination",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "422"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "labeled-eagle",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = wordsimQNodesListWAbstractsLabelsDF2[~wordsimQNodesListWAbstractsLabelsDF2.abstract.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "welcome-orleans",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.to_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "quick-chicken",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "potential-format",
-   "metadata": {},
-   "source": [
-    "# Fetch saved files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "defined-harris",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF1.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "documented-replication",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2 = pd.read_csv('../data/wordsimQNodesListWAbstractsLabelsDF2.csv').set_index('node1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "apart-divide",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "# Sentence Transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "parental-craps",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "initial-coating",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "documented-alignment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.550719976425171 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertEnc = getEmbeddings('bert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "continuing-impression",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1.24G/1.24G [02:08<00:00, 9.68MB/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.648171424865723 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLargeEnc = getEmbeddings('bert-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "responsible-national",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0568156242370605 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "distilBertEnc = getEmbeddings('distilbert-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "interracial-mitchell",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEnc = getEmbeddings('roberta-base-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "amended-powell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.765424489974976 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "robertaEnc = getEmbeddings('roberta-large-nli-mean-tokens')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "divided-donna",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "characteristic-religious",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "quality-sponsorship",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncMap = getMap(bertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "electronic-parcel",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncMap = getMap(bertLargeEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "confused-snake",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertMap = getMap(distilBertEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "vanilla-subject",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseEncMap = getMap(robertaBaseEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "familiar-soundtrack",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaEncMap = getMap(robertaEnc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "subjective-leeds",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "bronze-sally",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "detailed-imperial",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def checkSimilarity(row, encodingsMap):\n",
-    "#     print(row)\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "funded-prototype",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "improving-nevada",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "bertLargeEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLargeEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "conventional-white",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "distilBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(distilBertMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "related-garage",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBaseBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaBaseEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "portable-joint",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "robertaBertSim = wordsimDF.apply(checkSimilarity, axis=1, args=(robertaEncMap, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "mental-forestry",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF[['Human (Mean)']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "round-cornell",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-79-5105af3a2513>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = bertEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = bertEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "exotic-fundamental",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-122-3b87f5bd1dcd>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = bertLargeEncSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = bertLargeEncSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "latter-cleanup",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-80-c904909da8f6>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = distilBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = distilBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "excellent-meeting",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-134-f159fb4d0068>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = robertaBaseBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "south-rwanda",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-81-92c04e1dd63d>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = robertaBertSim\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = robertaBertSim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "proper-stations",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-82-96b13f46512b>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertSim'] = wordsimDF1.bertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "legendary-campus",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-123-cdaec8bd10a3>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['bertLargeSim'] = wordsimDF1.bertLargeSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "ambient-victor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-83-383da2239499>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['distilBertSim'] = wordsimDF1.distilBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "velvet-ethics",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-135-c2b58af44ba8>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBaseBertSim'] = wordsimDF1.robertaBaseBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "colonial-coordination",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-84-ce07de4ac656>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['robertaBertSim'] = wordsimDF1.robertaBertSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "informational-fountain",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.float64"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(wordsimDF1.iloc[0][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "japanese-order",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.426863</td>\n",
-       "      <td>0.360219</td>\n",
-       "      <td>0.436207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.339922</td>\n",
-       "      <td>0.433100</td>\n",
-       "      <td>0.427664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.686578</td>\n",
-       "      <td>0.706978</td>\n",
-       "      <td>0.645407</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.296175</td>\n",
-       "      <td>0.373679</td>\n",
-       "      <td>0.371295</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.340811</td>\n",
-       "      <td>0.390540</td>\n",
-       "      <td>0.488393</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim\n",
-       "0        5.5360  0.426863       0.360219        0.436207\n",
-       "1        4.1250  0.339922       0.433100        0.427664\n",
-       "2        6.6250  0.686578       0.706978        0.645407\n",
-       "3        2.0625  0.296175       0.373679        0.371295\n",
-       "4        7.1875  0.340811       0.390540        0.488393"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "marine-general",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertLargeSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "living-condition",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.robertaBaseBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "hearing-adjustment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1.bertSim *= 10\n",
-    "wordsimDF1.distilBertSim *= 10\n",
-    "wordsimDF1.robertaBertSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "suspected-plumbing",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertSim</th>\n",
-       "      <th>distilBertSim</th>\n",
-       "      <th>robertaBertSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>bertLargeSim</th>\n",
-       "      <th>robertaBaseBertSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.602192</td>\n",
-       "      <td>4.362067</td>\n",
-       "      <td>[[0.4949737852167514]]</td>\n",
-       "      <td>4.196118</td>\n",
-       "      <td>5.313815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.331005</td>\n",
-       "      <td>4.276645</td>\n",
-       "      <td>[[0.4818230866992358]]</td>\n",
-       "      <td>4.045933</td>\n",
-       "      <td>5.239753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>7.069776</td>\n",
-       "      <td>6.454071</td>\n",
-       "      <td>[[0.6407051485261465]]</td>\n",
-       "      <td>6.215223</td>\n",
-       "      <td>6.513252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>3.736790</td>\n",
-       "      <td>3.712948</td>\n",
-       "      <td>[[0.6186497481059658]]</td>\n",
-       "      <td>3.581833</td>\n",
-       "      <td>4.266170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>3.905397</td>\n",
-       "      <td>4.883926</td>\n",
-       "      <td>[[0.8556663921383962]]</td>\n",
-       "      <td>4.322380</td>\n",
-       "      <td>5.111274</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   bertSim  distilBertSim  robertaBertSim  \\\n",
-       "0        5.5360  4.268633       3.602192        4.362067   \n",
-       "1        4.1250  3.399217       4.331005        4.276645   \n",
-       "2        6.6250  6.865782       7.069776        6.454071   \n",
-       "3        2.0625  2.961755       3.736790        3.712948   \n",
-       "4        7.1875  3.408112       3.905397        4.883926   \n",
-       "\n",
-       "                  amanSim  bertLargeSim  robertaBaseBertSim  \n",
-       "0  [[0.4949737852167514]]      4.196118            5.313815  \n",
-       "1  [[0.4818230866992358]]      4.045933            5.239753  \n",
-       "2  [[0.6407051485261465]]      6.215223            6.513252  \n",
-       "3  [[0.6186497481059658]]      3.581833            4.266170  \n",
-       "4  [[0.8556663921383962]]      4.322380            5.111274  "
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "incorporate-punishment",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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Ru3Cvf+uHCu78aA0AOR21nLdxFhNrVuM1KcwYBzNHmWn3jSXUMhEZiUNBZ4RpKabkmayNCZAZDnN7cyuT/QH+EroGV0WQ8evmYfO7acvIo/6oU/EeegRWu3WnE2I2k4rJ3Yb2/Vx8c+bgXbgAGQqjmHTIDFKWrzO/j4HFMWaq90DoALnOXIYlDWN48nCGJw0nLzYP2dpGsKiYYEkxweJiQsUlBIqL0FvbuuqFrSpWpwbtGpFAdGIy4fLLcM/6mrJQGY/+3sFnFy756Q/nVw7d62XTqNF4LPDZP4/mvqmP/9JN6sVeQNu8gKa//omWpW2gCr6eGs8rQ9o5LO8I7jnkHpJsO+QHRkYibDj9ZNqrS1l8TRbX16yGG9eCffcC38+B/2kCx9/G86+MY9NmA5d/qZN8220kXPz7nRatbPHx0YrqqF44GNUJ2ytLGPXtB/QtWo7HYmD6WMnnoxQC4ZEEmiYRDkSHYCbbRhJS/4vH7GWyz89tza1kRSK8E5xMWVkKw4qLyHXX02qJYU7fw5g/aDIy1rVL2127WcWhh0jdtJKkNYtxrvgB1esGg4Ij2YeeFWRjH1jiMrHcYqbYaERuJ1EoQkEg0GTUZDHGFBMl9KThDE8eTl9XXz4t+ZQXVr+AbGvnTDGSU4pqsazfTHupbZe3tGxcMmMPv7hrAtWQmnpQqU62xdrhA1EDkoduSeGNS7/9pZvTi91AtlXT/sgfaZixDi2o0DAujfsPaSfksnHH2Ds4Pu/4Xb6njc8/T9Nj/+Kls2N5wFCCfeRFcMKvw/Pyf5vAgfDS1zhz+d849zMzQ0skue++g3XQoH06hn/tOpqeegrPt98SdJj5eJxkxnCNsXmTOafvRRTGDqHNH+Djknf4sORldC3Axe0dXNregVVK3MYkir0j8K8O4iouIqIa2TB4AguHH0V5bOo2Fhqdk4zbWWmousag5lLG165lfO06Uvyt6EAkwUBmZjNkBVmXZGCZxcxyi4W1ZhORzpdVYECgIFBQhCAsd4wgeIp1KFeUziOhNkT1ily0+nYcF1xI0sW/R6uqpOLCaNS14lTIDzhQ2jxddRW7fafmjoa0tN88sa+bMgqlxsf9v1N48fZFxJhifukm9WJ7hP34/v1n6l+eTqBFReTG8uLJiXxlL2dq9lT+fMifSbTuWpIObiml+JSTWZyvkXLJOI5f+TFcuxzifx0qwv95AkfXWfHKZK4Ot/L0v83ExiaT98H7KPZ9n5zwr15N45NP4Z03j1CslY/HKXw6NMCgjJFcMvgSJmVOosnfxD+X/pOZpTNJ1yS3NjUxxednK5UFZRYtVTm0L61CBkPYJ0wg/qILsU+c2KXe2a2ddDCCVrQJ8w/zcSydj6NyCwCBBBdx6e2kptZDQoS1FhMrzGaWW8ystJjx7kJ1ZNIhLCQn/iA59zuddquRfx02huWuMeiBNJxIXp1xH7UFIe4+VSXT8y8SNY2s9nrS2mpJbqkhobGa2IYqLO627ttusRLJykXm5KHk5WMqKMBaUIA9OwO72fibcEYpO/cY/CsqeOlohWm3v8iE9Am/dJN6sRVSEv7mZRr++SgdJRKD08i6343ngfjF2E0O7hp3F8fkHrPbd0zqOiW/O5e2jWt47fahPFk6H1FwJJz56s94IbtHL4EDVC3jgY+msaHVzt1vS+JOP430Bx/80YfzLV9B01NP4l2wkLDLwfQJRj4Y2EF2QgEXD76Y4/OOZ1XjKh764UGK2oqZEIxwe2MDeeEIJA+Eps1E/Bpttdm0bjAQafdjys8n/sILiD35ZBTbrlUY2yNUVY1nzhzcs2fjW7oUNA1DUiKOwWnEiIXYEjpYYTNzVWoS/p2QeHabzuUzFfqVR1gxwMqrxzuoM7UCoGLi2A3xXPRxFfeep9KaEUs6j/UwN9zWFNEa8JDdUU+Ou45sdwM5HXVku+uJD3bbsfsMZiodyVQ4U6iNS6MhIYPmpAyCrkSsZuNOY23sLu7Gtg41W9f3V8yN+psvo2XGfD4fJbDdeh1/GPaH/XLcXvw06EXf0/y3P9H8QzugwOmTeXB4Mys9Gzk652juHHcnCdY9h6ho+c+b1P/1rzx7ooFrjz+dwrn/giu+hfQRB/oS9hq9BN6Jjo+u5JSWeZy91MXkb1rIeOxRnMcd95OO6VuyhMYnnsS3ZAmRhFhmTrTwTmETCc5ULhp0EacWnMqnJZ/y1IonCYR9XNju4Uq3D9vQsyGhAIq+QhbPo6PSQsuWRAINGkqMA9c55+I6/zyMKSl7bsQ20Nra8Hz3He7Zc/B8/z3S5yNkUVmSp7Opn4lxsY2cEvRgACoMBopq7CTOt4EueOUohW+GCqxGG6n2VFShIoTg0ufKsbb6uP5KFYQg15nbNTE6PDk6ObrVRnr7gExb1wNNLUTKtkDpFtTKckxVZdhqKrB0tHa1PWQ005SQTq0rjerYVMpjUthiT6bS6MQb3rd31dDl+LNtLBDDTuccenQG28QIsZtVYt56Eu31d1iTI/j6hvE8e8xLvd6VvyBkWyXux/5I/fT1RHwGHIcMYPZ5h/B43ds4TU7uHHcnx+Qes1fHClVVU3zSCaxMC1F9zwXcMv91SOwLF316gK9i39BL4FvhaeSrF8dxa6yNFz9OwVnrJu+jjzBlZvykw0op8f3wA41PPIl/+XK0JBezjojj9dwK7NZYzu1/LsfmHstr617jk5JPSBZGbqmv5RjsiMPvgL7HwsYZyDXv41+xkpZNdtzVVlAUnEceTvxlV2EdMnif2lTnreO5JU9SNudTDilSGL/FgKndB6rAnujHlhrCV2/BW2fCkhAi45BW2uMky81RHfry2EQ2ESKpVeep5zTenqTw0aEKdqOdgrgCyjvKaQu2AeA0OXtYuwxOHIzNuHcjCK2tjeCWLV2WMaHiYoJFxUQauwO0C5sNc34+al4+5OahZecSzMzF50rCH5E7jdS3bVyT7WOcbD9y2F3c6Sk1S7ll8Tu0OOCaq220bb6PraksCpIdfP2nyfv0XHrxIxH04H/7Hupf/hR/oxFzlovQLddyT/AjNrRs4Ljc47h93O3EW/Yu4qeUkopLL6N12SL+ck0Cr4+4EMdnN8H5H0LB1AN8MfuGXgLfBnLhs1y78p9siTj452sq1r79yHnj3z1c4H/0saXEO38BjU8+QWDVamRaEt9OTeb5jI2YjFZOLTiVEckjeG3da2xo2cBYaeaO6jIKYvPh6L9A4dHQXgXrPiI0/z1avy+nbYsNPaJgLUwn/rI/EHPi6buNOdIebOflNS/z1sa30KXOOf3P4fLBlxFXPBf/v2/DXeSjZaOjR53Eq/9AzIBYzMUvIerXdG13C8Hm9XFYV1m4+hoDzc5uyTMvNo8ES0KXlN7ga2BLe1QXrwqVfvH9uiT04UnDSXOk7dO91NrbCZaUECzuNncMFhcTaegOPy+sVsz5+ZgL+mDaZgLVmJGxS1PRnSGi6d0RBbch+nlFTWyat5gb3ouq2n5/o0pD9c3IUNQM7crJ+dxx3IB9uq5e7CN0nch3L9L4r8do2wSq3Uj8tVfx36GSF9a+hNPs5O5D7ubInH2LFNj2wYfU3nUXLx+tcPh1D3PiFw+AwQxXzvvFglbtCr0Evi20CLUvHMopVh/n1hZw7OsbSbz6KpKuu26/nUJKiXfuXBqfeJLAunWQmcaCYzJ5Jnk1moCjc47GaXbyeenneEMefheEq2vLceROgqP/CmlDowdqLkFb+g7t779Py3IPYa8BY6wB10mTiLvidtTkrK5zBiIB3tzwJi+vfRlPyMNJfU7imuHXkK5JmHkLbPoMmTyYprbDaXrzE9A0HIcfTqS1hcCq1QAYs7KImTqVmEPHYDUVw+wHKX7fhMkZ4ZyLbEzx+jjD7WGZ1cry9IGsDLfijkQ9PlNsKRS4CjAIA0IIPCEPS+t3fN6qUEl3pOOyuGgNtDI5czK3jrl1rycztY4OgsUl3dL6VmKvr+8qIywWzPn5nZYxhV2OSsbMzL0m9m82NXDxq0uICXl5b+a9ANx1gcpRx9zFJcPPwmz438zE9HNCFs+l5e830bSoA11TiD/9WFquOJ+7V/6NTa2bOD7veO4Yewdxlrh9Om64oYGSE05kk8vPRzeM5NW8sxDv/g6mvfyLBq3aFXoJfHuUzuM/H53L3xNcvLJkBI45y8h5/TVsY8bs19NIKfF88w2NTzxJcONGlJwslp3Qhydcy/DqfgYnDMYT9lDeUU68auWm5hZObG1EDP8dTPkzOLtDs8ratbjffpKW6d/jr9VRDDqxI5OIPe9cZmbG8sz6V2nwNTApcxLXj7yevrF9YMnLMPsB0COEBl1NzTvr8K9aTewpJ5Py5z+jxkRN4sL1DXi++Qb3nNn4Fi5ChsOoLhfCbCZSV0fwGCsXjAzzWH0jR/r8hIEmVaXeoLLAauULZyylP4HPjs45mrsPuXufP8RtobndUUm9pKRTHdNJ7HV1XWWExYIpPw9znwJEXj6hzFx8aVm0xybRHtLp8Idp7/w1e0O8vbgCpGTGZ7eiRiTPHq/wZfYhBOtOZ+mfjyTRYd5Ni3rxYyGbt+B5+noapm8k5DbgGNmX+Pv+wWu+r3lp9UvEWeK4+5C7mZI9Zd+PLSXV111H27ffcPMlCk/+/gMKP/wjdNTALxy0alfoJfCdQHvvIs5r/4FWUwJPvmlDhCLkf/wRalzcfj+X1HXcX39N05NPESwqwpCfx5pTBvGE8weaQ62oQu1ythlhjOfOis30j0iYcC0ceh2Yt7E9lhL/dx/R8tLztC+vQOqSpX0VNo61Mm3K7xg9+ipoLYXp10PVEmTe4bQrJ1D/2POgqqTdfx/O44/faTt1qdPUVEnTt7MIfjMXy5yot6UuYFmBoGhoPMv6CCrVth3qGqQkUdMII2jehXTaP74//Vz9UBUVKSUbWzayoWVD1/682LxutUvycHKdubsMICWlxBOMdBFuuz/cg4C3/nxtHZiqyrHXVhJXX0lScw3pbbUk+buvIagYqIpJpjwmNRozxplCS1IGgcQ0nHYzf3nzesxtPqaPFfz7sAyOj/8/HjptyH5L4dWLTgQ6CP73HupfmY631oQpJZaUe/5C+fB07p5/N0WtRZzc52RuHXMrsebYH3WKji++oPqGG3nzCIWYiy/kttTJ8MoxcNw/fvGgVbtCL4HvDO1VbHj+EM5NcXGx4QiOfvhbYg4/nIwnHj9gtslS13F/+SWNTz1NqKQEY2EhRdNG8oRjEVXe6h5lzzEk88eSZcRak2DKXTD8/C7pYGndUh5b/hiVW1Zx9mobhy/1ofg0LK4Q8f28OLP8CBW0KY9Q+8E63F9+hWn0CPS7/khjLDT4Gmj0NUaX/uiywddAs7+ZiIwAYPdLXn5co9YFa3IFo4skiW7QhaCjfxqB8cMwHn4oianJJNdvJG7VOyhVOz7fZkVhZeEklsW4WK61s7G9FE1qKEKhn6sfTlMcP9QtBGBQ3FhK3RvwaVGTQ5Nw4FILsep9UMO5aL4s3H4RJetABG03GWwUAU6rkdhtftv+jydMcmstrsYqHLWVWKrLUcpLkfXbSOwmE6b8fIIbN3Ztu/EKA+9fPR+H5bebLu5AQEpJg6+Bso4ySttLKe8oZ3TKaKbm7MWEoK6hzXuBxscfo3WDgmI2kHTVldgvuoTn17/MK2tfIcGSwD3j72Fy1o+fNI60trLlxJOotPp48JIYPjljBjEfXAkVC+BXELRqVzj4CDzohoofoM8U2IfJqh0w9xH+ueIpXotz8p/WMzA99w6p99+P6+wDmxJMahodMz+n6emnCZWVYR4wgMqzJvC0/Qc2tHaThUkxcFfYzqkVa1CSBrD50Kt4vGkxc6vnkmxN5urhV3N07tE0t9XQ+vpTKO9/ialdJWDTaYmHpFoFRcJ/Jxv4aKxEbmf+5jQ5SbYlk2xLJsmaFF3aosuUL1aiPvoSMX+awHHmxVw3+ArON0/BM2c27q9nE9y8OdrGvn0xTTocbcIkPJm5aLWrsFR8R1bxm9iCjWwPnxC8aBvHPJODYmOAiLUWoYS79ofbRqFHYgCBanBjsFWCqVO/LVVilGwSjX3JtA6gT8xgMmLSdiRomxGHyYDyI8z9NI+X0JaSLt16sLgI79x5PZ+f0YAlP6pXtwwZTMzUqZiysnZxxIMLgUiA8o7yLqLuWraX4Yt0R8A0IIgguXn0zVw06KJdHk9unk3bY7fSON+NFlaJO2EKSXf+hY16DXfPv5vitmJO6XMKt4y55UdL3VtRc9tttM2Ywa0XCa6c9jdOcvaFp8fC5Nt+FUGrdoWDj8DXvA8fXAq5E+HkJ3+8y2s4gO+ZsZzuBLMzm8c/SyKwfCV57/8Xc0HB/mnrbiAjEdpnzKDp6WcIV1ZiGTyYxvOO5AXbYn6oW7zbuun2dNwhN+5wt4OMkJJRRZJbP+g2i2tMlqyfEsCWYCI5bRRJBUeRnDeVJHsKFoMFiKYgcwd6qiPsN1yGDIXYfEIFD7oEE20PIkKZXfuN9bX0K1nOiIrVDGwuRUXSaI1lYeogFqUNZk1iPhYlxGHqBo40reEM+dVOr6PdGM/81LHcZli/w74kaxIjkkdQEFeAQTHgCXtY07SGNY1rCGjRzEWp9tQe1i594/tiVIz7/Cx2h6anHqPxqRcAeOlohWMsIxjQ7iBYXEy4OjpyMg8YQMxRR+I86ihMBQW/eg/T3UFKSZO/qQdBl3ZESbrGU4OkmzfSrMnkGhzkBQPkttaQ11FPbjiCS5i4I97OLLuNy4dczrUjru15T5qK8D5/A/XTNxFsM2IblEfKX/+J6JvPsyuf5dV1r5JoTeS+8fcxMXPXYaD3Fp7vvqPyyj8wY5KVtacN4bVjX0N8+kdY88GvJmjVrnDwEbgWhleOheqlYLTBkffDmMt+nDS+6Qu+//girkpN5obsi5h454cYkpPJfe9dFPP+n6TSdI3WYGuX2qLB10CTuw777KX0+3QNsc0BtmQaePMwnTW5YqcmTcMCQZIdGSTZU0iqWkFKwENS/1NITD0V/vo04Y2bsY4ehdeZAHO/QYmEkdkO0vJriUtx06bGM890KJ/LCSwI5tER1HvEXcnpqOW5Of/kv0OOwHP4F8yxxNFUdD9b7Z+hW0URZzWSoQQZVb2eAaUrySxehSEcQrPZCYwchzxsMvroQ1AdDiyeCuKq5+Ks/hZnxewe19ShCM7ISEMVBu6PGcOGuGRWEWB12zrqfVG1ht3o6AzGNQyHyUFEj7C+eT0rG1dS542WsRqsDE4c3EXqw5KG/WTJrX3GZ9TcfDMAt12skjV6Mk9PfRqAUFUV7llf4541C/+KFSAlptxcYo46ipijj8IyePCvlsyDWpCKjoodJOnSjlK8YW9XOavBSq4zl1xnLnn2NHIDPvJaKsmuXI6tcVO0kCUO8iZC3uToLyYF7bnD+IsNPjDD2f3O5s5xd6L42wh9eC8Nr8/AXWnBmOAg+a57iTnuBNY0reHu+XezpX0Lpxeezs2jb94vsWc0j4ctJ55Ei+LnmvP9vHnqe/QzOOFfQ2DU7381Qat2hYOOwBfWLOShBfdzS8UmJnVEkyGQcyic8hTE/4ig+2+eya0dq/naYefd+FuJ3HQ/rgsuIPWuvR9WSSnpCHV065f93QTd6Guk0d9Iva+eZn9z14TlVggECdYEUo0JTFytMfbLcuwtfjZkwnuTFNbl9OyYpqnx3F28kq1ThfLiL2n9voSGfzyCYrOR9uCDxEw5giP+71taqus5rmwRJ22ZT3zQTXOME9lXYXRuEVZjmCqZyAztEKZr41kncwHBZWs+5eQt85l5wjg+G7gQr3sILbUX7tV9MEdCDG8sYkLtWsbVrSc25CWsqKxMLGBR2iAWpQ6ixRqLgQgjRRGT1NUcoaxkkFLOMrOZS9KSOcXj5YGm6HNdr+fwpVLAd2YXJVYNzVqFaomqVKSuIoNZEMhDiSQgBAhzPdJchjRWg4iORJRICqZwPiYtH0ukD0ZSMCgCVQiU7ZYGVaAIgapsXUJGTTFnvno/AE+epLBosJMjbc+hqgqqQld9u7uNjPVLSF+ziMSitSi6RiAukcYR42kePh5330EoBgOqsu3xtzm/wo7btv7fvs42ZRWxk/1CoCjRjrYj1EKVt5wqTwVVnnIq3GVUuMup8VT3kKZT7andRB2bR25sLvm2dJKbSlDK5sGW76B2JUgdDFbIGR8l6/zJkDoUlO0mrysWIV89jsf6juPVUDUnmfpw3UdraVtrAFUl8bJLiL/yGkIGeGblM7y+/nWSbcncN/4+Ds04dK/et71B7X330frue9x9gcLYIy/gtrG3wax7YMGTv6qgVbvCQUfg5R3lnPHpGQS0AGd3uLkpEoM10A56GKbeC2Ov2FEab6uM6s4VFRQDCCW6LlRoLaPp3ydySk4OBUmDeXhJH9refo/MZ54i5vAj8GmBHhN+jb4oGTf6G3tMBga14A5t3ZmeuUvXbI0uE62JGBQDYT3MR0Uf8eKyZxi6qJFzfjDiaA9iGzsW45UX8jf/h3xbPbfr2Ne0tnFpnYfaZSl4KwX2ww4j/W8PYUiKOpp0BMI0uqNtkqEQ+pxZ6O+9hSzaDE4nlomDSchtIKZ1AUKPEIrNxZ1/Eg0PzUIMH41n9AYuNjXyp+H3Myn96J0+CylBlxJNj/62rutSooU1WLcGw6J5mBbOQ62LqhuChQPwjTkU9+gJBNKy0AHV30JSw0K+avkv76v1XSaL26PeMZANsUOZb09irSFEbbiYlsgWJBogcCrZuNS+OEUeAgM+vZFWfTPtejERolKlAQd22Qe77INNL8CsZyN1U/d1SNB0HU2PqpfsnlYefPN2AD6cIHhnsoql7k5kOKn7enWJts21WwNextSuZ0LNGkY1bMKsR2g32VmYNogFaUNYmVRIeH+arIkIirEZxdyAYmpCMTWgmKNLoXa/l1I3QiiB3FCYgkiQGf4T0IPJEE5CFWaMQmeYsoUJYi2HiLUMYxMmIkRQWScKWaYOZYVhGJvVfmiqCcM2nUa0w6FHR3Km+9+c2v4m0xtcJCyxEu+BsoGD+fboKwnEJeCWRawLv4hP1pFtnMIQ63lYVHtX57ltJ7ZjR8VuOi+BQRHYN64m8/4/8d2EBP49VeHOoa/iBCZ8OonW9MkUT3pyrzpEg7JtZw8GRcFlM/4so6uDjsAB3t/8PvcvjEpFuaEwDw+8nEFli6DoK8ieEJXGE/pEC3fUwqP9dzhGCGgyqDSo0d8LcbFsMpswRiSPvRrB5oW7L1Godu5oFmfVJcm6JEmXJOuCZClIktFlMipJGEjCgGVrJ9HVcXSub+1AFAMShVl4eEKrp1wGGaE4uNGcyzDhpG1JHU1zytA8IewpQWxD3VwzxMV6s5kRxTpXf6YTE5SkDGsnodCH6HMEFBzZ3Tkpao91iYJvYyUtny3Es3QjqAqx4wfhGhTB2vY17ioLVd/Hkzmpmf8MNPNsXBzfHf4MLktc5zEMPdu/x20qKApSSoJFRdGgW1/PJrB2LQCmnBwcR07FMWUKlqHDCAudiz6/gGp3Be+mnUhqxWLU0m93+g5IoRJJG0FH5jhWujJZTpA1rWvZ0LKGgBYl/xRbOgNdwxnoGkqMyUV7sJVNbWvZ1LaGam85EHUuyonpS6FzMIWxg+kTOxiXKQkpJbqMTjo7T5kMmmRpIfzjDAMXFtzOmMSjkERHX7oOkmhnJiVddSO6jub1YV21mJjF84ld9QMGv4+IxUrT4DHUDTuE+v4jCBgsaLpOWJPROrokrMnOpU5Ek4R1nYDehlevwydrCFBHQNQRFvVElCYQ3d+z0GIhnIwMJaEHk9BCSYhALOdqi7naMJ1E0UG9jGN88Cn6iioOVdYyQVnHWGUjMSJ679bpOczXB7NAH8RivT8+LLv9JhP87fRvKWd++hAQgr6ikgfaXyFlVTOBZhM1iVaePi7Ehvg8AtW/wxQ/F2P8fGQ4lkDtNDRf4W6Pv68wR0I8/c2jKMLPLX8I0N54FpH2UVyhTudO49ucGPwra+WPT5N301F9uXbq/m3zznBQEriUkpu+u4lZ5bOAqB3yNYMv42I1EfWLO0ELwdR7YNwfCKPx1uxb2VLxHQ2hDhpUlUajidbdqMxHttq4+WU3LTk2ii/pR7LBQpJiJFmYSBYm7FIg0EGPgK5Fh5W6BlLbp22LCfAvg481ikaBJrg+qDI5pCOkDroOQTe6t4PWYhvNGxxoQRVbcpiwJgg3GyhLhidOVqlKEtzZ1MLpHg/mvXysIbdKS5Gd9k53fVtSEF+jGdWsUXhKPRdkpKADb9XW7/FYe0JEKmioaCjoKIR8Kr5qExXVicQ0+DBInVazg0Wpg/ghK5OisZ/hD+fir7wEC2HGKRuZqKxmkrKavkr1Ts8RlAZWygK+1wbyrSmFYquOtFag2spQDFHpW4/Y0Xy5aP5c9GAKQoRRrJWo1nJUa1WXRYwejkPz5aD5o7+3pz+H0+unJh5uvtyMr300wbrT9vk+GLUIw5qKmFCzlgm1a4kNeQkqBpYl92NB+hB+SB2Ix2RCMTWjmBtRTJ2/znWhBrqOJXUDeigRPZSMHowuCSUiw8komBEIhACz0JgmvuFK8SGpogU/JqyEAGjBSTwdAFSQxlJlKMuUIaxQh9ChOFFE9BiKEAjoXu9cQnQ5sngxp819C1vQx7rC4cQNCzJg6XI6yqxErEbShzZT3zeXW1LOpsL4Ytc1JOqHk8UZGISNqNGQQNn+HJ1LJbq6k22ic8Dds+6Iz96g/9xPefB3Dqr75DA19n5MRLhq1ek0W/P476CnonU7L0zZ5jq3WjB1nWO761UEHDkwhUzX3kcN/bE4KAkconE/pn06jVDYx5COJuZazYxMHsFDw28gY87DUPQlZB1C87EPcsq8G2gPtu/0OAJBXmweg9UYzGXf819nDOn2dJ73nYH/oUdJvuVmEi69dId6zZ4gbf4wYU0nFNE7l5KQphPe+r9rn+wqF9J06vwlLG7/DzWhlVhFAn3NZ5AiDiWi03Wsfp7FXNTyOMlaPV9YjuV1cQ7Hz/2Q0SXd9/GuYy6gaOTbPdp1XUsbZ7vdLAqP5PHINJplDCo6itAxoKOgo3b+FHQcIR9jyzdw1PolGPWoft44MsB5k21M8Zn5W3tJj+NvUApZYJqAR43DpOiYFIlBSEyKxCgkRqFjUCRGNIy6hhoOYwwFUUIR1HAYEQqjhCM4vI1kWzfw+fCH8NeqpKz+gaT1yzAE/IRMRlbkRWgbPpmYcVeiOWK6PiBHsIHMloVkNC8gvXkR5kjHTp9rRLHQHD+CxsQxbHAVsFoNUO7bSLl3LS2h6KSnWbGS4xhAvmMIuTEDMClmqnxFlHnWs8W9lrZQEwD3vqUzqFxHE3D+LSpxtjT+Of69HcmGzg9coYschAAhRDc5CAFIOvzNNC2djf7dt8QuWout1YemwLocwQ99BUv6CtocgkRrEjkxueR06qdznXnkxuaQ7khHFUrX+XcYzmsRWP0ufPYniATYKYaeE9Vh502C2Mydl9kNtLY26h54gI6Zn2MdNhRriqDlq1Vd+xMuPp+Ea25ALfoY36fX8OSwY/hPe7fF0RfTviDD8dOCye0M/jVrKDv7HEon5nPHoRW8d+J79IvvB8vfgE//CBd8FDVD/g3goCVwiDq1XPLlJZwc259xRfN4KDUNqZq4a+ydnOjxIr64DSJBIkfcRfOwM2jwN1Pvq6e+cR31W76moWkj9Qo0WBzUKxDsdGQBQEpu+khndJHk5WsL0frlkmJLIcWegkXE85ePqwiFnMhwLEjTXrVXGFswJ32FMXYlUrMSbDqCcOt4kN2mbwm0c7fxDU5VF1Csp3NH+DKW6n2ZVvwdF67/grBqYHNcJn3aa4gJ+1mQNoi3R/ahfujMHue6oM3NWe0BPgiewEva8fi3GwIrAswGFbNRYUTDZs5Z8SnZzZU9ylSmprNu0FSM5gj9PavJ823CGWhBiyi0yiSaScatx6JGNNJNEmskiO7zoXu96F4vaD0nbHe8H5B9gortgcVgsqGHQvh+WIx79myqPv8IR3sQVAXb6DHETJ2CY8rUntEjdQ1qVkLJbCieDVVLoqObncHkgOzxkDeR+rTBrCDIsoYVLG9YTlFrERKJQTEwMGEgo5JHMSJ5BOmOdLa0b0H85V/kLKgA4E+XRUc8fWL78PGpH+/2+sJamEpPZbeFxzYWHx2h7o7HJIxMcKcwYbNC4eoW7LVtSCEwDxtC3NHHEnPUkXtva+5rgc9ugnUf9txuiYPcw2DjDHCkwE2bflLgJs+8edTeeReR1laSzpqKqWk2DfMDhL3duv386Z9iLixkSe0S7p11FZUyyLlZR3J4/zO5+bubsRqsvHDUC/SJ6/Oj27E9ZChE6bQzCLY1c/EFbk4Zdi53jLsjOqJ9ZtzeBa0KB0A1/TQ/k/2Eg5rAAZ5c8SQvrH6BRwzZDClbwju+wQyfU4shLo6stEIs3mLUYCWG5AzUCReiZg9AdcVhiI9HtQqUzR8iFr+I7KiiQ1GoMhg4JyMVgMtzzuHQOz4gZIB/XZtJldbc48PbCotix2FIIMaQiMOQgMOQgF2Nx6EmYlPjUTCyumMma91fogiFITEnMdRxKibFjr5V16rr9KubzpSKJzBpPr6MP4+PHWdBSzsnf/4iBdWbWJoznBfHnE2DMGPwejhn82ymFX8HQJ3NxSfDU2lL34QlBJYQWEMw0h2irwe2BPKpC8djiYSwRoJYI0High6St3Er31toikA1SoyGCMIo8RlsKAlpxOb0Q7E7UOz26M9m28m6DcVmB6lTdfWVaI11VF89glGXPN/D5K/Z18RNz5/K2M1wdEUs4ZJotENz//7RoFtTp2AeMKCn5Bloh9K5UTIvmQ1tFbu+CEts1HopbxIdGSNZSYDlDStYXr+M6tK1JDWHSG+GAd5YDvu+O275UycqzB0S/bCPyz2OKdlTGJc2rqcpXidRV7ore1gdJVmTyI3NJc8ZtfLIi80j15lLmj0NEdHQmpowpKURKinBPWsWHbNmEVwfDTewS1vzsB8qFsGWb2H+v3a8zpEXwuhLopYi5Qvg9ROjSUWu+DZKZvsI3eej/pFHaHv7HUw5mSQMDtK+pApfgxlzdhop9/0VJcZJ5dVXofv9fP+HQ3jc+B2Z9nQeqKlkjLDClXPZ7K3myllXEtbDPDv1WYYkDdnntuwMjU89TdNTT/HupX34JtvN9NOm4zQ5YeNMeOfcaNCqASdFDRvayqPvSFtFz3VPPQyeBme8sttzSSmp89YR1ILkxubul/ZvjwNC4EKIG4HLiM7drAEullLuYpx2YAl87mWn4d+4kTaHIN8Mjk37eF0GA2pcHAabiipbUWmn1Sb4KNFK/6xhjNP64vvve4jCvvDQo3SYjdSFWzstURpo8jfQFmyiNdRIR7gZT6QJn94G9GyHokusQXAEE4jxp2H2W7B40jC5E0kO1nJqeA55Wj1V4QS+Dw4mFDFydPkSlG2OU21P7CJfqxba60sMGEE3SkwGhYA1Cb8tgZjmuh4JFbpux3H9eVlsxJk0mJOST8Uw63NYuQwANTuH+CuvIP6kE1GMxqhZ2cdXQ0PnsPikx6O2tXuJcE0NpacfT1swyGtXZPHQxe/1CGr1ffX3XPX1VZw/4HxuTD4b9+w5uOfMwb98OUiJIT2NmClTiTlyKrZRoxDGbZx4pISWLd1kXjoPtrFv1sKCkNtAqMMQXfpsBAMxBFvCEOz2Dg0awLzNwAzgwbMUVvXZuXRmVIzkOHO6yDkvNo+82DxynDm7tGvW2tqouOJKAqtX45g6lcTLL8M6fDiwC1vzzFRiBicTk9yMJbIKoW/3Loy+FI55KBrMbMmLUYkzLhseiob1DQpo/MNcMlOG7eEJ9YR/5Uqqb7uNcEUlsSNSoHUL7WVWVLuVxBtuwnXOOV2hmZes+pz2628jtT7MugsP4aSbn8ZWtRRePxlGXQQnPU5lRyWXz7qclkALT0x5gkPSDtmn9myPwObNlE47g47xA7js0PX8pfA8TjWnRUl5Xqe9tz0JvNt5CSsGdEcGeksTqsFLiz2RmDNewZQXdd2XUtLob6S4rZji1mJK2ksobiumpK0Eb9iLIhQWnrtwr+Pg7wv2O4ELITKA74GBUkq/EOI9YKaU8rVd1TmQBP7afWcR+/0acuvBEt59WamAInSkohBQbUQUAxFFJaBDRKhoiopD+kgOtaOF9s/wSROg7sOt1hE9SBugIyaeppRsNIsN3WJFt1jBZgObDWGzIa02RCRC+tzPcZZGnStqC3J56XAfaxOae7jRj/EGuHBpHEk/+AnFxLHhohtJXjaPjO+/BMB/muCi/iqTXdfQ1x6NZaEEA6QvmkP2t9OxN1QTjHURGZ5BQeoq4kQ9bZZMNuX8jtHTbkI17d5aYXuENq5i3XlnE0Dw+jWF/P28N3pI4n/74W+8tfEtnj/yeSZkRHNSRpqboxEUZ8/Bu2ABMhhEiY3FMXkSMVOPxHHYoSh2OzISIVxVRbCsjFBJMaG1iwkWbyRQ04T0dt9jiaQ1FioTBNXxgpp4QU0C1MYLWmJgQIXkvrd6Jn6YP0Dw2pEK7Q5Bii2Fq4Zdxdi0saTb01G3t4neDSJNTVRccimh0lJip52O+/Mv0NrbsY0dS8Lll2OfMB7RuAFKvyO86ms8C1fiLgNvgxmkwGDViMn0E1NgwnbefYgR53ZH1XvpyKhaCcAUQyjk5sMYBy8mJNGiCJacvwSDsmdzRhkK0fjMMzS/8CIGpwVrTDveOhVdU3GddQZJN/ypKxCcN+zlsWWP8e6mdyk0ZfLAV3GoC1cSf9GFJN96K2LO/TD/cTjnLeh/Ag2+Bq6cdSXlHeU8MumRvYufooWjsfO7pOcKZHMZZU8vJtQW4sbLIMGo8XptPTt8xcPPR8Zm0WyNp7q6hfZ1pSirS4jdWIMS0bnsegMeW/R7ObPvmZS0lVDUVoQ71O31bFSMFMQVRAO0xfdjVMoo+sfvaOm2P3CgCHwRMAzoAD4GnpByF/7SHPhgVv3v/w+GlJe5++N6hpZJ5gwVpLZKcuvBtveC6s+KNqudhgQvHqckByfvW46gyJ5For+Ny9dOJy7g5t1+U3mr31Fo+0AIsUEP04q+5aTS+Rh1jW8K0vhoShON8RoJLRaum+ljQKXO2kLJujHpfNlxAU9+8QSrE/swrLmYQJqXP5xnwVN0JzLSM2hTAVVc1/gBQ4pLCNSbkCqsyi7gmfxTqYxJJdNl5YxRmdvM3ouu9W0n+xQRnekXneuZy5/H+sJHBI3w1KUFnDzyEeyGGIQATYZ4ZvO1+CJubhr0AjHG2O6JQiFQggFs336F9YO3UOtrd3tvPDaFGpekOh5qEgS18VATL6h3Qdiwa53oqTU+fvd6dJ6jOh7W9VE5eklUNTLn6BSeH9kEQmFUymiOzzuOo3OP3isP0HBNDRUXX0K4oYGsp5/CPmECusdD67+fp+WNd4i0ejAn6CT2ayMmM4BI7BOddDTZ0dbPwb1iC+66OLw1RmQ4gupy4Zg6BedRR2EbO5pNj+XwvdXK+R1uZjhsvBAXS12nhFwQV8BHp3y0xzYGi4qovvU2ghs2YLABUiPiV7GPHk7KvQ9gLuw2pVtUu4h7599LrbeW8weez7UjrsUiTDT84x+0vP5vHIcfTvrfH0J955SoCuPqhRCTSnuwnatnX83aprXcN/4+Tss/CTqqexB0DxVHR3XUomsrhEJzaRoNiySLT0/g0X4dvFv4e1KSBlKuCirm/5OK9nLcsROxrNlC6oZG+pZHurihIglMYYh3w0U3qWhq97sQa46lvytK1P3j+9PXnEVGm4JeVUOovIJQRTlENNIe/Ot+SQyzPQ6UCuV64EHAD3wlpTxvd+UPNIGXNXmpbfZgOu9oVmWG+ewUC40ySJsi6FcFD/xn95Npv1bUW120m+20m+y0mx10mOx0mOy0m7cuHbR3bnObrMhtwq+6Ah2cUfQtJ5QuQJU6IQPYwjp+o+DViRmsHtZMiyXMSasiXDAT/jzxIs5tmcugdaXcfXYCS4N3dB5JMlFZwyXq5xyhriIojXysHcoXLaMZXlzEEVXLMeoai1P681GfSaxMKvwRk2OSm8TdHPJ5AI8F7j4ji6qWK0GPSvOKuYa4jKdIrskhqWQcGZ4mMj11ZHrryXA34wjvvpfWgemHCOYMVahN2LFtesSBJeggIdyGIeTH5ZEMbw1zRl0AzW8h7FWgZtfXtCEhnWcn51GTW4JibkJKFc3bF909HOkdhIKps+Pq7sTS3Q3cOecZrOEAL085j+TEdsbINYzS15BGI7oG1eVJtG6wYXSHaXYmsHLoGMZkr2acvpQ2EcsHtjP50noCQoN+5WsZVLKCvqWrsIT8SKNCSV6YTwYZWZkvCJoEQwNBzu8IcltyPGliKgXq73p2qqK7U1WkZNCimYz88i3UbSajAy4Xy069kvpBozGGQzia67A2ltFR/QXGhiIyWk1ktBtpzcpm9umH0uLQGRhzFHnz5lD47kv4MnKpvfQiTtp4ORHFzOKBf8YRqMXgr+AfYiPLjCFuamnn9+3dVmMSQcCagt+eid+eQdCeScCeQdCRRbMllubGWgbd8SAlhbH8+eQ2EAKbaie2wcPgcsngcsmgcomz0zesxgXrcgVrcwTrsgUddsE9b2qYw5K7fm/gwszzGeRPILvdgKPBjV5diV5ZiVZZgd7S0uPZC6sV67hxpD76KAaLpUtw2V84EBK4C/gAOBtoA/4LvC+l/M925a4ArgDIzs4eVV5e/qPOt7fwzJ1L5RVX8sr5yazqa+Cd8lLWOhOZ0W8SSzZ/i+ILoEhQdch3W7lQTEBZuQLzlgZMwR1veIdd0JKosSpNpdYlODf9JOI/mI3W5gHAYNWJ+H+6mqUp28nM/n4u/Kpb/5Nw+eXIUAitrZVISytaaytaSwuRtjakz7fzAykKamwsanx8dJLW5UJ1xaO1tuKeNaurmNem8PA0QdrwwxiZUEjKva9ibde49wqFaS0exr5lI5SVSv7z72Hb9AExK1/C1FpExJZMy8ALaO5/HiFzApqMeiDqzc0YZn6M+bOPUNpaCefk03HCNDomTCVsMESdUzSdsB5dbnVKiWy33eEpYtOm27n2rWg7px+aQFZgEM7mauJb6onr8PS8b86o9LxViq7plKqbnERVRlKS3QhjN0mmrNJJdPeoTlFCPE2WOOL8IeL9buKDHRh3EqJWGqE5RpLYInbYHlfow2IJ07DWiRYWzCkcyTNDx6PHr8fgXIVi7EDqJiLugYQ7hqN5CgGVwe0l3LfgVUyEMU/S6JdQA0CbtLNQH8h8fTAL9YGUyHQUKTmtdi7nF32JpTWMYtFZU9iHh7LPp8W4o5R/mFzNn1v+TWtDCKXChNMPIQOsyoXjEtp4JXcg/8lvIFxxAXHeVOpIADSge5SX7Gvh9uX/YUBTz0ngDpOZZRkZpHg7SPG4SfD19D5utUNtPDQ7BWM3SYJGeOYEheWFCo6IymOLDMQu9KAadLImtWCN737n66SLLTKRJ1IU1jn89GnOx9o0nmqZTK2woZnaOr1Mm6J28qbm6Lri4563NHLr4aGzVDJaJIPLoqQd3/nKNMXA2q2EnSNodgrsfklqK6S2StJbJGd+H3327SY7sSEvO4PPYKYkNoOiuEyK4zIpisukxpGIvou49QDf33bET7IXPxAEfiZwrJTy0s7/FwKHSCmv3lWdnyMeePXNt+CZN4/QR89y4deXMDV+EI8snY4YewWeI+9ldsVspm+Zzg+1P3TVKXAO4/LMaxi54p/EF82hNJzPFn0gamUdrsp6HJ6oZKcLqE6A0hSFSeuiQ7cZpzl5q5+XiBA4dIVDzYeTGPN7zF4fprYWTO2tmDtaMLe34mirIX/p913n1YVA2cX9b3al4rHH0mGPxW2Lo8MeS7vVSbstuvQYbaBHsPo82P1urP7o0hHwYA94cATcOAJeYoIestrrdnqOnaE0G6piBIeuk1G9YVaEjOwOyk3JfKhOZIZxAs2m2F2+rEYtzOFVKzi1ZB75HbW0mex8ljeBGXkTaLP0nLyLCXnJ8DSS6W4kw1tLlq+GTE8j6e52jNsNlioTIasJal3wzVCFFX0Eda4oKTl94PKAyyM7l+ByG4j3mIh3C+K8EWJ9QVR914mLATqsgnmDBCFXf6zOyYRik/E7XdTaG1gqn6Owwse9/+meo7fEh0g/ws0zE76gsHUu/StnYp27mY5SK4oDWo8Zx6rRv2Oz6qY08D0VgUUEpQe7NHBGqc7xHwUwqpK0KW4aModSETuacudoGux9kaKbSOMCVUyoeomBTV8SFhZWho8ivMpLQtF6QlY7ZROPp3TSCYQcsdhDTUwsf5x1oQX8M95FUBEouqRfFRy/0UifzQES3dE5mXU5gvq+8TRn1TMvfSBtgRKS2yC9ReWcuRpZjXsesS4pFBSnCeriodYVfSZJHTCqSPLNUIEjANd/opHbAF+PhLiROpNlIm3uOJTPaxD+MOmHtOLK9PCvvOcpMWfh1evw6LVs0J7vOo+KDY2dCy2xHsmNH2sM7Gn9Srsteo3lydEOHRkl6tRWyGixktXqw7hj5Ise8BqtlMVnUurKpMSVSYkrixp7AhoiajXWaT221ft2Zzi0IIGXLhyD1fTjU1YdCAIfB7wCjCGqQnkNWCqlfHJXdQ40geteL5sPm0jsySeTdv99vLTmJR5f/jgPOIdx2qrpcPabMOBEAOq99Ty76lk+KPqgq37Em8fg9gT+FZxFovTzeGQaz0dOoE+ghte8D/FlIA5jm0ZerSDe200GHVZYnScoTRVo8WHCcUaGKe0YgvHYgrGg2TlBWYxJaHxks5PwfixJHfDK1GRqlGO4du4HxAajL+fsYQJvKI+4dgfxQTfxgQ7iAx1YtB1nZoOKgVZLDK2WKLF32GLx2GPxOlz4YuII22MYv+F7Rq2dS1tcMnOPu4hAUhoxQS8ptVsYMf31qLfn1uuwKyhOExGvH7tX7ECiWyGFQLfHoMfGIp1xSGcsMjYOYuMgzoWIjUXExaGWbUH5fDqiuvvLCuZnEzKAqaYBs6ebDCMK1Md166Tr4wR9aiVTVkffzy0pkL8PzqBqXByGpCQMyck9l1vXk5MQJhMN82az6oMXSV1bizUE0mrBOflwYqZOwT5xIu9UT+eL9x/h3IWC/LKeKpqk4/uSeN0tUbvqrfA24/3gSeqe+5BQSxhnto+UER0YrDphYIHVwrL2GA6fYaTdBs9eFM+4UadwXJ8TyXXm4ov48IQ8eMNevK2leFa9ibdsLl6DEU/2OHwZI/Gg4Q17sRVVM/iLYgpXNxM2COYPN/LeGI3m2F0P3Q0RyfgNklMW6WQ37f39XFIomD9QdJH08FLJjR/rfD1c8MJxKuP8AR5uaCLRmsjmdU60RT6CBpg1QjBzjML1GzPpN6ccc2EhcQ8/QG2qkarydcTd+ywxxXXMmSh4b7xC627it6tCRSKx+jQGVsgutUjWdtfhM0el7YgBUlrBvgeS3h5Vg5IYd+ntWAYNwpiVtU/JsQ8UDpQO/H6iKpQIsAK4TEq5y9t1oAm8ffp0am65lZz/vIFt9Gh0qXPFV1ewunEV7/pM5LVUwVXze3ib1bR5uO37m1nZPL9rm1mxMils4qyGzfQx92HxkL+S3LiAkZv+jxP7jKaZEL+z3Ef2wu8ZMPOdPbbLb4L6VB3FrGMXOgmbd+/w8+5kwTFuFYNuxieiv5BmQA8KCIIa1DCFIliCIWzBADGhHQM+7QwN1jgat/3Z4jBqES5fNwOAiCL5bIzCp4comA1mbp7nIH9xAx8cJlifKUhyp5Pg6U+8z4oj6OmS9u2+DpKadu7avrcIdFr+qTqoGjtaDewFhMWK45ijcR5zNPaxY1Edjl2WlVIyY8sM/rHkH3jCHv7Q/xLO9gzCP+db3N98g9a0I7uljA0Tdo6g5dsNqHEu7IcdRuKD90XJNuTFE+rA27Aeb/USvFUrsc0qImV5VH9fNjDCD5MixFarHPm5ieZ4I/ecpdEas296UlWo2I12HEYHNqMNh9FBRo2b7K82M3Ft9Fte2F9QVxBhTOooVhStJLlOMKFEAVMI6VVQ5N6f85NjdcgLkhMJkxsOk2dwYnVm8JgBTn20AXMEIveczZCjzkU6M3m3dDo/PPcXrvhCZ0W+YFipRDcoFE3OpzgxwqQZFVj9Om9MUfhylMAYgT99YWLUWj+rBuvMODmRoCudkBYiqAWp89ZhDEQYUCk5bL1k4ro985UUAiUxHhEMo3s8Ueed7eC2QE2GmZwxU+hzyNFYBg2i4fVXaXrnbX7493VcOfKqvX8oPwMOekcegIrLryBYUkzB11939Zr13nqmTZ9GuiWB/6xfiil1KFw0fYfEpSsaVnD73Nup8Ub1kBbVQkALkKzpHO/1c2K/M+m36Ws2iDDnOhVOKzyNe8ffy5LH7sbx/Ps0T+hHarmnK7j/vkIKidiHD+uXQFMsJO48EsHPhi1JZmqMBXgNdvSYMuIijeRVOUj1uncoW2NPYHViH1YnFrAmMZ8maxwAwtiMNeMdVGslUjcRrD8BPRyHUIIoip/xNWu5fdaGHY63JdmE06eR6NGocQl8Frjz9zsZFkvJqGLJ2XN1cht2fh0Lx+kUZ9nZmJhGlTWRgKkIqfS8hgF+B57ACXj0fghpRkgLYIy65+saWe4yJrpfpylUj6FDZWiZQmbT7tUe7x/aKUXHC2pd4LFGHb0u/0LnsPU7ckGdS1CV3Yf1WYeyMnkoHZYteBzvoCtubn4/lrHFjVS7XPz5vBF4Y6JRModu0fnzuzr3nK/SZofTF+hdncuK/HgSO4LkNnpZnhfHi8dk0u5oY9qCWqYt0KhxwX8nKowqljttz7ZojDGT5O6WF5f3ETj8TpxeK8kdDSido0vVrGEZNJgaSxXvJrpZk6Fy/LjzuGr0DVgN1q766886jU3NG3G88gRH5Ry123P/3DjoCTzS3EzRpMkkXHIJyTf9qce+ORVzuP6b6/l90iHctPi9XaZP8oQ8/G3x3/i05FMKXYUcl3scq+uW8n3tAiJAYSjEiR4vG7JG8IW3jNePfZ0RScP5atpEUotaWPiX01lqraNPU4i8NStJrfbganRgKo/scK7tMXeQYFWe4NoZOg2xcOdFKim6xvV6PHmx+aS4BiIcaUhLAhIFvG1IfzvS1w7+DqS/g0hTMzXvrIqaWwDJh5owqD5k0IfUOhM2SIHUQWqCsF+lrTiaA1CoEqn9ujuQ/Q2fOSr5awpIAck76ZxK0iDeLXB5dv6d+J063vQIBiFpwoWzFpLbunW1FTFJrI/P5djyJbtti1AkloQQekKEz9NTmZtgQrO0EeeGxEYXic2xJLcqpPjaSfG2YNZ7vlOagIZ4A63xIRpsCgPLdryesAr3/05lc2b0OadEIsTXqNz/ZjfpL0obwLNDTiOsGDi0aS6HNX7PoMoIBj2qlljSV/BDP8HGLMEhGyU3fBJ92d6arPDxhKjQlFcn+furGmvS46lOMpPZ5mZAhWeXfhARBQy7n56gLN7KdwPtNCa3YPI5sVaPZXh9OWMaNvUo12q2UxyXRVFsJsecPIFxVffRaPHxUEIs35kEAy3J3Df1SQYkDuxRT0YirB81ki+GRDjtqRnkx+1lhMKwP5rN3l0bjbqZM37v6u0jDnoCb3njP9Q/+CD50z9F5mVx74LohKXL4iLJmsSapjUAjFBjOKm+gqTJd5KYO4lEayIJ1oQeKbi+LPuSBxY+QFgPc+uYW5maPZUvF/6DGcWfsGq7iYjvzv4Od20lNdPOod0Oz13iwK17aFIN6Fv5UEpi/HD+PDhi+d6bMhanwXdDFJYWCvx2nYJwhMJQmAKM9LFn4HZkUGWNJdU1lJRKC9YnXgCPB++FV9B+3OkEdQhpGsGQhh7yklk9k5FbnscebMDvVamutRFZ9uOynbTZoDEW2hyCNkeUCMMGCKuCiApGoWDSjDgCBmw+FWtAwRwAVbbj8Kk4fZKY4B48rnZ3fjsoOhg1MEb2TAAHEkqcA73Ns+eCPxMiSrRTMkZ2rorSRHS+IX07B1wpJE390/CpEr8M4pV+AjKEJQTDt8h9ckTbH2gfPxxnRiFKUTFy43oI7lqZvTpXED9gLDlZoyASwYSOpWENG8vnssZoRtUFgx355Mf1j8bmiUSQoTAyEkFGIgTWr0d3dybSLuiD3uEm0tiI6nSQdP7xuMZnR0NSu2s6l7VR4g60dTdCNcGtW8D80zMIbY+DnsBLzz4bGQwR+/ZLXPfNdaxpXMOpBaeiSY0mfxNV7ioq3LuOieEyu0i0JZJkjSZX0KXOjC1R3bDT5OTxI54iW3Xh/ugivvJt4RlXXFfdLHsfElYXced7OoH+AWLOOJWVBZfTGPHRHKin37tvM3jesq7y9fEqW1J1xm8zRGyOiYZyjt8DD/hM8N0QwbdDFEpTox/p+d/oHLdMUp4Erx2j4tKdxLS6MLc4yKr3MKyhEqdv38nSa1awB3dkxg2ZUV21IwAOP9gDu/cy9ZvAY4kO1z0W0bns/G8V+MyS2JBg/HpJTm30QI2pBtZMiueFvGbe+Xu006tNVGie1J/BH67n2yGCZ09QkNvZ2v4zYOWQQ1+j/fXX8Hz+OUhJ/ahcXs+rwBtn5FJRyIjSDjwrqwjW//gOZHewn3gihqRktLZWPB9FnWTUWAeJZ40nUlVB+/clRNx7HpXtCm4LVCVGJ3wTEhMYYsvBIuzUlcxlHQJzJOqQYg7LHSwzerHvsCYGsSWHEIqMRnxUgK3rBgMk9kGkDUJkDMc4YBS2ESP2exsOagIPlZVRcuxxqH+8hOvSZtPsb+bhiQ/v4I67qWUTZ804i0J7JvcWLacpcwRNYy6myd9Eo7+RJn9Tj/WIvuNHJnUTaZEQ6XqA5Zae7uIXfq1x4hLJA4dPZWHcMYDC2Lr13L+oOxhOvTWORpuLBH87ab4WtocuIGAU2EI/s7jzI7C0j0qLU+K2SjxW0YOUW4Mj6DDY8RvtqELBroM/aT4Rk5uUjgzqndG5gomNaUxssxAnfMTiJTbiwVoWIFIk0Dwqul1D8XaPev4xTSGnAc6ep/PlCMHLxyg9HIa+qqgmMQJGobE2bGF+uYvRawS2IBjiwyTkefF0WPAW7Zur/8+JJidoBjBrEmMI7P7/LdXWzhCZOAqEgoxEUGoaUMt+2qT5vkAo0Ulv9mKOSo2Lo3Dhgv2epeegJvDGp56m8emnueUGJ544M09NeWqXUc3e3PAmDy9+mNuTD+O8H96CY/8Oh/xhh3JSStqD7TT6G/m24nueWPlo175c8yji2xdRZFRxq92DVENE8td/ayR2wF0Xqpy4WOeYFZKaFBtfHTeI474sIqm+nXlnnkD1uMMxCzuH33sNCR3dUm7IAKYfL5ztEbqISr/ObQxX5g3qJt/CatAVQckQSDEKavMuxmByMe2vjwBQPGIQnvYNDN+i4zXDjLEKc4YJHMLI5I42xvok5riTKUo8FqkrCC2C0DQULcy8pidxSCdjHGcQCDWzqf0rvKFKzOEkaBuK5jej6joGXcOihZhauZSEwI6Tk73YN5QnQatDMLz0p3/rW1KjE5t1ruiywwq3v79n/ZXPHA0/EOOH1Lbu7SEDNDqjo8/tVTpVCZDZ3P1/b3Tle4OitKh9+PpsQVmKoM0Of/23Rt+aqJ4/3gMfjxesG63xVkMdWF2QfwQyfwrkTkLakrtUL2gagY2bqL7hBgAyn34a+7ixP72R2+GgJXApJaunTqLI2MIbVxXw9NSnyYzZdVB6KSV/nPNHFtUs4i0lk35bFsBlX0Pa7iOyBbUgjy9/nDfWv0F+bD5/yzqB1d/ez4OJ8QBc3ubmsL6ncfuGWTz+Qreeuzwp6niS2irpWyPJ33ufmp8FFYlRPbZBkxg06FvTvc/v0DEoJgyqDdG684QJBxPeOELBbQNzCJI6JEntUFAjSfoJl14XB7dfrOKzdEpkUjJsi6SgFixhyZCyX987ETDCsoKoPX6UrKOE3WEDhMAUlowoidqBKz/zQFF3aigd0RHZkkJBnEfi8kKcp5vcJRJidGROCoyciCzMQ/TJgcxUhGrozFIUfR6ivRq5cR7yxneBaKfx9IkqpjQTj2QeT07/0yB9+I7JmjvhW7aMyquuRjGbyXrxBSz9fyPBrH4MDkRKtTc/vJ9Rd73LF+f04ZI73tyr4EEtgRamfTqNWKODt7cUYTXZ4YrvwLxru2GIxur+YeMsnv76QWRTMy4vDGsJY/EJ8CuMaw4Rad67pA69AE1VCFmN+K3QYA720I97LYKACZLbJMctk3w/UOxgVlYfB0lt0Ym6Nw9X+GS8AlKS2QSjiyS/+25HcW3+gKjkFeuFE5fo2PbRyWPrMeLdkgFVuy83c7Tg+KWS1bmCL0YJhm+RDN8iSW6HkAp/vFqlzRElEmtQMrBCctsupNmtE49hA7ikDkLSaFDJrdu3KJf7gi9HCqoSBU6fZHiJpHD3McL2HQLiCoMsy9R5M8XOeYF2jtG8qGYdT62F6u/j93gIn8PAlgSN8iSoSBYcmd+fE+0CpWEZ3LgWTPaeFYLuaEjhktlQ9BXejXVUfJsIREcCN1ypcrbXzY0X/4ApJnW353Z//TXVN92MMT2drBdf7JlkZD/joCPwsBbmvoX3Ef/cRxyzUlAwby4WV8Je119QvYArv76Ss9Mmctf8t9D6TCMy+iYijY1EGhsJNzRE1xsau7ZFGhshsqN+QzfpBISySzIo6WOjwubHZwZNjVoInLaw+77XxwlS2rr/x111BbMDq1hftoSRW2BQ+S9oYvEjsDJP8NkYQcAUJWG/ia5l2MA+Bbk6YpXOVTN1rr9C5bD1OtO+l8wZLjhy5c7fW50dLS98pqj5G0BaS9SV2rl3vk898PYkhaV9BS0OOH6JzqlLwLiPcxWtsZKZI1TO/Vbn487s9ttjQIXk8i80MpujKot5gxT6VUkO2dcY978AIg4Fg6f7ff3bmQqJqQGuaG+nfyhMyGfG3ZiEu8KAvyYEEupd4E2PYfSACSiah+DKhQSbQoTcu47q9+k4wfRxCu12wZBQhFvdQZ7tM5IFbZu4ubmVi0ZcA0d0ZuCpWwVFs+CH58AX1cnoYUH9KmeXGS3AtVeq3GazclRtEdxZA6Zdxy5pfedd6h54AMuQwWQ99xwGl2s/3L1d46AhcCklczaX88TaP1PWsZJXnjGiDxxD1qEtxLauJWSKI2yKI2TuXJq2WepWlIVroNWL6PDS0ViMsbUVl7dHMu8uaDFOtLgEwq54muxhPDFm3E4z87UFtDmiH3KbHUYXSy7/QscRiOr63p6k0GGDGz+J2nTfcqmK37wjaR2zTOfir3TKUyBvOzfxx05VOH+O/pOG778UypKh3iVojoGWmOh9anZGly0xEDbueC+OCkomJo1ANBfzEq2UdyZleOITH84SExffqJLYDk8/q7GkUDCmaN/f26YYqIsXaDEaHS7BwmQVSwiumx4lnM3pPVVIW+GzgykoMUT23PHogn1SK/ztTIV1OYKQUWAOSQpqJKOLJeM2SRIP8LPf1xj1PwWGhDjMBQUIm4NIU3NUIKrbte5ItQjiLroMy8DBhNYsoPGld3co8/Ehguyh7ZwT0w/1zNcJxaRwx7vH8FW4icvNWVy7cT47e2LeehO1y1IIuzXWHpZEc2sTo4skV9/iYLFpINSthutW7LRdUkqannqapqefxj55EpmPPYZi601qvNdYVVfC7z69HGFsYcDSCTww+xv+MvYiMrKaOUxZS6poIYVWUkULFtHTTCzQaqD0yyTofKyW5CDzk8zUOgVnhdtxmSMUWTJYbcphi8NKjVWl0aBR56zFb93RtVrokj/M1DliTfc93NVHsawg6vlWGx/VJ9bGC5qdMHqz5PpP9f06cRkwRjuSqCoCfJboJKXPHNUTHrVCEjLA25OVLtd1gy5R9OgEapyXvXJZ/rFwW6JE3hIjOpcQdJowOI2YLBoOpZmXkmNACB5+JUJuPXw/SDBpH9u0Pgs+H610xfoO7aTjGL1Z59YPuiVGHYmyzWdvig0TV+ilTVMJrdh7+97VwzWem2CiyQl/39hG3sf73zb4pyBg3HPik18LDCkpROp3HQinz7//D1P1DFj1Fhrwl8R4PohxcHaHmzubW6MjskGno2cfQcMnq2j9cCYyM40nj5csTGzlhXdiqVPczPrToTy5/gdIKIBz39rhPFLTqLv/Adree4/Y004j7YH7e2Z/OoA4KAh8deNqrp1zLWEtwl2Gi8m/558A+M67lEDhAHw5BQTtMYQ1nXBYh0AbJl8dJn8dFn8DFn899spSzPNKkNUBZIxO0bggfx7tACEY6w9QbTBQa1DRtxnm23Uds5S0qN3D3UmeCK4OlXP/raHIqHqg0Ql/vlDFEoa0lmjks6tmdpPD9mm5Qmp0+Bjr3bch/ZcjBdUJ0dCpAZPAHpDYglG7bHsA7AHZuYwG8tn+/8GIqgT44FCFy7/sqdeujoeZYxTmDo7Gwt4eJy/SOf+b7mfUZoMHz1GpSoTJayR/+Lyn+mreoKj+fGjZz/fd1LiilhtbJfsOK7x6lILHAlNXRVUrIRU+mqBgC0pOWvzT2rYxA3Iaoy72xWnwyXiV5flwzWc6EzZ0HztglbhdkqSafYtco1pVwgaB7o3sVwcsg0XDkhBCNemsyMjiS2cblpOO48FJfyO8fCU1d95FuKqK2uNHccfANcTGJvOPQx7EfOwlfDpGYr76Ym74/CE49HqYek+PY+uBANU334zn69kkXHklCddfiyfsoSXQQluwjdZAK62BVhKtiUzOmrz/LqoTBwWBn/DhCVS4K1CEwvC2OG5/ZsdAEzI5AfPAAcQMHoZ14CAsAwdgSElBCEGtp5Z7F9zLlrYSUtfWccE3OtmN0aHzG1NUNmUJjvd4yQpHyE4ZTrbfQ5anifj2OuqJcFZGKgmaxps19dg671vTOgeNa6IZa7TTWji3fzIAY/0Brmhr54qUZP72nwg59VB+mod2oyDiUREdKsZ2BbPHSmyrTuoeYljsCq0OIyXpMWxJjackJZnKhASO0+eRTCUfWPtQZvaB2hn1T0oefUFgC8D7k3RGlEhGlkjU35aKfa9RlBYloczm6Ghk1gjBF6MUWmMEqiaZulJy2Vc91SfL+wgemaZwzDLJ72f/PDdmczqUpQiOXrH7b7HWBWk7pi/l+4GCzRmCqSt1chp33L87NMRG1YA7VR2Z4LMxUVM7o0aX2/z25n17g1YHNCVJGpIl5ckwP9NIYywMD4ZwhnTsPnD4BA6vxO4TGHwC1a/Qt0Tg+glOrq9PVZi8JhqTJmQ38+CpETZkRzvyq4ddTWqll/63vMw/T1OoGpHCxPottOeMp8PqpD3QRkewjaEihaNf3EBKuYfpx8YwcxS0awE0dnxeThTmX7Tqxzd4FzgoCHxTyyZWN62mzluHZc4SDntxCR67gsOr4zPDN0MEsT7IrZekN3dPZvntKt4MB62ZdmY469iSIsjJLuD0tAnkLmnC8N489OY2lvRVGDE1myGtCzqzVp8MqpGwFub3n19AcfsW3hl6A3nhCLhrkW3VVL+2GPfGqLIy/ZBWAn1CHJkdnY0uDIVIj0QdSp55KYzVESH3yCZ2Fko7ElAo+nj3s957g4gS1UEXZUQ/6uJ0ETUPrJacugzS6nuqC4Iunfk5BjZnCC6zeFie5uTRztH+X9a30++T6CSPYtTRwzuXtL46QqNaNWAPCDLdGiNKJFbPLx+C89eClXkCS1hSULN7O+YV+YKV+YL+VZLxGw/Md9ni6Pb23Rrffn224IjVcr+p8WYdbqTFoXL2jJ75zVscEOeVXdEQPRYoT1HZkqJTmhINx1wTT1fuVlWTPP2MRrwHLr5BxWeGQzZJrvpM/9Hqn7Aa7bDa7NBuF4wujt7nT8YJijIE7XbRtT9oEiR0SO58VyOtVfLZMToNBRpxmoZLB5dixmWw4TLFYDM7uTxcylTdzMOXLv/xN28XOCgIfFtUXnklgQ3rKXj6Rto+nk79e4uRik7roR4q+ug0SpVQqxFjs4qzSSWlATKbuj8gnwnKUqA5SRKM1cmpgpxSFVUDX78gsYPdpJnCxOiSvycm8maMjf9rD3GMZgSDGVQzjUvDNC3wkHR4It4tfvxVHnKmNvFdtpFbkhN7tHfamhBnz1BIGOAmeVi3c4qU4Kk1U7PIhb6TBMrmXMENxypMaDRw/FwNY+NP/8rCKiwtFJSmRFUwih7V2yt650+CokdjX5y+4Ndv+fBrQsAYJUZdiS41Jbpu1iQO38/rUTl/gCBopCum+p4mWKsSIM4Pjl0ke/qp+OMfVFodkN0I+XWSvHpJbr0kuwFMexiAVidLMhp+3P2bN0iwvI8gzhvtQGK90aia/arlLmPeA4QcOkZdompGMu+7Hvsh46JOPVYXmBw9rKlmLf4Xf9rwMs/nncWESXf/qHbuDgcHgW/8DDbNJFK6hqLnG0jo5yF5uBuEQkjNp/JznVBztNe3DOqPmpCEYjIijAaEQSWoB/BXVhIpr8bYvmelc6MTnjhFRQK/+1YjRTdhUARGRWAr3yYzS4qRiFcj0mk+JVSdNrsghMCsS3QZJclt9dxCkUj95/2ge3HgsS47qotvdQg6bFHv1tQWuOETjbx6mD5W8NUIhUdekwRNZnwGSVprgLAK592iktwGeZUxjF9vYELpPmRc+A3g/nMVylIEmU2Q1SQZWqdhb1XIaZD7bNZZkgoPna3itgmS2qLRDx2B3ddZkS8QErJaTLg6Qj2yYd11uYWnZzfTVmrr4TJvsKtk/f12LEeev9tjX/vvCayLtDPrvMWoZvtuy/4YHBwE/tKRULWEliIb9cviyDu2AUu8gNxDIWkAmjmdzVc/26OKMJuRu4li9ltB2CIJKuy1FBex65iFTsSr/urjjP+voT3OSFzEhPTsPOfirxFfjBQcWhfGVqei6oKAMSrZfjVSoTyl2xnp5g90hpT/PJxSFweVyRCxSuy6ztA1+5ayzBQT6bI1D5gllqDAmhDCmJVFx5pGTFlZZL308h4ddFpqVzD1yws439mfm05//8dezm6xKwLftaX8rxEXfwEdVXRcfDXmLDeW46dBS2n0V7kYNexjwDlRO8+aRS4iQZWkQ2NwTT0MzZyObkhEU1y0f7eCtg+iUeICDhO19hDm1HQKYvugd3RQXb2JhMY9dOc/M4wBwb4YLBm8CquzFRryBalenQElv14Sr+r0v9rdxJgmYM4wQU6D3OmE228JsW1hJL9OG773DlPoWy13iJty7HIJGKhxwVcjFb4dKvBZorbrfWok2Y2Sc7/ViTsA6pctqbCon0JH6jj61AYZuqGClMYmUtu2xlURbJuMeW+xraNQyCHJG9aGHlGoX1GHxRUm62SBYS+8Kz9f8DARIThpzJ/2WHZ/47clgQOhykpKjjqapJv+ROLll3fvkBI89VEyby0lUrGButfm4F7fgj1dI21MI0ZrVMXhazRRuyyBUFtPUutwmUgZPAqtMJ+HW96mPFlwr4T+a3UaFh6Y+xQwRgP6GDSwhfZcXjHpOLP9CEXSurnb9d+dpBHT3Kl4/Y2jNj7qIv9zx/iuSoAvRikMK5U7dRT69xQFl+enm+n14tcNe1qAjMODqKc+BsPO3n3hQAfnvjGOiMXJfy/4YfdlfwIOChVKIKxR99Qz+J9/BucnnyFS0pBEvaO2XoaU0WA2sjNTtPbpR0SefBRMJsx/uABLPxcNLWtZ2b6W0eWtJKxuwlusEfbufDCiGcDuDGGMU3Bv2abHnnosgQsuQ5pMxC16i8R5LxFsUQi0Ggl1GGAbZ5B2G8RuI5nUxfWMyPZLI6JEXd0DRrCHJNbgb78T6EUvfgwUg078cRkk/Ol5lLTCPZbf8t1DnFL2NrcUnsuFE3bM8rW/cFAQ+MlPzuOPr99FmzmGWydevVd1TAnfkOmt4sYvqylobWJmwUDeOnVTD688GTEzpCiXyUVNDKqoJ3GbCKYNTkiwhFEbdq3ASB3dhi0phCkmglBAjwj8zUYqvkncZZ3tUZIK1QmCOFuYgK5galH3S/jPXcFvgtKUaOD/nEZ2OhvfYY3GEFncV7A2RzC202u0F788qhKiz2ZzhqA5RnDiYn0HT9WHzlLwmQV/nK71EBi+HSIoTxZc9DPZuQNsyog6t+3LZOWzxyt8M0zh0EA6Z7f1JbbOh2HRKoyte547+GyMwGeGM7/veU/a7LDq1PEcGkjF0KlG3RmEyYT9sMOIOeooYo44HDUubsdCus7jLw7nFbNk9tnfkGjd++99X3FQEPjyWQuwXnspZb+/jsbJx0WlbSk7pXB6SONbpfAFLf9meceHGCKSC+ZZOG6Rl+pElX+dTNfky1YcsUrnotm7j1BniQ8RaPl1RBxcNUzhtfGS0RsF532r89xxUd3kVq9Lhx/GbdY5ZZFkY0Y0kNagXScl6sVBhoqk6HuQ8CsIqb42OxrOYFSxJG4v5m6XFApePUph3CbJEat0svejQU714BSqbjuHsRc+wZLxCp8N0nhpfQuhYCyxj35PuLycjlmzcM/6mkhtLagq9nFjiTnqKBxTp2JMjjrr6RtncvT8myiM78+zp+26M9gfOCgIvP5vD9P61lsUfj8PNXbPYWMBdKnz4KIHeW/zewAMLZVcPUMjxg9vHa4wc4xACkFyq+T/XtaoSoDaBEFKqySj+cC5njc6wRMnyavo7kSijg7dtrrbxlVps9P14j96qsKRKyVDyyRvT1L4eILggbc0surgw0MVpmyKcO+5Ku0mlT9+qjFpnWRhf7Hb2NYfjRc0xAlGb5aMKunV8f7SWJknWJcjiPFH38NRxb3P5EDglaMUWqf05aXyYsgcA2d0Z8+SUhJYuw73rFm4v/qKUFkZANbhw4k56ihK3P/hUlcj/zjsbxzX58QD2s6DgsCLjzmGcEUlitOJYjIhzOboz2Tq+d9sQjF1rwuTmdXF39NRtYXkAMR16BgDv05Pwf2ZkWdVrmDYzxiz49eIsmTI7Yy40BALX45UCBsgsUNy8g+/7nvz30MFi/splCcDQnDdJ9oOMdFLdxLJshdRB7VIdirxRifS6ydcseuh51d/P4nrlj+PmPJnmHzLTstIKQmVlOCeNYuOWbMIrt8ARGOQDz3zSlzHHIe5sHC/p1LbioOCwDs+/5zAunXowRAyGEQGg+ihILLH/x336X4/0neA3Mv2gFoXrOgTDey/Fdu6M/eiF7345ZEwwM2fB17GV9qYvSr/t9BTrA+UM2K9nX51XhQkVfZEFqQPYUHaYP580zQm9kvZb+07KOzAnccdh/O44/a9ohamdNJAIl7BvBNCvJvgYGrQy/XN7dSKQ/BPL40WU2BVvkJBtb7HyZblfaIxRpw+2SXh7QxprZC2tGcn2UvePbGwf3SitClW4PRKbvmwd6K0F/uOl45WaLWZCFhCqAo4GjJJqY1neFsVye4mXLuJr250aGzWd52KcVvE4caStoaPk128lXIhztZEDqldx6G1azi9+DvOKvoG36o3qTvxOGKOOgrb6FHR7PUHAL8pCfynoPXBK6h7Yx7ZZ8YyI3ko31TOZ4Q/xKRZv6k+rBe96MUBRIu5O2677FSHWAxKt2pEgFnz48dPWKiYlVgQAgGYDCrGoB/d01NCU5MSyXvvPYxpaT+6XQeFBP5joAcCyHAY57X/R/07h1L9aStDg/MYCvwPXH4vevE/C13AlhSoTBI9kq50WFWc/p1HsYoPuvkiZyy6UBA7EW4VdI4yLOIHhwEtkIYe6OmpOW1iBga1px5csdtRHLvPt/tjcdAzWNUfr8U7fz6m/HxkOIKGQtIwP2Udduyl3UN1Y0KI1X0EAxZ323sXpcHiwyL87n2VkE1i9irMHiaYNULh2k81Mlrg81GC/xyhdKUJu+V97Uel+9qK4jQo6EweO2uEIKdekt3YnT0lovx4D8VGZzQzUGmqoDJREFEht0EyaY1kcMWve0KvF/sfP+Vd+jWhwxrNvLS4n6A6AXSlm0CzQ2FO/0YCgoX9BU+dpHDsUskF38Cl16sML5H88TONUIKVJfEjOWzzAtJ1Hy8dfglBowWDKjhvXA4mVSCB7JovWFI+h+cTXFyYeS8uU0aX0+DIHBfZuXtOxLw/8ZNUKEKIOOAlYDBRM+xLpJQLd1X+l1CheBcvpuqaP6K7d24MqxkkbrPoYZsaUaJE2r+6e9usEYKjVki+GRLNju6xwDMnKqzK72nNomqS/lWSkcU9Xa5397FsdaffXu/+1IkKc4coCD2aiHdomeTSr3b/xa3PgnXZgjPnd5979jCBLmBQhSS9ZbfVe9GLvcaCAYIYHz9b8Ko9ocMKa3MFGzOjv5oEuOoznUM3SOoHhwiM9bHFYiH9uzjiq4Ncc1U0fsohG3Su/1THl59Kzoln0fLk05gLCsh69hmM6ek9ziFfPorTRR22lKG8ecKbP9u1HRArFCHE68A8KeVLQggTYJNStu2q/M9B4FLKHUx5gkVFVFx5JeHmZkSwZ8ARqUjEPsYP2Tw4jr9PdeO27b7eRV9rnLDkp73c116pEjHAiT/oTF0lsYRhcV/BzNGCyWslR3TGevZYorbi6S37llS3F7042LElBW6/WAUhOMLr44JXrTS6XNx2XhvmkJMjQrW0VcVx7ad+qmITmdH/OC5d8l9CqolHJl/GSls6uoQhYguPxNzPmRlpBGpPJdx2yA7nSnVaWHTn1P1+DftdBy6EiAUmAb8HkFKGgL0Ix3Tg0Bpo5aSPT8If9mMz2rAZbF3LimkN3P6eRn6nzazHApdfpyIknLJIcva8qGS7xzglRknMoX7Ot7l4lt0VhLo4AdulXVpa0J0FZG/w5PM9dXVfjhBUJwpyGqAmvvv4jgB7jIfci178LyK/Hi79UidsAE0143BrfDKqBVA4y1/FjS1tHK3dzlv91nDR+q+5dsEbVMQkY46EuPvrJ/m/kecyP2MoFxu+4AN7LEiFsHvITs81Ksf1s17bj5bAhRDDgReA9cAwYBlwvZTSu125K4ArALKzs0eVl5f/lPbuFhE9wvub36fCXUF7sJ2OYAftoXaa/c1UuCswhyRv/LObEM++XSW1BR5/Ibrt6RMUitOjGdz//qq2R3O/JYWCtw5XqE7cuSR+2Fqd66b3VHlcc5VKY5xg0hqdP844CBSQvejFT0CtC9bmRFO6/Rh9fEUiu3Wzr3WBMRJ1jjNonUsd3p6koCvR9It5DZLUnYxcl516Gfmr5uEq3UTrGecwyvgkR+Xm0D91Ao9MepQYswFF+XkCv+13FYoQYjSwCDhUSvmDEOJxoENKuct8Qj+bDtzXAus/AZO9KwXSf1a188Pny7j661e7iq3KymVATSUmbc8JhVvt8MVohXO/2/Vb9tF4wbIChdJUKKiB+9+MHrc6PppF/M/v6lQlwN0XqHitgiGlOjd/qGOM/LTJpG+GdM+ylyXDkydFA2FNXKvv1Ea9w7pvQYUAVo428nmehlGDm/eznXZIhenjBH2ro/rUbb0nPxovOO0AhfL9JfCfI6JJdrMOrmQ7BwSaIgnE6ShxEZz2MKZmA55ac48EJWXJ8MxVmXy4aTG+Mis1i3pKwAGrSoVLozZeEOeJTtpvGxm0PVZQnSzYlKpSnKThy03mBDGM0f/8EoD8z2bQ9PQzdMycSaAwyMWn2fjnkY8zNXv/q0l2hwNB4KnAIillbuf/icDtUsoTdlXnZyPwjTPhnXN7bNLDgi2fJyEMEmu/MO1LbD32v3iMQlGaYHSl5KxdRGmLKAKfU8XZtm++7ov7RuOQKHpUzbEhS/DQ2QqaKsivlTz82o/LSA/RjmX6OIUL5/x0UvUN9qP287MSK2UN9i610oFGhxU+Hq8wqEKS1Si54QqVtx7pvic18fymJ1+3zeLuM+1d3Pdd4dUjFb4dIrj4a50xmyX3XKBy+vzoRN2vDdvG7/mx+HxUNHKikFEJ2aDB2M36DkHZdpd0e1sUpcG8wQrlyYLyZPBZop2BSTER0rsfzCuPRXAEIBjvIO7pf2D/x5U0rVDZmGvguDe/wZJw4CIP7gwHahJzHnCZlHKTEOI+wC6l3HkwAX5GApcSKhcjFz2Df+MMvEKnZVkc2kYLLVeNoiNBJ/uRVajBboJ6e5KCkhvg7H9HIw02xMItl6r84xWNpDZ47gRBRhNkNUFmkyS5/cBewqtHKlQmwT1vH1gS9ZphcT9BkxNGF8neuBq/EngsO5/T2JQB/ap33N6L3SNoVgkYzLx1VIhvBnRv/6yyhmUWM0tsDpbanNSKaPS6Y5bqXDor+u212eGZsyCmSeHqzyXWjGyynn8OU27uz9b+A0Xgw4maEZqALcDFUsrWXZX/uQh8fvV8bp17K+6QG4mkf6Xkgf9ozBwteO2oqOnQlJU6sV7wWuCy7Uzz/BZ47o8Gmut1/vpGNEzrN8NEl2cWwO3vaYw8wFH7tk8E0YteHIx4/GQFgxY1kx1StnfhZneHhf0Fn45TqE+VSN2At1M/GWodR0zjOI5Ie4QvYswAnN3hJr1+DC7h5WhlKT5DmNnmRL4Lp3LdG018M0QwrFRi0OCv56iYw3DrBxpWg5WUf/2T5EOP+KmXv1c4KIJZ7S3K2sv43czf4Q65udhSyJR/rkXRofXUDmwGHbuuY9cl9sFn0hzsg37vKz3q970qHtWgUTPLQ0uJxu+vUwmausl7WEeIW55X+GaooHxUiCte3JdslfuGtyYrDAkHGbLgwJ3jYMLB4pzycyFkgPIkKEsRxLs5YKGEd6U62jQhiDktRGa5grnYhNa25/fcnhLE1c9DR7mVjnIbjvQA/hYjeljBmufHW25BhBUa0zXmjoYv+xlpM0TVK8a6I5kYrmdO1pqu4zlFMkMtN+KSiQzoWMCI9q/p7/mByi/j0FRYMjVEzgwHRg1uukzFFIHb/quR2gpzzx9En99dxuTMyVgN1v12v7bH/xSBA6xsWMnlX13O5d+bmfhtM9mHN2E/7QrwNsLqdwHw1Jmp/DZhh7opd/+Z2JNPpmjiJGJOOpF/HNHBnMo5qJpkRL3Gra//LJfQi18YywoE6c2StF2OKXcNXfw67fHr46LWU6UpgrJUQa0LBpVLbv5Qx7yfwhjvL3jN0Xj8lYnw2RiFY4vCZFYqGLZL+Rc5pg0zEm1WHKaUEKkTWglssdKyyU7EZ8DkDGMe4KOxMEw/LcwMh52/JMbzaH0jf0pJ6jrO7wf9nquGXYXVYGVt9QJK/+9+Cr+oZPawqJVMQyxUXz2KSUMvpKOxFXH9/Si6pMUBXx1iZqA5h0JDOkkRG3i8xJ1zNjFH7B8J/TdN4B2BME/PKUbTJYoiEAIUIVA6lwIQQnRv6zTt8W+ewXH/ep7Fw+M4aWgzZqEyfdxb5FV/Tv57z9KyuWd8Akd6VOnoqbF0bfONH4eGCduyhaihX9kb3otfDPf9TuHKz/UfRe4HKwJGeHeS8pNStV10o4rf0k3QF8zWOGnx/7N33mFWlNcf/7wzt7e92/suLL2DFKmCDVBQY4ndGI3dGKOJ0ZjEaNQkRo2xxx577wURLKCA9N7LsrBsr7eXmXl/f8yyy7JLh0T9+X2e+9y7e+dOn/Oe95zv+R7JHy9S2VAgSAlLnnhIZ2e6cmfTk109/K/7Ch45VcGtGYxdK5m0APJrIeiWLBts8OogG0LCKfURVggHamKXLlYx2fr5UBLDtSkQu+ocTvj5bSji0HsPfK8N+J/eW8WL3x4Yf9xiaDz01YP4kk3ceGUSoRfyRc18ljb2JPitgy6BKlZ064Ynv4qS2WbQze5PEt+PKdzuqEkRvHicoNovUCTc+K7eIcn58FSFnEbJsSslGQGTeTFrgODo9Uc+Ifp9wkfDBVMPsHr13VGmFEKPHbKV7fH/HQEnJC3/3XZqdV7a9ZM9UDwzUWH6UQJ2yTXZE5IHntKJ2OH+01Vufksnp0ky61idad2seAOCXy+L4tpiRU10bigDqQJf4/7dU5oCEXtHmu03fQU5jaYUxd5aLj45WWFhD0GzxzyGHHcOp5ScwtWDrsaqHnwY9HttwGNJnZfnb0M3DIwW4Zid/TB3/m20/m1+7vHp6/T59DXmXnoLf9c3ku17j2s/sTF48wESoIH6/nFeK3YybrXZxgzMop/1BYLLphsM3CpJquaom9PY+dT53VGCVyeoCENy1Lo0zl4Sp+v2vQgU/4iDxpYcCDlE67X6PuDbXgJDgaEb5XculHGk0Og2C2s6a1tYkQqlOabm/tEbDt91VBw6Rkxt/bsmBd4+RrCiSCHkgJTyiZy/cD6jt+yZt7o93cLGfJ2NeYKN+YLtGTBsk+S6DwyCTvj7T1W2ZwmE4URRE9hUG2+e8ibFvuKD3u/vtQE/UMQ3b2bLlAPrUdeY7mH2+EupyS5m1Dfv0nPtXJ47UXLJZ2YGOrRL8Ut5Otx4halCcMmGCCe93XmT4y3FBtMGq1z7vnmOF09I8HpXJ8esMg7Yy/wuYddCG4A/X6Dy6/d03DG48XKVmlTT+1B1yZg1ksumG61qilV+s3FG/v+Y151UTYGyEevlAXmNNSlmL9VjVslDYiH97EaV/PqWSsBqSZdqSXEN3wnjrWUm2ZSu0Huduu+F/8uo8sPX/QVZcYP8RgNXQKHRLXjmeJWww6Rf+sPw9+d0gi54/niF3uWSQaWSkipzHQEnVKVC1zqJNbF/lZRbs+Dt8VZWFBitIR53woUvkoEjmstR0TinNqzC+00YIynwjo4xM2s43Y/7OSPHT0ZRD0349QdtwPWmJmLr1hFbtYLYl68TWFyxx2XfP1pQmBvmPGsT5V+nEa4y493NP2lmgF3DoSXY+H42wZIkXx2v4/vazTGrzHNkzYmTrLKTfVQTO1IFW9an0LMcDLvBt/3BV6u2k2Wd9tM4z5e4eO2egy/U+S5hUw48O1FlUx789a0k3TeZU9Y5fQRj1kruP11hfm8Fa9I0Rl12OOlWF6akas/88kRRAku5FeUABcWOJHYez5HErg2r4eCqY38oqEmBdQVmwc62TKhIF2Q3SYZsbq/ouScEcjQ+OFNjoctBZUvnm4FbDG593WBeH8GDpykgzG5PQzdJfjJv37mLaRN1PutqpdIveOpBnaXdBI+cqnJsOMHkcJDhsSiZesdYfzKisH12OvFmC9lDmknrGYHUrnDpp+DNOajzAz9gA1770EPUPfZ4698Wj4ojTcfi0QhsVDGSAkdqgrxRjVh9On/KSOMDr4d75wYpnuVEz0yi1loJnNHI0bYojZtcVC3yUzCunqXbUsgssxD2GHgiAtliZFSXjh5RiboNXhtl4YtBgrgVfv+GwZAt31/P+lDx1QBBSZUkv67NOAUd5lS4LIv9ehj3B7sKgu2U+f3/hI+Hi0NWuTySqEiFLwYrzOstqE0BXwT6bpPc+N6R4XfedKlKfbqCpujElLY4+OlzDM6bbbAjW1Loj2GpVYk12lH20+bNGCJ44TiFSz7yM3xrM5f/SkEXNuI1k7E2DcRLjPm/GQGxAMSbIR5ERpuJLF3D9kemIeMa+ef1wzc4Bybeacp6HCR+sAY8sngxtf96kMjChQC4jj6aWn8Wia++xBcP80bP43ir9wQsio6TBA4RxZL6Ln9/ZSuWFA26xXDM9RA+s5Fh1iil0zOINdoIOxWsCYO3jlGJdfHTc3WQsQvaiKxPTFbYmi0oqpWUVEom/T8zInvCkm6C0mzYkmM2jqjzmXHObpVw6rfGAfGMwy6JO3JonnlptmnwfzpHsm5snH4rFfTmHzn1RxKaKqlJEdSkCKr9UOMXnDPbwNYyEd3J1a/3wkcjDo21sjtCDqjxQ3qgYxGczE5QlSuZX2Dnk64qg7ZILp1hoCtmqKXZDYO3yHYNyHfF30adyopRG9HtG0lVu3Hr0X9kco9hSCmJrVpFYNqnBD6dhlZRibBacY8bR/atv8dWsH+9NveGH6wB34lE+Q6a33mHuscea/1fvSeNl8deyIa8XkhVaaUbXjLzCQaULeN3lyqcsSnOuC8tvHZJHGtzJme+YyYWyzLhyZNUeu4wp1zfl4pIXZGo/+NwxLe9BEGn2UVo3OoDu7+iNnAepFbIv09SuGTGd4/P/P8Fz0xU0BTIbjKZVVmNkuwm8H5HZI4/HySo9wpiNjhjroEnBptz4OHTBSdlFjJi3K30U71U/WciV3pzOPtro1VvfyeUFB8150zgj3nz8G9r5BfVvem9tAG9ogKsVjxjxuA7aTKe445D9Xr3sCcHjh+8Ab/3uc/p/59/0qV6C7W+TMoyiulTvhp3IkqDy8ec7j35qlcOucHt/PbT5bw61sUnR8c5d7bBlIXysE/F407BskJTR6GDdy4kyCNjZOf1FjxwuoozLjljjsFp8787M4OaFNicKxi1ztynP16kMmmxwbg1kuVdBHUpcPzyjvu7NQs+O0phYU9BTiPc+WL7nML8noIeFXKf8r8/onNszYKAS6AYkBY6uK5NHw8TvHysgmYRWDRJjwoYWGowYKuk555TUt8JKFYDq0vH4rMiPOno9VWsFlacCZNbnrKX0n5NgfXd7GSfcgajzv4VFr//iOzjD9aASylpfuddtv/lTjQp+XT8CNYU2bFHqvAGahi3vJ4+5Uc+iTi9zzjGb/yWxd0FLx6nM2KjOT07XPAUR3ihh5Mt2QobsxWmzDE49QgZ52W9Jc60BG8WOtFVKKqB82a1MUkOJxb0FIzYYFazXXuNytQFkgu++rEW/kjj/ZGCJrfg4s8NZvUXPDpV4aRFkktmfr/O/fQhgmXdzBqMGj8kWnrTIiX59TBks2TQeh+DdjS1+51iNXCmJ4g3WdFiB8+2MU6awMMDKlggNzO8aAx/OPoPFPmKDv6A9oAfpAHXGhup+vPtBD/77LCt87uMZI8Ys11OihMaWkBt17PzcKPe+98tAvkR/8+gSFNvoBPsS4ZgR9reaahhO9T6BPVeKzoqXWuTZAb3HVcTqsTuS5LwGGxMOuhWtc+ftEPEDtWpAm9+F/r97DrSJp10YCvY27790Ax4eO5cKm75PVpjI1puBs2WBO7qAI7wEXATf8T/a8zuJ1jZRaCpZuRLAog2+2OIjn+7Y3ToxvQjDhyaAooBh16MfpihqqZstdHxGkdtMGt8Gkf//l5G540+LJs77D0x/1eQUlLzj3tpeM7srCNcLqw1jaTHdsmUKAJrhh9bVgqWdBeGXknwG5P42XReI1GKcH8bxlfa8eSXp8PX/RTm9hEE3PD8P38YHO4fcXDYmAs5jZKcRtnOeEvMh0dFElXM+LEiTbbNj2qIhw+H61xW+SHggowA+50rSViguneS1Ew7vxpk5YRFCud/E6XbzJnYCvJbl5NSEp47l4ZnniU8dy5WxUKyuZFb376C35z8N07pdsrhOYhO8L0z4HpdHQ0vvYRwOLAVFmAtLMIWX4c1tgabR8fm0bC6dYTSFl+oXuoDPKT3CeKa6SJSG0S16dQelWCp7mpNnL10rMJRm0zu6DmzYVUXQcJi0uDm9BE4EgcmtxlymMpvy0oEV3xqltm+MU7hVx8aPHCaQsIKXarhJwt07PHvTiHLj2hDj8p9LfHjdfuuYF0+vD9KQTHgkpkG/pD5vL0/UiAVgdeaQh//EE5Z+BHdanqjrEmiVLaPQ8by0qiPNZDbLLFpgsJVVsBgmCvJyr7ANxD64nPSfvaz1t8Ek0G+zm3ii5/52dbXzYlzI5y8UDJlkYF186fEru6Jo1evI3LM38sQihGPI2w2xE7Rm+0LoHQ2GDoYSTA0qF0P6z8hWm9l64w2ycikUyXQ08O8AYIXCkNcMU3nhGXSTOScYiYzshslv10YoWSdih4++DHuSGlTl2Wabdkym4+cfvO+UOWHnKaO/9854P2IH7E7Dte9sbCH4KMRChvz4BefGZ0yl6I+g/TRjRT741SrKos1Jw3lLjI2WyhuiW2XFdppHNOXrIlTGdJrPD7h4qppl7Glbj1ZSQVbs05eAxxVmyQ3LUrfgiHUvLQRAPftt7CuehUba9dS0ViGqhn4cFLiLqKLI4/0+gSRWV+37o/nuOPI/9cDKLbOZTf2hR9cDHyPkBIWP4f85A80lTqpmmd23lDT06k5+xIW9+vKm9V/p1kzXasX79Wwa/DQ+S7mFMXpEda4Y2WQtDKV5lL3HjezLRPuO1PhX0/qzBis8MwklewGydWf6PTdfmQP8b+BBo9Jn1K/OyzEHyR215X5Ef89aBYFRTMOa3xd2GwIq9V83+Vlzc6i4PHHUez2g1vv/wsDHqrBePtamqbPoX5TOlrAHO5dp5zM4l+M4t0t77Osdlnr4n8b9ze6nWy28HzmRIUplVnkrKsFTd/vJqlgxs2tBmQ27jnZEvYYuEPmt3efrRB2CO58UT+iBvL1cUq7xsQ3XqZSnQpJi2jVWd6bV7S6iA7NY79LSKpmguuHNshE7GbidPKSjgdmCPh8sEBT4KTFP7ADP8JwDh6MrVsJwmYjSpLSwAbWBzdTRQzNYt5PSRXOCwXorSeomXADd256EYfdiT0ZYrvDQnGN5KppBtvOHkX3C6+iKKM7is2OYrOC1doWFTjM+MEZ8Omrq7jyxcWtf0/R5vG7ra8Q22jFiCs029ykJMJ83T2Nh86MIZRdyvviKRQuPp1zN3/I8PI2laVyTyZLs3twQvoS8hrqadxkeuCJFJ3Pelr2qSBYl2UwqEcTM4us3JflZ/Jig59+0/E3NSn81zTAOwt1VKZCWnYM+zpTyGtTLnTfZ6x33wg62lfdJVVYViJYXyDoVS4ZvvHI3WsxK8St4Eh8NxT9fsSRRZPbhj+855LdgEfiC9NaMLcxt4DX+46iuqgS1beSSmcIKQR94glODoWZHI6Qo+skxv6a+ekFfPHN3XyRmk2DbpZgj1ZTOK66lPHhKMF3M3COPoGCRx/b4/YPN35wBvyNhdv53dsryIvV8tctT5G7pQ5DU1iZU8grRxXw86WLyG1KcsOlTur1wTgjLoY3zeaojVaGbJakJNpLv/1m4iXYkxbuXvI0onnv5yTituMKmyLGVammBvjhxGvHKKQHJD13SAprO3r1FWmQ12DSlZ6epPB1fwV7QpIehPSApN82yRlzzWOo9UHmEZAdX9xdMHRTx/M0v6c4rPrNP+KHi0dOEywvUrAY8PijOp68GNZ+Yd5QU6hK2EgPmM1Pdr7vr5xFwgJ3nqsSd+tMikfou0IhdbUdR1ywuLtg7giDAb4wJ4UidNE0gkLw9chL+MIq+bpiDhE9hsswGBuJ8pnHTZam8XmjDsVjoG4jlR9vo7nMQ8+vZqKk5x3Zk9SCH5wBT5SXU//g32j+5AukIYkMyuSDyV15X1nBxAVJLplpUHfe8fQoGETtzE9h+RosBmheFwu76pRmSc6feZCiG7tAV8zCgUORAq3tniRzU3uBpYgdNuQJNuQLtKwkVyv1ODbbqVnpBSmo7pGkKWCjZ4WZ1HlrjEK3WuhaqdNjazZdGvag3/ojfsR3DKuKBf3LTDt0y89VtmeaYT4Ar24QVE0X5viaGJe8oWALdgxU6gIMq8CaaG/PDAGNHvN5Kqxr/5sdo2J83E9lVrqdpKKQpuscG45yXCTC0bEYdgn/HnEuj9XOY8653+C1+wAIvXI/2//yNAVT7Hj/9AH4D3/l5e74wRjw+MaN1D35JIGPPwYMtvVVeGqCl/XeCNm2DK6s6k3/J7465H0NOcwLH3Syx6RkWSZYdMhuamOb6AKa3fvPNd0dtT7wpCSwugzm2hxYNIE9AfYkOJKSLrUSi/Yjde37gPJ0+HSowqwBgtPnGZwxV7aTwj1Y1Pmgyi9o8ph89OIaSVHdPn/2g0WySxynZiGs6MSbLfiaDzwtKb06hUMb8ea0d+rmutxcmZ3OE+4BjM4fA5m9kand2XDi6fjyA+ROsMCFb0N2v8N1OJ3ie2/Ao8uWUffkU4S++AKAZq/kq74KnrhgwiqJqh3acTx8ikJFmiDis2FJz8AbMbBureSG94zWZqn7QsgBnu+I8trBYEMeexQeWtJNsD5f4I5LJi+WP1IFf8R3ClWpELeY7TSLdmH1GJlJmlSFKquKIQSypWLWqUl6lrdfR9bgZtJ6hXdtyUlQCMYUF3BNUzNXNbXFInd8m0W4SqHHqVUIBTjmdzD8F+DJbtfT83Dhe23A6/79b2r/9eBBbbMs00ykregqyApbOGuZg/SWXpSJdDchLURas8ByzTk4ajX0zaXEN27ECLYXAlnRVSGQJhn7Y+b/B48qv5n72rVrS3m6GVst+S9Gphb0FKj64eX6hxwwq79gSovmtS7g5WMVspskkzphvRwoml37H6veF2pSYGNe++5IdV7TSepMWG3OaC+OgT1ZzhrGvxclPQi/vkIl4hC4DMkUZwH5iRifxCrZYLeRanFzRe/LOHFxjMDzr6DVN+LsU0zGKcNwZwQQS54H4PQuJeRoOo/XNpmNG4DmMgcV89IoPqEWV8YuO+NMhcw+MOIy6H/m4TkRfM8NeGT2DGbdcyNdNnfu9sU8VpJ+B95y0+iu/NME7kp8jVQEqi65bksex3wTQqvaLdu4m6yr4rJhz0/Dnp+KPctH9RvzQVXIunsKN0YX0lRWy9//oxMYEiUwqAcF/2k/hNdlG7w72EK/vAD/yvGTGpTc/rLeagimDxFMWiq54jqVG9/RO4hRfXC0YG2h4Oa3fqzFPlisKdxzyGtf0AU0ek1GS8wKcdt3m0Z5oPi6r2BLrqAyFSrTTBXI/5by48bc/alqbY9VRYL+2ySaAq8fo/DB0WZFpZCS//xTx5mAN8eKTpleu+KJyQplWYLtmRC3de4dWzXJY9EzSX9rNlpVFc5Bg8jIXYF7zEjuyC9iRtkMvjn3G4SegHAdenUZG864grSTRpA9sRDm/KttZTkDYcBZMOb6AzvgveB7bcD58HrK/vUBkR0OrF4Nhz+JIzWJw5/EnprE6jQon5NKaIcDTmnkp32zABi02eDmjzUs4c5jYildIzSXujAE9Dy1CovDaJ391KzwUr/GS8G4ekR+kgvzszj7XZUhWyTf9BUM2Sw7dNN+bipESpI8UlOLBvxTpHPya3ZUCSIzyX1j7fzmXYObLlUpyxaMW2VwXYvgkUEb2yRqg+0ZUFLVsZJTU2B5idn1pjRHUJptdr0RwIQVkgu/MNpR+Wb1FzwzUcEfhr8+r9PggbvPVbnjJb3TSkqAc25WkQL+9KpBvwoDf36UwFYXulUStAn8e9FH/hH/e3Q2iFX5Tc/1QEJ8C3sI7j9DYdJiyc8+Nwi4YG6fNu99T7j7bIU/vNFxYDiUvp8NHnhtvMLi7uYD+syDOq8eo/DuGPOp6Vop+dNrOp4Y1HbzoHUrIfezFe3WYQDVqbAtS7A1S7AtC8oyBbV+kC0P/q1DbuKE1SpNTz2DVlGJI1Ow8aoL+E3yVT44/UO6pnRtXd+2X1xGsryckk+nIZJReOsS2PApjL8ZJvz+sIZSvt8GPFyP3LYIQ9NR3S4QCgjVfFdUgnOXUP7nB4mfdywXdTHLV/OcmTxRPg7j8wXYuxZhL+mCvVtX4jOeo3bmNorOyeL1o++i291Xsy1V5x/9/4KOgoGgW+MO7pn9b74sGML9Q8/Bkf8yfYKr+OsL7YWtKtJA6RYjP64TX+XGYsCi7rByrED1xqiRKfSeH2fqQknEBn85X+Xv/9G590yFhT0Ev/zQ4Jj96FhT6zNvfHvS5Fav76OzfohO2C8JKAq1qkqdqlBrseCNmEb82JVt663yw0OnqjiSkj+8ZrCsRPDOGIU7XtI7LfV/eYLC+6MUjl5n8Jt3O/fQNAUeP1nhJ98aHbL7/x/wfc937C8idjM/1Hu7bG0O8vIEBUOBi77o/N5Y2d/g9SFW7mppvBGxmY0Rvu0lGLFeHtbKxzl9BEu6C9YUCgwFbnhPp3c5vDtK8N5IhfufNnDH4KHjiyisExTXJSiuD5EXDLTuh7BI4qkGc3M8bMyLszXNzVY5gRs3rmXkunUkwxZKs+HVPmP5JuVUpFA4b0QhN8VWUf2XOyn5+CPs3bqBrsGH18Oyl2Doz+Hk++EQu9HvxBEz4EIIFVgE7JBSTt3bsoeSxHx7cTm/eXM5ABZD4+jKNRhCwRCC2+c/R5PDxq+u0YnZBYnGEcSrfsKuDGoVnbstzzBq1koaIx7Om3w3UV3hhVm3UJql8ec+9wFg1ZM88tUDuLQI150+Hr14OgADSg3+9NqBTTffHm1ypXeWSj9+ssLVnxjUpkDmHgp5NuXAqxMU/CEYvtHsom3VoTwDNueZ5e39toKqw+Ke8MnRgi155kgf3aWha59tkss/1SmoN//WhSnsE7HDL2YYvH+0IOQU7abQC3uI1mKbqM0cMPaky7w1C3Ib9q9o5umJCmXZgltf1w+6XdrhwivjFYprJCM2mOd1f/FNX0Feg6RkPzWipw0VzByicO/T+ndPCvV7hl1npwcKTShYpMFmXx6PDD6TKlcaYasDVRoUBavp2lzJgMAWJgSXYm3S2lVf16QIHL4ERr0ff8Scdpb6cni11wnkTD2Zv47PZdOEY8m84QYyrrzC/JGU8MWd8PX90HsqnPk0WJ2HdgI4sgb8RmAY4DuSBvx3by3njUVmzLmkaQcPf/UvFDrue1xVCVo9BG0uglYnIZuLiNXOCMc6ChO1NG91scFfwHP9phC0unhw9gOsKIE/DL4Zi2cLlyybxenLarj7HIXlJW0X8681daQkDB7TM+hbJjl9UwJrraW1U/3BYms3nWkjVZakqVz/vkH/bZLyAak8Pk5hY0ozqWGDYevhwiUJnLUWEBJ7ioaRFGhR1dx+doL3Riq80sdOvqbz68YmNlqtPOPzccp8yVlzjFbWyJpCiNrNgWXngHKw+HyQ6CAktCkHuh+gEP7uWFksGFC25/vy8ZMVlncVpAfg7hf3bIVLs800x960RipToSlV0mRTWlu9HSrCdrM+YH/CBTOGCOb1Ftz26v5dh9n9zJZ0Vh3WFcBTk1Qq0uGsbwxOnycJOKF8ZD49qmqxr+k4Ws48Cmb1U7FqEkcCvFEO6R44VGzIM7vW7x7Oa3KZ4UOrZlJ1v4tyCbaSEhJbtqD6/fT86EWoXQur34PV74BsOaenPGh644eII2LAhRAFwPPA3cCNR9KA745kRQWbnnkYXn4PgBVdBAMi6Yiag5vP764cuKNPBpvUeiw2g5OTISIOuC83hZADhMikMvQLhlvW8MfA0yxq9lBT76Tnjj3T694bKfjJt+a53jml3BUSieGQqLG2QWPOiQk+ynNSlQqGXfL6mlrcm200b3WiRSyYLOC2ASSSptOlR4C04igJFc7Ky2WrzUpWo+QXnxkM2XJoT8E9ZylszRYM3Si57LNDe+jn9Rb4IvJ/liRc1lXw7ESFoBP67zAYsAkmHsaeqHvCnrrNJCzw6niFgAtOXGrQu7zjMrtjY67Zy3J/WCoPT1X4ekB7P/bCuiDjF0LKMtNDXJcPxbUdm0o/e6LC7P6CwkaDknJxxNrrgTmgKUaL0W55T0uCXdMRmoK/sW1g3Nl/87uA7qdVYXXu9kykFMIvF353PXAhxFvA3wAv8NvODLgQ4grgCoCioqKhZWVlB729nTCkwUtLnqHw2n8iBfz2FyqvnvE2vdJ6YUSjJMrKSGzdSmL9ahJfvUC8Jkws5IL4nu863WFDjZl3ri0twVaLFWtckBkxYK+iVpKYHZqdgiip5IUa95sjfcf5CglVkNMkyW2UdG/Q8TYpdOuEix1yQCBF0tMWQ/HobEg6sNRYyOikTN5w67w1KJePRtYTswvcUcnAUsnVnxz4g6cpsL7AZGM8e6LC+FUG3fbBJnDkxbD7NZrXeDBSNbadFOa1bDeVYTsPPnngDTL+eJHaGk/dX+ji4L22g1EInNNH8PlgQdIi6LtNct6s/z2TKOSApd0E41ryLAkVbN/T/iSGgBqPi5ygyVEULp1Sfy7b3XEMW5BYpB85wQiD6jZ3+G2NT8EXNe/9FV0Ey/vZKLY7yReCQkOjIBZFDYTRQnH0mIIWU9DjClpMNd+j+9EzU0jSeoaxFHXHPfkcHGOngi//sCUyD7sBF0JMBU6WUl4jhJjAHgz4rjgcHnhCT3DN59fQ86W5nLJA8ucLVLqMP5khWUNwqA6cFicOi4NoUxkr597PCpFgpd3GyDU6v37f4L1xNlK69KBPKIXcBomcNa/jNmySbemCgnQP6UoVz+S72eRTCboEwzwncEI0jX4rH0NPKGz0T2BeYDl9Awm6RywQTVCNHzUSaccG2Rt0l0FTqqTOJahwKTS5BMeskq09KTfnmB6aO2626koJH36d8UMxeLui+LQqXC2eSGmtk/AsP5Upgr+drZLVLPnzK/ve8Tl92nN/DxS16ZJ5fQQ7fGa8++R9sCYOJ3YX9Pr/hDl9BGv6GoxKRNlR7mbSHmY0YYfEHTs0w6YpZpV02Gme85BTkGZTsFt1nNYkvmYd6xabueBuKOsiabAJDF1g1cGmSTJDCulNh+ehcg4ZQpdXXzks69qJI2HA/wZcBGiAA/AB70gpL9zTbw6HAW+MNXLtw5P4w7NBPh8seGry/nWU/tMrOtlNkuuuVhGKikOx4khE+Oe/JDV+0+Nq8EveH6aS2yDJbYBuTRY8Te1d1rhHoHuT6Ck6IsVg/vAJvJdcxNVZXRlROheHNHDcsI7qrQuJnvM75vSBN8cq5NdLfv2+cViSeIfSKOKzIeKwhAp8w5uJZOloH6e1/s9SEKN4bANfuF285fFQHrFz7myDo9d33N6rxyis6CrYkgOTyuMUbLIwcf4h79b3Dsu7CFRJqxbI/xJhu0lf3Z4p2J4p2JFuOgud9fZscsODpykgIS0I131kLpOwS94Yp6BLQUZAMmUfCp52f5Jqm0Iirv7XFDoPFFJIRIrO0hyVRGEWPxl3Navj5Uwrn0FpbAcZvlxO6XMGo7uMR3W4sOZko7hch3UfjiiN8L/pgdcGKll56mRs4QQr/3UZQZvO1+VfszWwtXUZn2EwUIOBfc+la/5w2FZB8RX3suOnI9hatJpouIaYIogJwVmPmvGppjQr111itBL9M6xe6pJBrElJTiPkNUjyGiC3QZJXb37elUamKWY5b2WaoDLNLOcdXCr512lmf02EwBuRPPNg+zns0xMVnAnou03Su1y2Gvg6r8lXFZLW2PWcPoKt2QJPTOKJmtt3R8ETk/gjEldMHJES9xeOU+izvU0O9oHTFHxRWrnBzgS44m3LFtSZIZuMPXS1f2EKfDTQgpCS/Do4ZYHBsSv+9wbscGNeb8H7IxW8UckfXjcNnGLXUaySrXYLv7lcZdAWya2dcKbbQUgSJXFqmhytrKIfEpTcOPM9Dkr0BCVGEgTEwmnEazqfyiQs8NkknYuDzbgHnQFDL+aVhU+xccN8zhMjcG7cgSw98ORKwiLZmg9rChTK0gUNXkGjx9RE2rUA6MLuZ9Ivdxh90/uyqXETjy9/nE1NmyhJKeHqQVczsctEFHF4uUc/CANeH63noeuP4dzZBvecpbC4h4JVsTIocxAjckcwIiEZMP0ObCmFcNG74C+EeJCa315M/cw1dD+1ul2iQfaawro7lgJg+/c/+EnZrQD8o/sF/G7TywDkJTUqrJ1zOT2RXYz6LgY+p6F9rDFih4rUNnbGx8MEY9ZI/C0lxzuPRTEkXaug3zZpGvTtskOyc1lXweNTFBq9e64oc8fAG4HulZKh6+2M2HwIUon7QHk6nRqVtQUwt4/Cku6CWr+gpFLy9/+YJ6UiDbKajnzz34i9bWA5nGhym/mAiN1MtA3ZLDttvvBDw67c922ZJh88ZhNmIwSLaViPWWVw5lyTDXP3uSqJVAN/ldhr6GxbD41/TbQQtbt4oFxniFLKV/og/pC8lFqZwteFHyC+/oz65b4Ov9WdBu4uvUnWN6LX1Lb7TnHasaY7sXmS2CyNWKxRhCrRLBBQBYGEhWhMNWPcUQvWqAVX0MCW7HgtI3azmKjRYxr1pd0Ec/opeK1eeqf3pjJUSXnIzDx393fnqkFXcWLxiYfNkH+/C3kwQyeXfXIJt/1pPVE73HahSmW64JiCYxiWPYwBjVX0/fI+XDkD0M99heats2j68Jc0ShX7W6mEs3WWT47TqCo09ZpEoyJIX76dC58uBeDs3x++/s4WTfLKvWbV47ujFPIaJL3K5T51NN4Yq7AlByrSBbUpZpV/l2rToA/daLSrrqtMhdXFZgHD2kKBXYOSSklJlfnqUt3GdEmopoJdXie65WWZ8N4ohQU9BS/f993KcMVdBvbIjyzqQ0XWoABpvUNsn51GuNJByAHXX6nSv0xyynzjkJp5vDtKsC1TUO8T1HuhwQvdKuG37+j4YhKHWyMesBC1i04H06hVwWIYJCzwygSF1EIYVpHJiMgGtJDKnEA/+uSWYJSuQNm29+mHke7gxcEJ8tITXJOsQ7UZe84hOlKgyzgomQBdxyP9XUlWV5Pcto3Y2rWEFi4ksnQJNHc+jVzSC/5xhhWjEyrzTvRI7cE1g67huKLjDtmQf68NeHO8mcs+u4x1Deu4bEsxY9/bjFUztUPeHa2QsO45IbKzmvCvZyss66bgtrrx2/1kSx/X/20dznCSN8cK3hynMsFdxNrmLVRbLJwaDHNt/rFMCiwA4GfNAQyLA02PYwA6grd9HgBODoX5wuUk1lJIs7Po54OpGt/2Uthks3HBF2YLs5svUTEUyKs34+zn74GtoClQ44eKlpBMRZpZMn/9B8Y+KwAbPLCwp2BLjvkqz4Bfv28wdKPkzxeqbMwX5NdJjllltCZLr75GJW6F+57RD1oK90ccfjS7zGTd7rOczUWSb8boVEgrZ86We1SR3IndWwTWeSEjaA7uS7sJ5vUxmx3EbYKsRsmtr+vkNJq0PotBp42DO4NE0ugRHe6hR6co/PQbg6xmaPKAf5fvbTlxElWd94oUikR3K6xOSWFAeRMABePqsXp0Hs/IZ3FcZ1hLwVtG0CTWrs+HtByNIZkBvCkxhICk4kDNH0nCNZCkUkgipJIoLye5vZzE9m0kd1SAtkv80WrFlp+PtbAQmzOKtfZzLGf9lZ9ve5ijB5/ELc0NaMtepq5oBNXH/o5qIamOVFMRqqA0UEpDtIEdoR0EEgF6pfbiV0f9imMKjtmvc9jpefg+G/A7593JGxveaP07JSS56AuzDL0mBZ470QxBdIY/vGp2lr7zskH4ZW+8RgkWI5UTPn+LAWvMrNnzxyvk9oyiSo0nUlMYF84iNXEm+fydx1NTeKmiikHxtlhGSLiZ7uzH7dlV5Mb74ZWb2eCIYdWz8MfO4vzPlzF63df0Pn0HugUmZBXyyOM6C3sKVvY6hSwR5GLn69yZkcq3DgdXTuvYWfuzIQJfpC08Y9UPLGtf44fVRYLVRYL0IJw3y+DF4xQ+PLr9eRKGJC0E9V6w6tC9Au54ec+e+IY8WNxDwRdpS1At6yoYXPr9DCHUe80qy5hNkLSAPSHptQP6bO9YqVmeDpq6Z4rhTqN4qKjyw/ShCl8NEISd5nW3JSUnLdqz+NSOLEl+jbnsp5M1nhtsZ2Cp5JT5koFbO16bbRnmzGtxD0HULjgnEEQCi8JubnnTQNXhnp+qbCgwczcPP66zqovgwVMVSqqhe4VkykKjUxrrgcJQBS5fnFhjW8f2zTnwzBTBSGuY80MhHlDSuPwFC7nDm/B3a5M7fFKM4uXCbTQoCl2q4bQlOmOW7992g1YHVR4/Ve4UcvoWUjiwkGhWCqEsDyG/naiME0lGiAa2E13yPJGu43inwVz5iJwRRAPlRJu3EVEUonYvUakT0zv3rgSCOefNwWvzHtQ52pMBP3xxgyOIi/peRP+M/qQ50vA7/KTaU6n6STn/eOoyLpohufktA09+mOwhAXSvwVK7nQVOO+vjDgZtVXl9nEKVfRVVrAJgwgaNAWtgTV+DvmsUNBX8eoL70lM5Ixji9rpt1KlLmFKQx8RQuJ3xBggYduY5ywELlfbVRHWdW+ua6d0sEPIJrFsSiByJYoG1NiunzjcrId8Zo/DrxsXoCCKJXK5urOHbPCfPTFQorNXpWWHyVAdulUxcKpk5xmBYSQPupCQRtBBtsNK40bNf5yyrCbKaZLvk4LFrdMauNrBqZiPjne+7i3LtDT0roGdFeyNyJI13ebrJimitktxNQXIntmVwwE0NLvqNukd1OjDzCS/fa1rxpSWiNcm8p6YdB2O8OysT39oiUJbY5en0Rlu40Hvop7qgm8IXZyj85kONyZ9aKNissTldIaex82tTVAfHrJLU+gXr8yWv+7wM3GLw53cNQg6443yVHRnmuTnrGwN70gxxJK2C9QWwMQ8W9lDpXS755UcHl8yozjbIrlao6pfO8X1XMNfh4EZ/Jn//xKDbBsmtrxs8NsnDyT19TFpkAAZb8iX1iTT0kIIzpNMruIG75ylEwlYymzqXmd0VhoCPhyl8OkxQ69eAupZXC3+8seW1CwQKLq8HW/2a1rq5YCJImr8r2b4iXOWLcTZU4cwZhKvbCThtXpwWJy6rC6fFidPiJMedc9DGe2/4Xnjg7VC7Hv4zFcI1rLFZuTIzi6kLJafOEwhhwXHa8USOGYGwAO99jvxsLitvncQSZy0r49tpCNZz/9M6IQf84yyVR/6t88RJCp8PNh+jWxtCHBUN8rLPy4ceN8/Ti0JhJ7X0YwBiQvBMVhH/dpnn7eLmAJc3NeOypRPNHESyIkTNy9vJO7qRlK5R3rR46fGal297Cx49ReXhkA3xfhQZkO0M6XepEW9npey7Frfc+jOVZjdkNkvO/8rY5/R9d7w6XuHDEYKUMNz2qim3u6ynZPAGwZISM/6f3QQ5jXKPiom74+PhguJq6L9t/+/nmNUMNc3uL1jZRWAo7Y356XMNzptlJsyb3IJulZITlhkHVOSzvz1J7zxXQVMFo9caHL2uLcG9OzbkwZy+CttzJSctMxi+cu8zswaPOdBsz4CNp4U5f1WCqvmp7ZbRFFhTZDoOW7Pgb2erNHoF9oRk8BbZKmj22RBBdiNkN0kym/deN1DZO0k0qRJLmEqYeQ17P/5/XK2zyG+GUZwxyYSVkktmdhwYdq9kTVig2g/VfmG+p7a916aYrdnU+p4M3BZneGmAYWVN+GNJNCFYnZPB/IICFuQXU+XJAMOKNGxg2JGGDSmtYNhBqrxqvRuniHGm7XxchS9wx/BH6ZM6GAC3qlO0+O8w/9+QOwjOeg7Su+39gA8Q3+sQCgBrP4TXO1LMV5/+EJeveYKiiIu7Fncj+cVsbF26kH3r76n4wx9w9h9A4eMt3aObtrHl6ouILavmlosthJzw6OM6j05RmDWw8xDMz/v9nP6l3zJgw2yWTryVf616mqqWRa8YeAXXLZsGwSq4bjEIQc39/6T+2WfpeW0uarySfy41mLRQcsPlZtIVaYZ/8uvBkZAUR3S8CQlJgRHbP077kUDMajYqjjjgxCNYUn72LWq76rTUoORPr7aJbt10qYqmwtHrJSM2GJ2KRzW6zThqdmNHSYL9QZ0Xgh4QBmQ0m8yKRjd800+wootAAEdtamOW7Kn8/VBQlmnSRN1RGFQqEcCiHoIvBwo8Ubh8utGREpqZpH5wjM1FsN1hYbtqIbXUwsUf7t2Ix6zw75/rDFJjPJ6awpjVBtd/YBC1dSybB3PQsWp0GESCDlOOtSkFytIE1alm4nL8StlOVbPaD/edYUom74Q9IRm7WnLlp3tho2RAamjvhVDvjhJUtGy7yKoTjo/mZ/pSRorNKMBsvS+XpYzEnjmz9TfJYF9iO84FaUORBj0btzGycg0jq1ZTHDSZBVu92Xyb24/5Of1Yn1qI3C3peIvlVS5RpzFAewh7z78Tqz6JZMP41u+vP74HNxRugvevAT0JUx+AgWfv+UAOEN9/A/6fqbD1606/Wm13cHluFj5h5cnaXiQ+LCVZbc4zc277I6lTj4XZ9xH84DXKZ6eweryfO0aH+EV5nEkvqjx4qoK1r4uVeph6y/4b0ZGbJ/KU8jT3Js/mdf1YBimb+OWMt0hzhSg+tp5EVGHNxzmt3vf+YOJig8s+M/hygOC9UQolVZLrP2i76d8ZLQg5BI4EOBOmIFFeQ3tNkR1pkL8Pj+e/gbADNg1JkpUbIfedFACemJzD50PaxzpSwpKnHuoYd1+fDwt6KjR4zeTtg6cqXDbdYGk3wUOnqfQql/z+DX2/qYKHomq3JzxzosIXg0wp0z+9qu9XM4mkCo9MNSmWcZsgu1Fy09s6Re1ZcHx6lOCrgQojKzPovilBn+0NqImOxtoQJjXOltyzIV9XAOmB/ZsN7ESjG1LDsLZEcs9pFiIOwfBojCxdxyol8xNufvWBTrcqmDlY8PzxCoW18Jt3dVKD8M4YwaZcQXYTnFwVIxqy4KtT9pgkr/PC8m7gcWvkOBP0ssUodMb5czKbCz8wr5zdn6R5dIQXuzv5wuVEAsdHopwfCDI0FkcACWHnufQpPOJtszV2aWNMbDJ+vQcR4SCKgwhO7E0BupWupnfpcrrs2IAqDYJOL6uLB7K6eCAbCvqQsNoZm5jDHyN/51ee+5md9jJWPZ/0sKlA6HNa+feFQ0lz26C5HN6+DLbNg8EXwMn3gs29/yd9D/j+G3ApwdBAtZqfw7XQsAXqN0PDFlbVruCK2HpSdI1nt1fR9Fo2AEKVZA4IkFIcZcunmYTSPFx6UYJhiTgNARt3P2vw6lSDv6Q0op/6KK9ak9yz8G/7tUtCSromNQbE4wyIJxhQoyHeTyN7aBNpPSJ8syod/2o7v75CpTqt7eHy6JIL6hWCOAhjo1CpwiliRLEw3TiKqV9XM2p9A/efrjC/t4KQkolLzHCFkCb/9rOholWEHiA9IHn80Y5GsCYF1hWY3e035wqymiTjVstDbqy7vwg5TGW+3dtszekjKK6ReyxMeeA0hco0QdQGR202p9O//YXKscsNpizad3PgtQXwnxNUElYoqZKMWC85esPhO+Y6H1xzTfvZRGGt5P6nD56KGbOanv5OzzvkgC8HCmYOVkgNmUnjncVU/03UjtS4fpydY+NR/llTh5TQtMVF1UI/YBYslWUJspsk2Y2Snjs6cvxjVrPQrdovqE6F2hTBLz4z0BSz7dkjT2mEi5J8OMVgntNBpcVMAFilZPICc9bqHdZMdLUHLarg7x7BGBLmjQw3b3k9BFSV3vEEFwSCnBQOY5eQAKYW5rWuC+DUYIibGprwG7vsoGIBmxtdeghV2gmVQWhrAiNuICwCd/d0PN3deNT5WHMKuKVLL+ZHyvliyK0Ihw+Kx5h2aSd0DWb9HWbfB+nd4afPQc6AQ7oG338Dvh9YWbuSK2ZcQdeQnT/9sxpfcQQjqRCqcLQuc/OlCpF0g4hQ6F+hc8NLgvdO0TnJ6eY9fTTTClcStYVJNg/FmvotUkgUQyEZK8Tm2I6utL8z7YaBCkQUhdPmGVzwlcHDl+lEFZXrn4V5fQSPTVVJ334ybydf4u4MP194VK7a2oWIlkk1qVTLVH5jeYMiSxnnKj+nzJbkr5/Mobg+zh8uVtme2VId2iy5YprB4FJJfRa8NSCb1Fgtg7YadK/oGJO84zzTaymog8I6SUGdpKDu4LuifNexrKsgYYUR+zDU2zNgQ76gxi9wxiWj18oDLuN+dIpCVZqgyg9BF5z6reyUEvrB0YIZQxQe/rfOlhyzQndvBUyaAhvyD74t3L6wvBtk17eXb33uBIVve4vW4rCiesHNryU79dYr0iFvD4Nukxt0r86qDAt1Ppi8xCwq25ZpJkWDrvYzhBHrDX77jsHDUxVymiQ//UZSfEItzowk2ywW3nHk8Wwm3PK6Tlaz5MYrLAwMJrhgtk6XlRZUu0HO4ADWLjE+8bp42edlk81Gmq5zViBEc8N43k9MJNH9cQxrW4bZqdi4PX8SJ7mKEMkwxEOQCLe8zM8yGiKytZHQhiDBLUmSYXMQcKQlKO1q8M9Bbp6IV5Cv63DC7TDkZ6DHQYuDngAtBhtnwJd/NQeIyX8zmx4fJP5fGHAiDaz46namvTWDqXMheXVfrq88ibMXfclx5UsAs8XY6xME0il5fkU9kelpvHuaxnWOILM8gt9nZbSu7tRgiOsam/ksfhylMoeQsDKny5cErTq9Yxqb7CqaEFilJEvTufEliY7gd5dYuHimzuRFkl9foVKTKugbd9MvnqBXspo7M9K4sDnA+YEgyxx2ltjtLHPY2WQzaVSqlAxqVLjuRQ3NqvDghems8jWQ1SgZVCq5fHp7C9DggdoUaPCaHutx36Oy9M8HCZaVCCYtkfQvk/zhZ2prp6B5vQWLuotWnY0f0R6riswagSaPaVS7VbZP+n45UJDZtO/E7j1nKSjSzClkN0pyG+mUerg7XjhOMb3qVEFNSuf9JsevMLj8U4NmN9x3pkppTtsyQkr+/pyOMw63XKLywJM6NX645wLBcZEoVgnvud0894DO6r4G95zcRjPsUiW54lOd7pWwthg2jovT1Rlnmd3Oh962kIXLMPhJ+mBeaWzfXg0gW3Fwu7c/hVJFDVRiqd+IJVyLKsEiJSrmDECRoAUsBHc4CO1wEKm3IhBoboPM3Cie/BiurDjKXqOkAm4pMwuIDgI/bAMebYJvH4N5jyFjIdZOK2RltuSFS4t5eth9BM6+lGp7lLldEkxZKElYwTMoSEHvIWx7YRPvXuBh7M/v49ovfgnAEFcBN29cRL9ES4bH7oN4gIgQTCjKZ0o4yp/r6gkLwZLCQcwPbWWB7uKOJ0yGxZcDBY88rjOnj+DxqSqn6g6WE6VM3XOM0mOYNV3hXbrqDNpstPYW3JU+VuszqX9Hokz8cOCTYYITlsr/unRptR+qMuzUZ0bJdSawSkmi0UpKuYX872DbN4nEEGK/VCCTqsnr71Fh9mIN2+GrgYJPj1Jaw3NWTXLvM/o+GR97w67l8jWpkvJBSaZlO7hsukF2U8flH52i8E0/gb6Xe7ukUpqVmRGzwfCuuuRDNxrc/JbB4ycrCAlXTTNaQ4cAPcsld72o8/BPBPN7KegC9JawlTAkJy41Zz4WDd4drfD+SIFmOXy9KHdCkaAisUiJN6rSd7NZQDSoVJqdqxxW3AO64hneG8+wflhS08BiB9VmvnuyIK3koLf/wzTg8SDxbx5Fzn0Ehx5kfdqxrAmOpveLz/H5xRfxbN67/OYdnQGbYvzuUoUdGYKSWoO/fhRDqWobzf90ocr6QvOi93FPZKrjJC5YfA4bsk7ii963I5A4k82UNX/E25G3+aM2gHO2m7TCkOrhbbdgQ6mPC2dI/vgLwajlMHmx5NdXqlSn7v/NpBiSbpUwsFQyqNSgx472YRHnsADp2VF+1y2N2A47Z399YJS2H/Ej9oS7zlHYnCvQVHjwCZ2aFLjtIpXcBloTlZ8PFlQMieNbb+O0ue1/b+se4eKTvDR72u73PvEEa+3mc+aNSG541+w49fEwwUvHKabRl5K/Pm8a9xsuV/nb8zq2JNxwhYquCs6YY3DubIM7rzEYRIxCPZ+hsRgpehiH0YRFxqlOWKlalkLaZisVafDUZIXVxYdfgkEg8OkaKe4ctsXMjPOpBZO5ODYU74J1hL78Eq2mBhQF55AheI87Fs+xx2Ev6bqPNe/Htr/PhTwdkAjDgidhzkPYow3M0IfygHYmayq6cPu8Z6h3+HigsT/jN1Rz1PrPePFY03hbpeSmZAPPjrqIuh12blnwFgCXztD5y3kqzUYJC9YexxXWu4goVn5WdhK1ZetbN+ssXIViT2FBaT5nW2GS7SJ2ZK5AsTVy23SDBr9BzAYnLoNZA8R+Ge/MJtlisM0QgidmsiW25MJ7owQruipMWGmq9b2S7aY210nuFgVbnSTkEERtsgMVbEUXwcfDBZvyzOYCnTUmXtRd8PEIwei1kjGrO4pm7Y5nTlSYPcB8wG97RafXjn0eWge8PVqwMc9UVLQn6cCw+S5h987uDWmStIbD79kdKRjAku4mJ7rfNrnPgb7PdsmKEoUz5hikheCfp6sct1zy85kGSRXuPUNhYS8FcEEOvDJOMmSz5LfvGFgMSGxy8dTDOnVeuP8Mlc15otV4AwRdgrvOU7jwC4OpCyVdanQe+IlKdzXO1uEGJ3xgY/wqyYvHKfzhdYNJiyWfjBD0L5OUZsPKFBsrsQE7Y9k2IAtvzEeaJlAKI3TbFuf0zw3+/IrBrP7muoJOMym8a+GaVQNfwsAwFGya7FDYJhr7YktasCYVrJqp8Dki108PjwIrP8BItVJmZNHQVEl/3yaIryEcjyP1limnYRBdvJjo4sXU3HsfRc8/j/voEYf9GsP30QNf/B/44i6ThdL9RGJjb+Y/ZWnohsTeUMvoP17G1klnsX3cSYy882pK02PcdqGKVAQ3NUaxFNzJTHUHy4NvMmpFmGs/Mk96wAmrTjmXlN4FnLXhRr4suo4FeRcipTnVjekB3q2/gmL7WIqbv2WWo83iuaMmDe6jEebFnrikLfa9OxxxSb9tkkFbTMO9U2CqzgvLSwQrugpWFgtCLrOQolulpM92OOfr9oZOU2BrNmzMMw31xjyTjvabTzQcIdP7qPJ37DUI8OEIwVFbjdbS6z2hNNtkvKzs2sJ4keYM4cSlRoc4+7ShgpMWm/9rcsGtP1fxReCU+cYhNWeo99La2OJwYmeyUFNFu3jvhyNM1scVn+r/s3Zvhwul2dB1NwG1ijRTDsCiQV5CQLjt2DMHBKhd42VtnsDpz6bLympWdBGUj48wVkR4w+vhM09HSlx2o8kS2T15/MRkha8GmuEVVW8zlMctb0v4PnOiQnkmrWqFj05RuPZj8/OHIwSnLJCtn3c3wm3vstVIu2MdGU+HAt1qR9ps2N1OFLsdJVSOcDppTs1lfXgL/fKG4PdlodjtCLsd4bC3fHYg7DZUrxff1FNQOzlvB4IfRgjF0OH+Xqbx7n8WnP7vdvSd2kcepe7RR+k24zO2/+lmQguXcNOlLQU0LXBanES1KGPzx3JD09Hof7yHB09VOG9NKlmb6nHmKOSMt+H403ywmB5ETItx57d38sHmDzrdrXGrDK770OC+MxR+9b7B1/0FT5xsZjSEYXYyH1QqGVhqtFKsYlYzprmiq2B5iaAqFQprTQnY7hWSHhUmY2RnAUnM2lYmfPfZCquLO8b6MpskN7+ptyspL083Y+W7c2/3xon+fJDg3VEKNakCxZD02W7S8IZvMAWDdGFyvHdls8zrLQ5bU+Cd2JIDAacg6IKAy6yiL6mUdK9sz+QozTala9cVmDOEwVtkhwHvSOHzQYJv+pmqkAX1MHqNweTF8oDkCfYGXTGTaKIT+YDDAiFRbQZ6vGMGLuw3eGOYhSsTbnzeYhaHqlgZqW9nQHc1ou6Y2VPzSCBmNasuE5Y26dqd7wmL6PC/zGba9YCdMVhQnyXJk0kUVZKw2yhLz2Fzsna3dbWt58y+5/DroTfgse0iX/HWpbB9AbVXfslxbx7Hb4f9lov7XXxkDnoX/DBCKIoKv5gB034Hq96CmrUw9Z9QNBKp6zS99Rbu0aMJfvUpiXlLePkEhcp0wSnCxyeE0aVOVIty15i7OK37aTR/+CEVwOZcQc8/vYHz8bupeXkGpW9KQvHrmXNyIQuDq1nTsAbN2HOt+/ANkgYP9N9qPmyz+ysct8wwjfZWcMfMG2lbnpUvx9iZWxihzifoUm0a6yummRWHOw100GF61t/2EmzKg025pkfeY4fk9pd1piyUrOja8YG2aeAPm593JqN25Vnv2smnM+P97IlmAtZQYMR6ydWfdPRCgy7JunyTl7yrAd8f470lG+pSBJmWJIrdQLdJnE0KueutHZY1FEkGOjHNQlGNmbTdKZzU4IFlJYKwA05ZIJk52EykjVwnmbDCoE9LQ+B6r8mB35YlKKkyp/yHs+GFpsC4VXK/lfo6Q9xiUvN26kw3eKDRK/AIndPWJnFvte17JYcCKTo13gDuJoVLZhokCFLHKvIVyOpgPNv+bvCaHG9USbdqSXonIadotzj/6eUkYTHv9yunmTekIzVBMGjFqgmKTq7mk/XZ9G+RJxHn1NHoVim3WMyX1cJCh71V/XPPxyYZv9KcHRy7QvLxCMFTYxy7sGXq2LUp+O54fcMbvL7hDXLcOVzY50IKvAUUpBdTsPodMg1JnjuPFbUd2S3/TXy/PPCdkBLWfQzTboZAOQy5kKB9MuW//h3ZN1xL+aOPsjEb7rhARQqBQJDqSGVU3ihmbJ1BvjefZyc9S82bryH++ijXXKPyq2N/y5Kv/8p6xcPoWTFOXCppdsMLxyvM6Ss6bU461LAwNGJw/ONxVJuBEWn/IETcktysKM70BKrNIBGyEK23Equ3obWUzeuKpDRb0CMlgszQaMo2eCfXyafezqdcxy0zuGqawfsjBS8f2/HBc0clP/3GOKA+kDGnBI+Oo/bwjOd1fknZkCRDvzSNT22axHdKPRfl5QAwKBbn6aoatB02qhankAxbSOka4cETLAz/0kq/TXt+qMoyYcYQhfUFgoALnnhk/6guYTuEnLRjUmzvkyRvh4Ia+N9JGPyvsSHPLKcv3GXWFnKYM55/nKUSdLYZaEMRpOoG1zY2katppBgGKbrBVTlZZOsad9U2sMVq4ZhorNUsanGFmmVemkvb38/PH6/w6VCBFHDeVwanzZcYVomSFNQdHWVNL8kxL5htySIX1zM0qYM7E4Idxct3WFRmuFxM97hYZTf1VLoqThyeHKIY1FVt5YKvTMXPmhTTUVmyB/XSw4E/j/ozZ/U867Cu84cRQtkdiTDMugfmPcr2b9KJNroJpSQQ1WbVXk2qwKpYubDvhVw+4HK8Ni/zKuZxxQyzBPaEpQZXfGpwxXUqTR5Bim4wJPdoNkYqcGwo5xfTdbpXwapiwTMTFaozLaQl49S0VHbdovsYumwL8jN/6y5pKWBLj/JRmosxgThZ1YJ4s4WdI73mUUmmWUikW4mn2diaKbivazPXNDZxdVOAT5Se3Fxs8rgUCTmaToGWxGcYrFVTaLBqXPqJxvhVkg+OFqwqFngjkB8yyA0bRBJm7PlIVextyQanP0mRN0aON47Tn6RqSQqBsrYegAt6Cu47U6V/mcFtLbHN/Kk1HNM/l6KkRnNM5XfTk5RsUrF5NbwlESqwsL3GRUll+9nBu6PMhOdRm/ZP2GpjAQivRopLI8uZYHa2jf8UuDlqk+TCLw28UVOUyZ6W5Pg5AqOl47iSmsRo7DgT+C6g0W0271hbKFhTJNAVswVfvzIzn7Kn0vQvBwpmDFbYlAcIgSsmOXVTgpGbDfzlKq5ARyP2whUJCpQk/07tyFd+orKG0bGOQiV/TUvlPa+beWXl7GkolAY0lrmpnt9+vcu7CB4+VaF/meTqT0zVw5BL8tyFOtc9aT5nf7sA/iJq6KLte/pUblGZ4XYx3e1idYsxH6B6Gdywg4IqO13mpZBaGWJ+L8FzJyhk2pOMjsY4KhYn64xnqLa72B7czuwds5lfeXBNWnum9uTtU98+qN/uCT9MA96C5Opv2HTW5WAzIK7w1CSFGUcpnFh8IpcPuJzaaC1La5aytGYpq+pWEdfNAOXkRQaXzjC4/HqVFxoq6TLsapRJdzGzbCZvbXiLUDxAn2/KmfxpHbaE5KMRgrfHKG1TMCkprIWb39JbOdpxS5uyYNAlqC5wE/A3UZ1l543UkTRY05CGA6k7kIYTNDsZaW/g1+t4cksDVeFcXkiMwRtL4otppMQjDE2uJzvRSCDmQk3oqHrn10xTTE/pcDRODjhNWdOt2VCWJdiaJahIB10VDIzFSdN10gyDNF3Hk5AUf+Qmv9o8LxE7/PxGC/PnV1H2hVkYFXJLbjvPwogVvTh3wZrW7XQmFBVwHny1qM2bRGZpLCpUeKPEQVwoXP+pRuEOs2qyNEfQc4c8wMSopLOpdoPHZHpEbZDb0k4vu6ljpeX6fLNXakWaIGaD3lUGo1ce3PHtjmo/zO5vJrEvmWGQHoT3jxakhmHsaokjaSpJzhys8HU/QdRhHocnIrntVb0DO2UnA2p5V5MBtSHfvOajI1HuqGvAYxi4pGw3yH7kdvH7rAzeKq+kV7Kjnuvn+hBe0Y/jK2MwBaKWafHfMffbbPIb2l/4Z05UmLLQIKepvSLm5jx4/DzJS5XVpBodcxtJYSOmevFq7UtEt+9izNe0GPPB4TgnLc9nwNxadCGYfoyXN45qJqEqOAyDbiILj38qWdZeJGSILxseICHbsqIChR5JyQlaCnUlt/JG5e+4feQ9nNnr5P27YAeJH7QBr3von9Q+9hRgUujuOlehq8WD6slmc3MpEolFWOid1psh2UMYmDmQm2bdxNT5Bj/7wuD5K+P8XVNNRUFHx757Wn09Nff8neYPPgJA6d2DsN9OfOWq1pgzQMBnpaGb4Gu/Ro0fCrN7ojSHUBubsDWHsEcF3qjJifVFwBcx49R7UrqL2yDqhITDIOmQJJxQ6xKU+yzEDThvtnkz3/ozFYGZTD1m1b4pgXvCunxTs7wsy4zH7ho2StF1mlXTv/LqBrmaRqXFQlA1H2V/SPLkw23hjLN/b2HKAoOLPze443xlrz0Rd0XIAdKrY/VoWCqtWGLtvcTydJg1QKF/foB+aozz83O4fJrOicsk04YKihsNCivEXhXt9gfWISGiMZVgkw1vjYJFNzW65/UWzO2jsDmXTsNqiiHJaoKHnjDPRVnm/iX2InZzAD5kmQMhoThO0i0pdyuE6+z032J+FbfAnL6CqlTB5MUGvkhb2f4zExW2ZIuWvE1bDULManr/pkEXVKSZx+02DFTDQkBPw2epJdBSyHN6MITbMHgucRpeaTBcljKedeTJEDHdw9facI5hHc9lxVirZ/GzaV2YtGlhh8NIqmY1ZM7gANVLUnjoNEGoROPpqmrse3hegtKJkzgW0fFe225R+cztYrrbzVq7jcwmyfXTdHpuhapUH4vH2JjZu44de+h/uxNauBu/qU5yJssYEn8UT687GJt1Gv+ecvsBXKQDxw/WgFfWrmX7lDPxBiQRG/zmMpX6FPMGG6zBkOLjGTLgAvpnDMBlNaf57258l9vm3sZtG/vR/63lXHCTysyB15M2/PLW9cpkktiGDcRWrCC6YiXRBd+Q2LH3J1E4ncjoHp5ARaBadRSPDVnUG93rQrMniVsiRJUQL6mVBFwwRo8Qc0nqXAqrvC627qXCbWeV2q5odh0YjcoQ8NYYhaGbDLpWm/HBX2VV0aAq3JiVSd1+qDN2TyQ4PxDkrrQ0XrvH3J9fXqVy7myD3tsl1/zSwlMPau32K2qDb3sLKlPb6ziHnYLsBsmElQbjV8l2HV/uPkfhaH+QC4JBUgwDEoItMQcvhNNbaWmdaZnvL2pSTEndtKDZqNimm4nQeb0F8/qYoQjZidEGUHVJUa1Zzj52tbFPLZNAisHGHIWUAAfUk3J2P1O/vDTH3M/jlhut9E3Sk7hUg2RERYuoSGP/mSs3/1zlEr2Rk5IhEoogqKncGy+gy/YkA0vN8nowRbyWdxWsLwZ7dhzDCSFFMM/pBMCtOxAiQkio5mCyP5BmHcQfXu9oeL++MMKAaT7Qklx9pQWrEDxWXY3PMHAbBh5D4jYMdga/mqxZNNtyCFnTCFvSsMgkxaHlpMfbsvHbLRZe9Xl40efl6PWSS2YY+EPw2VGCV8crpKk6SQFhi5MYoNN+VuHBzl+qy4kMe4vH6x7EoqjcPuwxhnVJ2+/zfaD4YRrwaBNP/XUCY980QyKPn6ww8JIbGJc/ju6RAOrHv4XKZdB1PEy5HzJ6ENWiTH1nKjnuHP61aSh1jz/FOTerdEvrwTOTniHNkUbDCy9Qc/8/kfH954KtKYSevfuSWf0t/8hPY3jv4zlt2EWoXg+qbECNliG++is0lXX4rbT7mJ/Vjcst9XgUG8OCTaxw2Glo8XjdVg990/rTMxqkonIha5QURi+Nc+LS/WtptbJY8NZYhXWFpgHqWS65ZIZZXbcTm3JMVkBBvSnA9OExEgdQvYuSm9MwiO4h8+82DMKKwht/M+NH83sJCmsk5ZlmPNwXNmlmtSmQIzUGxBPUqyoLnKbQmDMu27FIDGHOplZ0FeQ0mh2KwIyZuuOml7svb3Vrltm8ZycXOmI39yuzGfp3YuR3snQaPObgMq+PGULY3WgLafYz7VYh6V5pcvW7VLNX6YA5fQRz+5q6L44EZvy6JYa9uyKjppjNgaHz7ju7wwB+faVKVZogNe7gwnAVk0Nh8sKSZEQlEVaJ1thp3LhvLrJukdhcOkGPZH6GjW6WOAXCSS71bA872RDMpKQugCtubrcm20DJTTKth4V1+Spdt53GA7bHuTRxI2vJpZ9lAz0tm+iilpGtVhNXzPvkK5eTL9wuuiaS5EbdzE0xp41DGhNc9KbSqUrlf45X+GRE5/efwzBwGxKPbG/Y7YagQc+kQnipscdIOBo7/NYZl5wz26R/Nrtg1nidwvwIJ0YiZOg6d6on8JnTT7O7GuHcjmjx8IvrerJRZGFN/ZbQ+tu576dDObFviwqqAJ/j8OVUfpgGfM6DLLzzYTxbrazr5WL8azNIc+4yCho6LHoWPr8TtCiMuZ4nU1N5eMW/+c/k/1D417up+Wwtd97iZrMKRb4inp74NLZFawlOn46amoqaloolNRU1LQ01NQ2Lx4Ea3oj+wnlsn5NGvM5kWjxzBszuoXBlUzP/SkvlX7auHN9QA/UbTRlcAMUKRhKJmWy5Xj2NrQ6dmLMaxVHVemOIpJdJsTq6Ri08GrqWiHRj83+L1b+Y4voof/uP1tojM6moWI32lmNNIXwxUGVhQR5BuwOLu7TDqRNSMmGFKVG7q2e8MyY9r7dgw7Ex5nsc++WF78ROA74Trx6j8O6Yzh86ISVDNknO/qZj44akzSz8oBMvsiwTNhSI1i4sDV7BtR/qrUVRUZsZiskMmE17l3Q3udpLu5l9RjuT3W1yw7e9TKO9rgCk0pbnyAiYnrVprM0K0p06NEmrhDQNryZJhNV2TTnMcIsZn+5eKVuN9k6eftwqkdka2wskb/awsTxPxS8NGtW2dSiGJLMJzi2Lkr3SRve9VMBKIdlaKNmQoVKRJrB7kwy0RyjZ7MW6VBCzwL+nCBZ0c3FafYILn+kYa4tbYG2hwBWXZAXM0Nju8X9hMZBa59c0kmKQ0TVMTl4Um1dDCIhJKytkCUuN7iwxerDM6M5wdSVzenzCz5sDXN/YzH98Xu5PT+XJymqeDmTy23cMFneDoZvbr39WHyePn5ogEelFMjAQocQRSgyv2kiGWotPbcCpBKmwa+2cj/1B10pTIKtblals+cwkpbWaekQ0RpeERhg7a+x2Sh0GhRE3m2ODsKXNJVx6LUassN360tw2lvzpxAPahz3hh2XAQzUw83Y2VZQSfWQrCRvk/noYeWmppnSjoprvQjHfI/Ww9EXqFYUphXmMEC4eyhhL9dNvUbnJw+xrFYb3PZvryt6j2J5G79IRNAdUNFQMRMu7goaKjsJRYiM3Wd9g9vIMMtfakBaJ0ASruksenWihPkXwxbZyMnXTID+vj+cNa2+2O+Lorkqczs3ELebDI3UbeqwQPVoEgD3jS2LVUxja5OfKlH/xiDefDe4EUqpogf7k1BZw1+Y3WZnq5IO8DCpy6hFCMnU+VLgzWJXVnVQ1gcdaRa2nhkarKb6fbPEiuycSPF5Vy1U5mTQpKlfVNFO5xsfkjmFI1hXAP85UCe0iA/pkZTUrHHYeSfVzbiDIW14P2i4e6k4DvrOV2N1nmxobO1ukZTXBoNI9hxh26kbX+KHeD0EfhFMMlmRayW40S7cbvPCX81SOUqNM3JCk8DMPqtF2H+/03uf0FSzoaSburJrk2OWSyz5rP02f20cwY4hZiCMVgS8s2xnrbpWydYDTFNOr35wrCGbqjIvF0Bot+EutuEOCpGomFQvqTXrexnxBvzJJcY1EIIhZTV56Q57OzG5WtuTS2sZtTCSKS0pmuF10hjPr7LydEWdkOMatHyWIbel8ub1hYy64suMMtEWJbXMQrnLw1YQhbPHmYItNY+xys21ezGrGymcOVrD7k9xa3kSPprbQTDKi7PJZ3SOPfCd0m2BNcVfm+AawxZlHrdNPo8ND/+5/REgr1WXX8xPbDF7quhKvYXD6TJi0wiB4XgPX5hRQUCX524vtkxrVKXDLhPOoEkOxKOB0NYJzMzi2mC9LEwB23UKvmGBSrIYB8Tj5mk5UCEKKIKwohBSFFUohq0QBW5VUanEycc1Wzp1fisWQvD1a4YORexfrMnfoIogMav1TAA+eN4Rje2Ud8HXqDD8sA95YBq9dwIwFVRTMs2IbHaBb0R64VLvg7vRU3vR6eHdHJV2TGlVLfJSXefj2kjC/bGpmrsPBr7Iz6JLUeLqqpr3o+24IRixs/SiLlf0NzutVRf16D7WrvcQUk6li7RPBLiTLHTbW2WytRq4omWRQLMHgeJxB8TjdE0mQghg2dBR+np/KRruFNM2gwaJgkZKuySQZCQs7rIJtdnM9dsNgYDzBsFicYbEYqbrBly4n0zwuNtlsqFJydDTGSeEIx4cjeKXkC5eTmzIzKNKS/Ko+yG+z0xgZi/G32jp+6ing4plGu+o1MEuv/3b23kW5hGF6qdlNkttePbAKyG0Z8Mlwhe0ZZhFIwAU3NDYREwrNisJHHheBXTzSnuWSW9/QEXLPbJs7zjfFjCyaKVcwap1k/Kr2x/X+SMHMQQoZwTZD3a2yTRfcAMozTGO981WWZWqrj15rMGqdyTjZWZK/U8r3xGVt20lYYH2+YHWx+dqUCz21JFEh2Grb+/T6zR2V+AyDSYX5rf8bHopx+3tJwtudpPUKkTY4wAybi48a0xi91owj70lrfGEPQW6DJKexPUtGsRrYvBpais6rBS7sCehbJimpNmdiW7Lh85ZCqT8313NSONKhCMzQIRC1cJU3hwl1cXqUQ/Y6C+o+qkeDToXaFIOI6MImZwHBnE3UZdZww/sGtpQkJZNqKbU4OS1rEDgr+Nvrgm5bOrJcnjrbz4xu5vOf7khnaPZQhuUMY2j2ULr7u6MIBQwD6jfB9vlQvgBWvg3JcId17UQyolC9NIXgdiciRWPZ+ARv9HCwsUXyuUT1MKaphlGnPkOKM41+6f1Q964ne0j4YRnwFoQXLyY8dxaZZ52ACFSYRT3NOyCwo+W9HAIVoCfYarFwekEuZwZD/LHenGtXLkph2w43Sy4OcfXAq8CbzVw9wHXrn6drPLZXIz5reQapG2yEz2jC6dJYbrezOWGn21w7IzZIytPhmUkKzqwWYx1LMDAeJ20P61tts/Kaz8t73s67zjsNg8HxOMOicYbF4vSPx2lSVaa7XUxzu1jpMGlSQ2IxTgpFmBiOkN7JthY47FyXnUmqbnBcJMKLKT5uqW/gQ4+b1TYbR20yu4x7dnF4ml3wr9MUAi5BTpNsbWy7a4PbzgxHKD+JPyDQgm1T2VofTB9qUtp2NhBQpWyVCN2JLE1r5dvvxJ8rGjhxvU71Ul87r+/NMYJZAxSidnjqQVNTel2hYPgGs6Q95DBZDaktz+u83oLCWklefRvnvNrfZqw35QpKsyFmF61U0dFrDUatbdOuMYRJd4w4TB1tVZrbsLZEZ/58gcrGPA5Y2rQ4meSKpgBpuo7PMHgg1c8ipwNXzGy71m8bZA1uJr13m/HRgcFdi3BHzeYUu+vFWxw6vi5RanokmZZvp2S6i36l8PkQyNd0SuoMXM0CLbLnkMNOr/yzIQq9vRH+UteAQ8rWsCDA6fk55Gg6j1ebyf6tFgsznS5W63lkrg1y3HLZaYJdIlFdbozIbl8KicWtohR3Z4laTZMWYOwaiS5MKd3dK2CVycfS/Z4HUFsogx03JE17ULkMKpZB6SzToO8FoQo7VYv9JMMqKV0jRIeF+WzsuXzWtJZNsRoEgqHZQ5nUZRInFJ9AhjNjr+s7WPwgDfj+4Kt1Vfz+xS+Rua8QdZdzSulQ7lLeBGDHghS21rhZc0GIy5vbsoFznA5+lZVJSTLJ01U1JuOhBVWqyiLNSfGbPj4fLHh+oorW8ox6DIOQojBio8FFM0395IquNox+aWhOFYmgq6iij2JmxBPAdLeL13xeVjg63nRHR2OMjkYZFovTJ57ACjQpCjPcTqa53Sxy2JFCkBuz0j2YSq+QD69mQyKQgGyJXZ6szidftGWFVtpsXJ2TiVWCXUqTOiXNhyu7EfLrzcy8o6Oz04qQwzR6NX7R1irLDyPXSSYtlWAxsNgMtIiFqF0yu6/CVwM70u/SdJ0XK6qpsKhcnpvdYTsWzeyMPnaNZMRGA4smSHoMlhcJpvdXWV4iUA0zKTh6rewgslXnM5OXu/abbHTvaqxhS67o0C0mr14yeo1k9Fpjj23fNAU25ZmaNquKBRG74J7/6DwzUWH60MNX6ZcWMGcdefXw6FSFOf0UJobCpBsOGi296BVbxJN+H6mNgl+9r9M16qPut1dizS3EqQsUfddjM2PaUpVEXBATAoOWKLcE1QCbIfFqEgyQUnRgszR6zGMHyNB1bC02pElViQlBTicFN5oQxIQgggXFMFp1VNSdj5aQoCroukSVIC0GumpBSgG6jiJBMfZW+N4G1ZcCFrXlNtMRMgla0uyUI3fmPwSoFlOvu/VlNfNVWtzsqJOMtp4xI6FgaApgascoFjMsGbM6iWK0Sm3YVTsOiwOHxYEqDtwjdzgcFBQUYLW2n6H9MLRQDgLPzd1Gta0Rt3cr8doTsesxUOCSxE2MTC6nt7oKBfMGLDWyeU6fjC2pMUUv4728bYwtLuDCxjg1Vp3lDhvVFgtXTNMpEpL3RwpObpKMiIcYE2/mc2sud+VBjX0Sf5lQzPjKVzh9UYBEeT0v9j6ZD7uOxmiZZglrLd4ujyMtpteRousMjcUZGotjAPenp3JCOMK5wRCz9QFY1ZWsU7ycVZiFUEzL6g4V8FLjQhbFxnCLdgWf7HLcmTTxmO1fDFc2tP5P6pCMqHQNCZ5ZF2C64SUlIMhu1Mhuop3BNmivnbITX/cTPHuiwmAZo9xqoWw33qyuSiYt1UFTWFCkMmugYFEPQdIiODUYwkjaKd0lfNCgqkwpzOOoXSr8hCHpu11y3rIkXTcpWBOCgBM+H6jwTT+FDfkqQprViFdOM/aqReKMQ9DZ9vdV16q4HRrbbR1v/ewGcxAYtW4POutC4kxLmt1XshN80cXK8xletrWcg59+rWNgJkMPFwpqJbe+bjZu/us5Cqu6mJazTRVwE586Uzh+meTnM3XiVlh/97UU9RiJLUWiKBoWXZIZlVhjFixaWwJXsRiodknUQesx7IRdSvI1zfSyJehSQdNVkJIGp2yXaDUAp26lOzFqVJW8BKSw52KEBBBUFAKqQsJQcCbMpLAz3lYTkbCYA2/MbiFpdaIIByoOXMlGfPVmuKTJ58GiS9DD2HQFW7Kz2a0VcIAdpKpgqBaSqo2kakNXreiqhdQUFy6HDdEZRVRPmhXf8SBGsJ5kWMXQBIpFYnXrKKoEi4OYzUVACAJGnLhuHrvT6sRn9+G3+80wzj4gpaS+vp7y8nK6dt0/DfHvtQFPaAZvLt6OYez5AT6udyaVri8IaGn8ZtCZnP/t2Wz3j2HckAvosXUDiTDsyBjPstQccspfYJTzQ2ZkD2e1xYVsYRq8lGpH6nYGRFQubqhk8Ao3cwbYuaSmO7minhNVMyPX4AwiZAqvGc/idkgC3QQ39cnl2FlJrlr5Ptdtf5u8YY24MpLUqgpPRFIoSSQ5Kpqgp5Zonc7P1fvQyxXmKX8KZwRDjFFWAdDNCHJBk5eXnEUozgrCnnLOcxRyfGwlp4YeJxLqy23Jd8iMNJEMqyRCFiqDKSTCFpIhM+HELnHJ8Rao9kuq/YJVxW1c7OpUQW0KJFum/913SP76gvngj1stiVsNnjzJiWOXmclH2yvI1HVGFRUA8MVAwb+ntPdAFlnSOSFa286A78QSu52SSsnVS2KUbFTQoypRm8qCHjCrn8LKLgJDwJg1khvfMVrDIbvCwIxH925halx/hUpVGlw23aCoRnLTL1Ry7AnKLG3bz280GLreDJHszoRBSBypSVxZCdxZcZyZCTa7LLzk8/ChJ4WIotAnnuCO2noGxBI0rk5nTZHarqnBrshPatQYGSTtTRwVi3FrXSM9kkkiQvA6I9lEOmOcX/L77HQAem2X3PyWDorkm5/Eacx3sLsMmSciuWqaKeW6oovg0akKt2XmYfcnWg2SpgoqPQK73SC/3gz/6BawJhWzT3gEsuySoBMsVklQUYgLwRarFZuUpBgGUndhFwZWNNI1nTw9QVAItrV4ilE1SbSlkL7JIvFq5p42STcKEjsJ7ML0UlXMAcJtSCIqBJ2idZAtqTKfZUNpEWYLa0hCxC1xwtY4TRYXqe5m9LBKXAmRsCu4pKTcIknTDbI1nbhhJW7YSBoquqEiDVANHauhY9F1HEYIx64nsQ7iigJWK2Lny2JFWC0Iqw1hdSC8XhR/ITbDQK+vQautJ94ssDh0SLGgxZpRkDgVgaao6EA4GSacDGNVrHht3k7viXa3mxCkp6dTW7v/ko4HbcCFEIXAC0A25izjSSnlgwe7vv1FIJaksilGLKlz2qNz9rm8xbsSZ8F6YpVn4Fj3D4Qa5eKK09myYyV/qa0kW4U3jRW8ntyGUmiKLdmSaygOCXyRgdSpNmz+RZBI4576NSxdkoYhkjxTfAG1Wl8AZoibqJF+HrHmYE/U8XRyGCcoS+hHGffFK7n6tCy+2GrluhkKZTMzSekaJmtwkD/qZkA1rHjZ7uxOcdQsMR+trmWW8ype4RPu6HouZ4fCDKj9GCsGtzRV8NvKKurCVjYknFRFbcRDdvzNm8lu2kw4aiVMZuvxq3Ydq0dHZmpUpuq8k+dsNdRNns4LU/KSGlOiMUbEYgyPxnEbBg9d46d+s5vLpxucsExi0XVemaBwtBJllstJk6qwzGHHUMyKRbcuuT5o0Dtcx9U5WXRJJPGJOC/72t/IufWSsWsMxqw248u6YmFzF8m7AxXW5wuKaiUnL5Tc+sYechH9TY2QzbmC7Zkmq2PCCoNrPjZIDUkcScHxSyXThgnKMwV5SRVHSDBhncFPVyRw7SLgZQC1mZKFXRQ2FkEvf4Tz4kFSNZ0Zbhdv+DJZ4nBgMySTw2HODoaICsErPi/PyFTuazSYN2LP3rdZ5ddEflLjztoGilpCDR4p+QXzKLVauDI9C7dh8OiCZhyzPdSkCO46W2W8Knmtoop5Tge/y8pgYihMzzJB/y9s+CKSyLAoI3uF6BNUqEUghCC1pcFAXBFEhEJ6QKIrsC3TFJGy6OCJmfx8T8tLVwQeh0G9SzG9S8VKrdBAjWNV7LgtGTgtXmLCgjA0ekYqkMkAOywWIi01AiFFsNZmI1XXSZFxHIZGRAiaFJWwIoiJnXPeNmRrGvXCvBZ1Pgi4BEKCNy7xxQ0ciTiOaIJ0Ahgt3mxmELZmSnQp8KLSoILhzMZjTUNgtnzYHQaQkBKha6BpCC2JHQOhachkEplMYsRiyM50VxQVrGanIMNhxRpJoMVUtGScWp9C1C6wYHLS7VJilxKHlDgPIErd6SxgLzgUD1wDfiOlXCKE8AKLhRAzpJRr9vXDQ8HA2z87gKV17Fmfosez6B52kuFdwGW2QVQ6PsDj2IGyKobWEjbQY4UkGsaiRwsZn9zGbZZXyeBjXtJP4N7guaj5r/NHt4ebNiaZ3j+NWsU03l1EJT2UHThkHL8zwHGRMNdblgNQI/2s0YsZtqOAN4sruOTKes6a3o2pazZStSOVpf16Ei1xMEjZwgh9fWuAz9DhhmVPE7OlYwvOJrM8xI6Qn0RIJRm2IFtimllAlpBYXRoJn0FVF8HidIVlGVYq0gQ1fojaD/wSv1BZTbbenit9cjjMJUM8fN1P5cy5BlMWSEau0/H2jzPnBAcXtigNgtmcYkJtnC519bzbMtV/pLqWYk0jJARLdQelO7ykb7ZQUmU+VJtzTWNc5xPkNsIN73VusMvy4fNRKp93gaS185t9Th/BRZ+bbe38IYOgC94cp9B9h+TSz6H7jp3HZmF7JqwrgoH+MON8AfrZJblWK8+n+PiP18d/aJNWKEwm+W19IxPDEWa7nPw5I43NLayEsxfrGAIWtIRPHqquZWwkynteN3/JSG+3fzusFqYU5tEvHuecQIhJ4QibrVauyclEAZ79shmxwIMzPclR4xuYpPh4xetlhtvFdY3NnNUUwrHEyTHzJY2pklsvtrA1xwf46B+Ps7OeeNcwhztu6qLU+tomYZoKTW5BkxvSdRVvOIEaFzgjgoKIJGkBh98LqakEjDDN8WaaErU0JWpxW9347D5EejFWPUnX5nK0RIj1tjaz2aiqmC5K2/+cqo10YcEVC+AyDBItnn5SteBskV32KzoBLHikgWGDHQ7T4Ds0SWpc4oyDSIKiQ2FMY4dDJYGGRUJTog5vrB6furMfpa19nFuxtORg9izTqxs6cS1GPB5Bi0fRk3FkMomimR68JdE+tGjRIbcRLClJrGoLb97iAJsDLE6wHjjlc39x0AZcSlkJVLZ8Dgoh1gL5wBE14H+a2pfXFmzDYVWxW5QO7/Zd/t4cn863zfWowkZ595e5gQwUGimwp5HnOJlsMYeIUsU5tnHkH3VTu+18ljyfozY/xoXbX+cc5vNtTYKVS9IxhEQ7+3L+nGsa8PQAJBc6CLrcBFSJzzeGzwon0+DtRdSegTCS5MdruCmylRean+eVUzdTODyPYR9UMXrpKlgK3oIo9VY3yZAZ9tCiCiA4t2VfGixunB4Duy+BJy+Oza1h9ejYPBpWt45QTCMobFa2OJ1UeRTK9kFTmxCOMC4aY5vVwvMpPl7bUckDaanMdzq4NTOdJ6tqUIEtVgsPpvr5wu0iU9O4JtjMT7qE+Wf3dPK+dTB0qYf7t+o8f7zC2LQAX7sd4FbQKkzjscFmxWkY5IV1Gre7qN3uIqPaxq65+ogDulVCj8qOrkrYDi8fa7JWOut6vissUnJULI5dSr4cZOPU+eb6Hj9ZIeIQFNcaWJIw/Sgz6bimyExeliSS2MISa9xOv3iCaotKs9oxZlkmM7nHk8+9/maEukuVrpSMWqOwPl+l2W3wj5o6MjWdn+Vls8puZ2g0xq31jWTrGh94PDySaoZfVtvt3JZp57bM9Nb1PDQthljupakoyYyJSWocfupUFYmZJHxGpPKrd3RKqiWfDRG8cLxKYpeBbJXdjrXF+4sLgYKkKKmhBFSECoWqhkiY3tcWm621SDwuknidBs7UVAxhJx4MQziKXtcAdQ34szPJSCsiLjWaE0Ga481UhiqpElU4VAdWmx3FooIWZXjxcBaWtRUWvPfqe6xetpo/3PMHFMWK3e7H5c5BjTXjCFaiIGkQgqw4CCFxCwNLS2K0WNPQgJCiEFAUqtwK0m1ea3/SwCMkXZMa2y0WEi3ea7li0CURxNUJQUMiOP7sq3jvpX/j9qdhSS3mp2efzkNPP0jc0AnFw4zrO44BRw3gsVceQ0FgtynYrQKrAXZdwy6laTilqbIoDQHONBSvD6xOc6DoxJM+99xzufPOO+nRo8de7+MDwWGJgQshugBDgA6cHCHEFcAVAEVFRYe8rV+M7covxrYP8L/0bRl/fG9Vh2VtmZuwpvjIjLm4OLGWr8OTmRmZSrO0shE4vWkuugOWbgvw9PLOxp1T6CcG8DG3MrDZQvoKyfShghfqXieyxAuGAxtJHhB347IuAuYQKosTSrxKiawhN1aPPxRCC6kkwypDQhZKYw48gXKMXZ7/YLkZAKzwprM+o4hydyZjvKs4NmU553Tryg63hRu3ZXO++hXKLvoS9YrCXKeLuS4nn7pd7Qpq9oV5TgcJIZjrMrfdN5Hk0eoabsrM4Eu3iyf9PupUlbe9HhxS8svGJi5qDlJhsXB7Rhrve91QCIM2G/x2epJb3oKyYhfdxmlU+1T6bFIxkgKt1ME98ww2NeXucV887Ws0WNhDMK+3YHEPQdS+92M6PhxhSCzOUbE4vROJVk2M6/tmwXzz9l7UpQCo4PPBCp8PbjPMHsNgaijCWpuVR1P9HdZt11XuqKvjG9mbj3KqUWxN7b6XUkVrHkR+aVfyG1/ng4H5QAXvO7OYk2mQrutcVxdmZehYltq+xWFvJC4EPwmG+dTjapVKAFNL5YppBjkrrcwcLHh6kgNDuMjWk2TqOseEo4xYIRg4x0rMCv84U2FRT/NYXtlRxYBEgk/cLu5KTyMpBGm6TlII6lQVI66g6AKrRyOACyElUhjoyNYYd4OqsFWx4tICZGo6blVSn2YhZggKwwYiUgnJCuyAH4FFUWhQFeJAVIuyu6qBTxfk6XHqhR1lF+7IzriwgsRnGPiFwGlIworAlQDFKlEE6LqHoCXMClkEUkUY4CBOioiiKFESqka9TaEOFSEFqmEB1RyOJIKtViuFCYlT6ijC9PTjQvDhzK8p6deDareFhBbE6XKyYvUaKppqSHHYWfL5bPJysnBKSY+W+6l17+0+cHlMr7ql47yoXg12D6Tu27ZdffXV/OMf/+Cpp57a57L7i0M24EIID/A28GspZQdlDinlk8CTYNIID3V7nWFdVeeCIInayVhqJ/CK/TdUyUxuT5zGrkkgi6ETVwVS7pnuUyrN0MDSjamkq5DSJ4zdsZ1exX/m3u1BCkIhkiEL06s9VK6wcUrlImIhO0ZEEDLshDDpgbpQqHWnsMOdTnXPemoym9khR9OgDeLEbQs5sWwhrniMxZm9+LxwKPOUAUyxL+LG5HZ+Zc/E5y8jFLTyrq0Lda465rts7ZrG7g2iJRZnkxIJBFSVuKK0Gm+AgV3b34CP7WLQTg6GWNxSfbkTXt3kpX/dzcmtF7uZtMDJ2KWNnP2SuU8SWP92Lmfsti+risyOP1lNZlWmgsl2WdMFZvdVWNijTfJ0b1Cl5IQAuMMlbIsUcLH6Tut3r2sT+EYbwFWYmsyPvr6D68+jlXfu1Q3ura1jdDTGp9YcNvkywNb+HlIMQVzVuSU7FditsWQLhNARtgbGVzagC1g0eAcgmOM159f1FpWHM9yQsYCvUADT01Z1C6m6BNUM5dgTZsf2o7ZI3hir8NZYs4HI0dEopwdDTGiM0TA/hdAOJyJbUjiylt7ZHha1hHfOz8/h8207ODkc4ahYnE39JZUWC27D4KnP6ymvSCDApL5hISEtoCQRSBSp4iCJhkocBSF0dlINhVSQQjefGKkghcnZLsmycsVx6QjAYQgcBggkmoBgy6MUUCUqKilGghwtyVbDwGMYXP+rPzF+4ngmnjqRJkWlR9eRLCxbyJLZC7jqr4+Smupl1YaNnHnKiWT0782rT7xMIhrnuWeeIKNLV6bP+IIHH3yYRDJJaqqfex59kNTsFB68/wEqyysoLyunakcVF15xIRdecSFS2lolKgBefXca5190Fo6WgUsBTj1uDJunfcFPp57AHW9/wkWnTeLr+UuwAeFIlOv+eA+r1m8mmdS4/TdXcdqUSWzdUcNF19xEOGTqEj/yyCOMHnsMX83+htvvuIOMjAxWrVrF0KFDeemllxBCMG7cOH7+85+jaRqWAyzz3xMOaS1CCCum8X5ZSvnOvpY/UrjrJwO46ycDOv9y5h3wTSM5l71JaeHwdl+tP/UhFmlwyQAn73RXoGm7KTbVvB2atiEbt6M3NlBV5iJ3nenbnfw1DI/EiYQtJMMuSjHjW/2AEqeCLDkKz+AibIWFWIsKsRWaL0tODqLF44rrca7/8nqW75jDn0aeyNm9nucfD79P91ce57dLXuPX296kx7BKsGNWagJ/yEzHkW4QU2Ko0t2u8MVuGAyLxRkeizEiGsejw72OITQLO2Mty0i2eB9xIVo/J4Rgqd2+Xzonb/o6ZtCDqsIKw8HQjQbdK+IcszjWKW+8LBM+GqGQtECXalOwKqephUNdZGVFT/i4v0HYuWejfXttPePCcZ5NnM4am5N8zyIaXbXM8jqIpVQhZCUrEzmMbEm81oRcfJl4ktvOSOfKd8BRr3PvM/DwKQrLuym8VlHFP2zDuTo3iXTUIPU4pzRrnB2uo0FVudNfQL19/4TMLM5Sxm7TWVXcxiWXyRSS4R5IzcsJxnp+IpeTqetk6Dovx07l39pZxDI/xZoxiym1Ea54B+KNVgKjI9QMtYEwB9b5TgeRKhupnxn4IhI5LES/bgEUAf1qe1PcMIytRZ8i1ATHF+XzQkUVQ+IJGnSdbE2j2qKaXdYlJKwCgQVDKiB0BAZIC3aSGAji0ry/pVTN74WGFOYAYwAIky0uEUjDjpHIwmqAlQRWEcdFHAcJFN0gHotz5oQzW89Rc1Mzx088nqSWimpYUYyO95w9CSs3bGD1F2+RkZ5CyehTONNh583PXuHDx17gleee5MG/3MRpI7rxs4+eQROCJ195l5cee5A//OUmVCkp3VTKc+8+RzgUZuqoqZxzyTlYrVYydB1HS1hp1fwlnPq33+PdJUl54dln85f7HuGUc37Oio3buPSKq/h6yTpIKeTuf97Jcccfz7OPP0RTYz0jTjyDE8aPIstnZcbLD+Fw2Nm4ZRvnXXsdi6a9DPWbWbpkCavXrCEvL48xY8YwZ84cxo4di6IodO/eneXLlzN06ND9ur/2ef8d7A+FmS59BlgrpfznYdmbw42GLTDvESgaBaEqmPcYNG1rMdBlyNoqSLNh//ZDQh+8YyYJI3YSMQ/JsIVEkxOZyGm3ynCsBH+On6S+idfTdaxpdq6YfBs/3Xg3Jw36KTePuHmfu2VX7Tx07EPc8NUN3PntnehS53fXnYe89hRqXnmOuvv/yebpWcw+2sazo9uSiSqCf4UVjq4tozK9K1/3GMOQssX0ry/HevmX5lTu87/Aspd5LLzvbiJPpfh4KM3Pn+J2XlQjbLWoCCkZHI+z1NGOaIUrJrl5fZB+FQbNzblYasM4Q50nGhf2EDS74asBCkM2G5w+zyCvwWyGvKpY8N4oU6Mk5No5IWtvvC9oDlJutTCrZYaw3mbjJ6Ewt1jfJGQ42Nycx6BgLUlgpd3OfKed+Q4HL6Z4ec7vwyJXsiiewjqL4Cd+iS0JzW74wxsG746SnHJMLobSpgrlJMlHKRY+Stl5rTsabz1agKH5EEoUxdqEsDYhhKlCmNtolubvRDLUi2TTMIx4LuV0Z6L929bvNhtFdMl9mnr/Zn62Pczp76kkoioFYxvw5sc5utpsEfaOy4Nc6ubEhaYG+l/PtlCW7ack4ea0UIhTQguYqn+L3AY3ZmUw0+3iZ3k55DcW8zuZRqFup5se4/bBaUTsZqFVmq5j1e1U2zSk4aAkGcOGxiaRRVIYCCUOSrydx7orvJoFh27BRQKnrEBVzOUMKYhhpRk3UWnH7nDw9pfvkptQkWqcx9+Yxprly4jbmtEUDQ0rRiIDobTFzuwaHNW/P3m5mZTLTAqLu3LSMaNICEFW/358NG85q9QUNtaUc++1t1JXXUsykSS/KJ+wEKhIJh8/jq6qgs3nISsjjfraenLychCAzzAQQENTAK/XawavWzCwJIutpZt59dlHOfmYYRCohGQEmrfz2YwZfPDRx9z34GMAxOIJtlU1kJebyy//r73zDpObutr470qaPrO9ete9d+NewNiAqaZDgBDAQICEEFpCCCmULya00AIEAoQYQi8BXDBgG2NjY9x77/aut9fpM5Lu94dmm3fdqHaY93n0SLqSrs7cmXl1dO4pd97DqrUbUBWFLTv3gC8fnKkMHzqYwkLLnXbQoEHs2rWL448/HoCcnBz27dv3wxM4MAa4AlgrhFiVaPuDlPKjA1/y7UBKyZtL91Jef3At6aSt99PfiMGeRdYCxBQXfmc+9Y58dGoYvN2E7Sk05FYybHbCmbmEcvJRuil0rJpDcLOHTd3TKbrxBUybZSJQTJ2Mkkd5SX7FBzv/TMQuKCnJ4MnZW5vkRDJjTQmldRFcdhW3XcVl13DbVeyqghSX4dPq+Oviv/LwV8/gkHmExDY81wt++rnKKV/FGLTOxdoxg5jZdyGmEIwr38vzab9lof0kzD0qO+NDOS56Czw9hBg27LShBh8AzybKZv3FESVXdzMh2JscfTWzHBo9iiRdilx0LdboVh6moDYKuDEBHy3L2azrIJgzyArYyapvCjs/ZZXlmbG+g2DacMGSnq0jHtvCR+5Uwno6YIVAvpHq441UH2P2DuZMfRsThTXfYQMGR6MMjkb5JfWEhGCV08FXTiefu3yENMGnx5lcMdfk4YtUTlptcv4iSa+9Bk+c7abK40Kx1RFRWxPW8X7JebVwQ+ABWpeANrGlLcGR8wmjNgUwhFVGrgH29CXY05cA4I5GuT+azoBolB6xOK6c16jyuPnNZj+jZ9gxJXQYX4k7y/re7o1fSZfqYs5auppoDfh7RXnnZJXdKdZfdYfdxuMZ6TyZnsbx4Qjn+QM8VF7Js+mpvJiWSnH6brD5KVLySPc7SJVBcJuAatncVUvzzJZBKm2CgGJDUo1CQvs2HSimhssEDzouolTadIKKgl/TCatxpFQAH4biQlddGKoDhEAADoQ1fydMNKePlGiQDHsabi2FNHsODpsdCKHYK9GwoccteewxUJ02NjjsKMKP1MCfeIDHNRPDjGBTwzz0x7/wixt+xjmnnsiShUt54NHn6GFI0oSG15NKmq8daC7sdhedfZ2xO+xUROuIuNIo8Bag2eyYuf1RhLAi24QC2b0557wL+O3kv/P5x9OoqqqyJiN97ZBC471Xn6Vn106JGUsDTJN7H/47uZnprJ7zLqZh4Ow8HHx54M3G4W5Kh6GqKnozbT8SieByNYss+4b4Jl4oCzi8yNZvHf/5ajd3f7j+kOfNEYMZprgpltkUySyKZRY1+CBkiX1pn3fxysXo9GSpchKlnkyqnT5kws/0X7ZH2FjpIleDyb0vpWrerv3ucD5ZvjwihR8CMHHto7wUquZLs18rWfzRpi9RqAFUz1Y07xY0z1aEArqoQxd1xKrGEgz04NEuHdmb/gVXrfqYEz5ZxKC18KeJGrM8bnZV1BE3F3GP7RX6KE35xe3E+dwYSBAHdnQGKdvIFq3nB3bYNB7KSCcuBKohyd06hB5VVXSuXUm3UoOzK0CRBhCgymOjMsWkNgZpzVJVbGgPC/oqLO4p8IZh9EbJ/a8YdKiwXrk3doCZQxWW9BTUeVr+THyGyeX1fsaHQuTrBmWayoMZ6Sx3OflpnR+/olCqhdhg2gk2yz++sP0KFgL3yPbk6AZ5hk6ubpCnG+QaOnmJ7TOCQTY47OzEydwBgku+gPFrTF44Q2VDB6sO6iNTQjx9doRVXZv6N+v7EKkZxfBIhGftD3BH/Hr2J2/FuQdn3oeormL0QGdGrilndYFK2FXHtF1VPBT/KTXZS1mfVg3AeoeD9Q4Hb9Jkhhq4w+S4qW4iTpO8cZXsduczOXY5C41+nLHrK85dN48KNY0NozpzdcdZDK+CHfUa7/i8fOj14lcVTCGY73Yx3+1C0+108WfSLlrOPodh5RYR9aTGJHGHQhAvihHHVJuiIytVNTGBqeMxJZqpIKTAiR9VWDHrZiLJmjfuImqPWikjpEqlYlItYqQ5PKQ73CiiJY2IhvqvdhWi4BAGNlUl251Jn2592L1xD/YL7cycNpN4PI49EfTTELFvShNTmshm3uIeU9IzFiNaV0/vgs543fm88cHnCJsHkdcf3Jng9FqFjxNSKEKhwFuAS3NRFixjR90Ouvfozo4dO+jWrRs0yG1zcs11vyAtM4f+w8bw+eefW54kvlxOO3MiT015h6eeegohBCtXruS4446jTrdR2K0/SruBvPzvf2Ps53Z7IGzZsoV+/Vrzw9fFMRmJecXIjgghqKg/VN2sbsBp9AB6tHFUrRnAi5Fl/MQ+iJMzT21xLCu4jYEL11G2NZsFIwu4/JTTDniP10pnE5NB/pWr8eK+B9ibOYF5nW4lYM/iveVF7KsLoLr3oHoswlZd1uu7qXvQg90xgt3Q0pajuXchDTdDw3Futz3AiLxNFE/IYPaWwfRZv4O//SvCZ0Mz+EvhiyzQBlIoKqiUKQSlk46KFfs9TrV80P3ShU80+QbcEruRGfSjm/0DutRspMdGkzP36XQqB7tuaYsBp2BrtpcVw2BfTojMeui726TvHgNVwo4sFxv6hJjRX0PTYfQmyZ8TdRVNYFN7q67h4l6C2v2iEYeEI/QMunm+9jcE4un8yvkzAO6IX8/U0GiOdz4NrnK+LLmVdqKGvqKIl2zvssFu45KClh4spwRDqECZqrLW4WCWRz2gB07ALVjYWzB2neS/Jxos7KuyI09w2wcGf3jb5IORkg796qivH8FmvQvbpcqttvepkClMNUY39iPUAPacj7GnLUPGfeQVj+P0ogD5wa3MOgFODMXoJIM8q70ANRCpFUz1enguJZMKexMRnbjGipzcmwV/vUSj1puPGUvHXb2Se+Z8wsg9pazI6cbfBv+UGmcKD0R+xsf2O+kSL+XO6lpurqnjY4+bt1O8jRXYdS3GlvSWZX0y/BJTwL5UiaG0DFst0PUWVWwsmJCIlCyXqdRJLxHsjRQq4hEUexVZZpxyPRtDC1EdqaQ6UoXUPZiGFxLOAA2B0SXhAIqiUBWpozZaz5aaLYy/eBw3X3kzn3zyMcefdDwutwtX1LrAOEi0eUjYKJep3HP7DVx2zS/xpGYwZPSJmI3vzm1DCEGmKxOn5qTIX8TIk0Yyc9ZMft3t1y3OKyws5Oabb251/Z///GduvfVWBgwYgGmadO7cmenTp3PjjTdy4YUX8sorr3D66afj8Ry6WEZZWRkul4u8vLxDnnu4+J9PZnUwrFv9Mpet+hvP9L+JsYNvaHnwv9cz++V5ZG1X8Hz4Ct27Dmu7E+D8D89nW+02VKEy0J7BP7atp9rm4MsB57DADktKlxLSQ9bx7IGMKRjDmHZj6J3ZuzFHgm7q/PHTG/iobAk31dRyg+6GE34Dg69EGjFC6xaw5om/kbasBN1r0Om4WnwFlgkpZkulJqU3uVWWrXVJ//vYUXAuvpJNOHbtQuzdi7FlEZl7SxsLEURssCOvZbrUGi8M2SY5bYNGz+1xVENSk6Ywt6/KF31MpGbjzK2pjFq1D1+l9WfdVACLeit81aspu+D+ONk9ll9veZMucZ3PRk6hImMI6XXrOXXhZWzpdDkr+9zJ+g3XM4Mi5lbaSQ20zOL/scfNHTlZFOKhSkhUIfi16yx66xJ3pBRXuIRwpIiHXLUsTVT4GRcM8Xkit3aXEsmDUwxemqDw8VBrvG1xyaQ5JhNWSva2kyw6NU6qK25p9IlJQDVlIA41hU+UKqY4agkjuTQEN1UU4ZUm5at8VG72cs0tGg/UV+Lp8ju84WL6bH+RgLuQWl934tVfcE5hO5CSc7+SXP65SVWhiX7pySzvPIG9sd041y7i3LfX4QmbvD5O4eNhGtmuznTQChhVs52TypbSKa63eBcIuAv5pP0ZzAt+wVxbyyx+z/R8gmHeXKq9tHqQ9orFDlg5vjkMKdgns6jDg5nQqFV7OaoSIy9qY4/MRYg4QvMj1CZFQUobYCa8WSwIwCbBJa0IxYZIRSk1SjQVX72JMy5Q0uMUKx4iitHi+sZ+TC9O00m+WY2LCFHFjT2zA8J2eCaJuBFn+bbl3HbDbbw/431y3blHHPn4TfD444+TkpLCtddee9DzNm7cSO/evVu0/WiTWR0MumHZHVVlP3e8mt2UL/qQ/C0ZbDilCxcfhLyD8SDba7czqe8kqsJVTNsxjZHtE0ncy+dRYArOKjyRMT3OY3j+8LZzIhQtQ5v7V/66fQ5qTjZPp6dhpA7gl3u+Qix5HlG1DQ+S4d3g9m4FXDBbQ/siE2+7CLm3/Bz7OX8gs6aGwPLFhF+5k/yFz5IWfAujutb6nKrlDbKgj2BbO4usizKtqjOKKTmpLJvfrLZRsKIYW9Sk3hPl48EKC/qouOwG5+/0cfb8dGybdwERtrRT+e/JCl/1FFSlNv0BXIqDCXVV9Bdu7vc20c2c0HzmFLYjT9cZvf0OBjiupX3/awgXjafnrlfpuetVHsxIx+n1tCLvuCuLzic8xVmhNczY+xrj885ndfWXPBqeyrU97qZ32ggC+2bzyo4H2aQ5OdtI49ROd5Dz1TWNBL4jX7A1H05dYZLeLcjAWJRaVaFspMbnOTZGfaZx5lt2/jHRybJuzWmyPLFY6BON4opHmOF1k27ayN2Xwo7OAoc9jsN9KsW1Yc4vetEai1AJO/VKrm/XAY8B/5wWwL5RY10vG/efY2KqX5G/S+GSeXFGLVlDZWY7/nXB5WzPjZGvb8BVt5QNtq0ssSk8WdgOr2nSOabRL1qHLmCzls3G4Gbi2n5vodIKjTdSaGW6Atht07AZqShSwy3DOGUEJ7FWcSeqkLQXFbSngjrpoVymEjG8oNRQ7NCxG+XoikSIliHnQrScg+kaj+OQEr90E8FGWNqpxU4UGyYCVSkhOwZxm0aajBGLZ2CikpUCKCHqY/UYiYpTUgkQVgJUOVLIJB1XoBwqNoM3B7y5Vqj7QWBTbQzvMZyrrrmK3eW7iWREKPQVoinfDw2mpaVxxRVXfKt9/qg18OXLnmXS+n/wwuDfMbJ/s4H96HfMfu4Dsnao+Ka9RtfOgw/Yx9LSpVzzyTVtHnu7zy/pNf8phL8EhlwFJ98D7mYl3/athLkPwNZPGpsM4N6sDD7webkuJPl1an9EwXGQP5BgZl+GvDgFZ/abnD57IFeuWYEiJbUOL6kh6zXZBOwpOsU5dqZ18LC9fYjdOfvlpZaS7sVw/HoYtVElLRwjmKgX+UUfjXJnOyZujDB2WyWeKuvPsy2vSdOuSGvqyx23U+sfiu7vS3oojWVO69W0h/da7FmzCW6/g/OULxmT8iFPOAZiePYQUBRUKRkQjTImHGFMKMLbKV6+cLmYvWcfqpAUySweif+EqeZopGUhxVX4Cqp3C5F9l2DPmI/iLKF92EW1048iBXmlo1hvS8GR0zrdwolrTH41w6RsbAbj2rUM+orWqxR/mUG01oarT4A9w2L8PjcLfyIaU5GSAYkIzXJVxRCCrvskD7xs8MxZCvMGKHSMJ7R3XSfXMNhlszHL48amS/7zdghlt4PPuw3i4b4/RTgq6SQ+5bdzV9O5XDKzXyYv9PoJmp7P77U3uFyb0/hdrnbYuTulJ7u8dQf8DY4IR7iyrp5+Ycm2UhfOi18irUcu/oO4ZkrDjRlPBRRUTDxEcIsIHiLYRJSoEEQSSa0aluZMIbACoRryfdhNiYFGlaoSVM3Gc326jUIjyHrZuVX+EyF0XEoZBVUSw6Oi2Q22yMJWsgo1jGKrbv0hTDsZhkmeDCFUO6S2B2dK6/PaQE2khpJgCapQae9r31jw/GhAUgM/TJhmgwbebBiClZTOe438rSlsnNCNiw5C3gB5njxGtxtNz/SejC4YzeCcwXy25zPu/OJOHqpawj9+MQ/3gifhq2dh4zSY8H8w8KdQuhqeH9fYzy4zl3WyE+vNzuzc1xGRs5EX0lfxdEkmsTV9sIKfVwMDUDNmMXVsFV9m384zW56knV7F9C6jmJk2kp15BimFH+B3VQNhms8zt9ubzZg1Hsbu2kFuvUlMg+XdTOb39LHd052hW2xcPGsffaqsidEdubBonMKiXoLyZhV5OkVNTgv5OSkUoncsTo/I3daflyZvjljleDR/V0409vC4+jr44SL/l8QrLde/BW4nC11Onk5P4+n0pvF815vKev/JvBU7m2iLfBUK4X2X4u70DI7cqXQrPoEdHYspcgcBBUyVbQVf0Tyr+oVl6dwQXcepHQr4so/gis8gXFSNbNdy9t2RYtBpQgWlK1Op2+Clpk5gP1fFox9H96rOFFJPvqiinagml0qcWjWO1TEMxcHSHoIxoTBe06RM01jiclKaCNLwhK0iDMpeB6+dJPjyuI10jT/IyNUqFy2oImLTeOCcVFb2rUXjBQZHIjj9QT6WbhY6fMxzplHjCAN1aCb0j8YYFI3gkSZCwja7jTUOB4tdTha7nCi64NFPJNolgnotFUHLCWwBTTZtNYSqhpBSwTR8BEScoNCpUKB5npCGsHyPaeKU1qN0r6aRYpoU6jp7zBzqsBHFZuWfNwHdRNHqEGoIvxZnr6Jh14NEzf3sxEoUV8ySyGmLUUfbdmRpuJCKG6GGMOPpCCWMUCOgxKhWoEa6yDF0Mqq3Uy89lMhM4oc0FAmEko1pq2Jn3U7MeBrSOLQd+3CQ7XOQn/rteZocDD9qAm80oajNiGLxP1m7wU2WBkNu+8sh+2jva88/J/yzRdvpnU/HlCZ3LbiLmxb8nqdPehr3oJ/C9Nvhw18hV/yHKZ5rqHD+mhKRy5yaXOr3//GW9seBE0fWPIQwiJafhfUXVIlWjsfV7j2Ks/xc7rqbV+1/5VrxKbPVfETHT1s4+WXXSkauyOeErVV0qi7BELC2k+CdsQrrnScwYIfKmQu207dqBQpWzcc3xios6i0ozWiiuQGRGEMDCnv8J3CesZET1aLGY1udV3J2dDKbZVNR1/WOa/GIaKucQevNrvzTfzZXhmZxs7qevcLJarfCXTlWdpTJOSmQsxQ1Uow90AMjjXgVGAAASHxJREFU2AMj1AHQwHSiFl0MXf/Bjo4ft+xYsd4UUkMZXFCawzBzHyckJnSHhiMsczmZO1AwcbHkwvG90EJj2B0+jjLSScfPcZ7PqD9nPhnbFW6cafDsi1EYvoz5eXHmmwP4r3FC0wMlJnl55/3s6xSkKxGeK2tK//mGz8tfszLoU63zp7d01HqV7SdFSOklGVkXYfjcCD23C1Z3hmcmCmq9tY3XrnA6WdHC/z5Mvq5zmj9Cx0AGFbEebNW7sk52YbtsxwnKGt6xPc0+xclN6oX03rGTgpqNVDrAYWvtfdTWu7YQJkKztHublPiMhkx6gNSIS8vcEZV2pNDJpJ4sYVCpqmQKQQelnAqZQqVMJd5AJ1LBjKejqmFAElYEhr0WxQwhdR/StD6jUCO4ooK4quJS4gTNA5OeNDwI1bL1m/FMiJsINWK1KVHKNEGFdFCoh+lh7qWMDKpkSpufubFP04aM5aDYqlFstUgljhlPO8gVh4eYfmRlBb8JfnQmFN0weeGLnZTVR3DWTOE1PuYCx404UsZhN0Jc/MUFBKa5+WJ0RwI/eRJFAEKgCMs9SrGinBHC8ndVEv6vSmJfNO7DJv88Pip7nPbuflxceDd24aBr8YcM2Pgotriffxun84R+IUEO9MOVOHKnYc/4klj1aKJlZ2ORuIGn6yNIw0do142kEODulPuZnhFluctBStCqAzl2naT7Puv73dROZX7XQlan9mdQ7BNGbVDpUxRBwapLuai3Rdr7MptI24jkEasegxHozelyPc/an+Q/+in8Wb+GdOpZ6fzFYY/7OrMTE2P3AwIFk5+qc5hs+3fj8V/kZlOjKizd91s0zxZUzxZU926EMJGGAz3Ule4hB13cS/jM13airtGhMI+VV6KZGptkezaYHTlbXcQVhWkUxk2yq5xc+JrO+6MFb47ViNcNIV59PPaM+djSVpCr63jKxuIt6chNS94nrS5Aaq8g+QPqiAg7X5m9mW8OYE9lNr+d9yZPTVQYl1/LT/wBPjWOY2PmLl5M9zFit5ur3gd3PMqTIy6iLsfHiPINnL78KxyxGJXDwnw8TOFj75HlrwEQhg1TT8OIp2HqqaTrgmvNhfSI+PG8l8VOXybpj/yV/G6HZ0rYH4pUQXejG96E6ao1VHQURzlOadBJ11u8zew2c6jHgwTL7KHE6BWPsE9xU69ak5tS2pC6D1WroVOZScxhI80TYoPZAf0gmrPiKLMeDrHslgeEiVBCCDWMajgoNEP4RJiwtFMss+hW2LrSU3NIKSkPlVMZrsSluWjva49NPXgyuO8SR2JC+dER+PQ1+7jp9ZUAjPG9x5rCpaTuuoSi8HFcq37EyDWfkrVLY9IZt1CntD9Eb4eGlrISZ7u3MUKdCe+dBNJOGn5+p73FT7XPKJEZ/F/8Cmaaw2nbrV7iyJmBPXMBseqRRMvOARRsaYtx5r9PrGY4qrMEr7KHYVskYzZYhXxVCbuzbIS6RMnKNHm/5FyGl6yhf/leVGlF9y3qLVjUS6Eou+V9w8WXovt7g2xZ5u332uv8QpvOarML+2QmZ6htlLJvhneNsbxnnMAb9vt5OP4T/mGcx1hlNXdpr9Nb2csqswseonRXipmUZ0387t19B9tlopCvEibLs4ps3zKKUovbvMd/9tbhj7bnXZ+PuTn7UKNZ1O+9BkO37DI+QmR2v5dx4RADq/IJLKml6z6N6y4fiZK9qLGfs2rhZ9U6Z0cfRqJgM+Jcv24qE3cuojgji7KRmYzybqSLUkrZyhQqtnr45c0KM8r3oRkqf8vy8U6Kj34bOnHbR8XEVBv3jpqEmlnJTzbOYvT6Ckoy4bFzVXbnisYKTEMjUWLBrrijqfhEhEJRyUBlO4udDl5MS2VxwqsmW9c5JRhGRbLLZmORy9kincLFXxhcvEDyhytVbprwJHmd23ZVc5gCE4W4cii/ZWGZLgw30mxd7k+oQRRbLe11nZQ2aq/6pYsi1YWpBegQAyEVdsh8hBqyPFeEjjMmaVcNulfFZmvb/t3inloARavDjOUiEqH/QjT8a0SzbfARJEdWoGEg3FmQkm+lkj0I6qP1FAeKEULQ3tsej/3bMakcKZIEfhBIKZm6eh+VgRihvX/n+dhsrsn8PR7PEE769Byi76ssPKEzscuexJRWHVQzkQRKStli30yUmzIb263oSymtcxv290QXsDLyLJlqH4Y6foNI+Ne2D67jwpLHKIxuY6NnGG/n3EKFraDVvUxTspt3KGEm2fJECuUlVDCfUvMNjtshOX69ZMg2iV2Heh/EusRZ3PkiYv4UBm39lI57S1GkYF+6Rdpf9lbYm02LlJe+4Hm4IuOtpEWSxs+XbVQwyFjDEGMtw81VZFJ7wLGdyomcwzwAVsqe9GMbtmbuYPXSTYoIsVvm8LB+KTOMEQD8QXuduR2WkWqYPFdWwQv6mfQWu0mx72VuiuDFtNQW9/HE7YRUA6kYSFPDCHVGD1opOh3Zs5C6h/DeazBjOYAkpdcfuLqull9Wh7g6OIK7p+/g8TNTWDSwyf2uUyxObvlwZvsvaXGvE4pXcevKd9CFyuJhffl1+w/ZOi2XZe1UVp0R5S+VVdyVncUsj5url9s5dXYYv0/hrfMUStQ4V82AzmUwbxDsHBnlOMMqm7cyPJI/6tc1mR3ahI6WsgZXu3cTuUhaQ0hJQb3JAy9ItnSWfHiWyTWDnyWvc15jSta2tXxhJQY/QL8tz9RQpAcNLwpa4i1TEmYfqozTJW5Q4eyMwwyTHtvH7r3FTLzqFpbMfZedNhvtdR2vKSn39Ey47Ulq4kWk+KOkB6AoB3zCgeIsREHho+lT6dq9O716WSS2bOli/vC73xKNRghFgpxzwbncc/eDzJwxjc0bN3H7b+9IMLdooQIJDByhcmzRKqTQiHny0e2pLVwHGzYbHgJxM0ppqJi4GSPTmUO6IwNEc9VKNNyq2XU0O0e0ajtSV8XkJOZBIITg3EGWhjdrrhv2wFkDOtCjajmfrpNk2+D0ux+hQ/tvL2cv9Gfa9kL+uOCP1KU+z1MnPYVTcwIDwbgElr5I788mc8+ea+GE22HMrWBrmYtEyqE8tbKQF1c/T+6euZy1wUoM5YlC0KuhnnMS7SacR9qeTex7/SnOmPoBiikoTYMPRyp82Vthdw4tSPvMzmdy1/C7SHOmNd0oVA0751sVu3fMg+qWbn3NYY74JcSCKCtfAeAc5mHm9EMpX0evq58hmtYZ5ZVTURN9pAiLMHP7jefhrHQeVHaila9Hq9jLTCHIlZKwEOSlfc6Lqekst6e1uN/Do1+ke2pfQBLWo2yoXsXKyq9YVbmY4mBTBgdhr8XT9TFuH/AgPVOP44YvJGmmSUAzKB8cpWQhnLGmnoGX/JVBGcdTM/9nPKvsZXHhSkamObig4y/JchZgmGCYQ4ht7krW4/cz4cul7M7KwgipfNFb4bxgiHEdCgkoCqctNznt0xBbC+Dhi+DUDXGunStBk9jH1XNDXghRCw/EL+Me47T9JmhNhL0K1VGG4ijF5SjC5ighZq9HigMrWFdXSE6qV9CWgaobnNzNT3nYMkE014xNrCnwWlWlIpFQLd3Q0YUghkBHYByEYyQ6hqjDwLKVm/EMpGlDKD5MWzV1isQeLqNIZlFEJ8pMiY6KU1pltcNCkIJJXnAT28x2hHCg2MEVFcQ0BU3oVIkYMrKLeNTJe/99n7GnnIYvrxMAN/z8Wh559iV69umPVCrYvWMTe6tD9Bt1Mv1Gnczu6jZK3TciBRdOCkQl7sBe/LKSYplJlIOZSLJQbNVURcqpDPox4+l83aBzIQRdsjx4vkZhlcPq/8emgTfHx3Pu5I6ij/jg5Odx/vtX1L4WZtOZvbnwse8mseLU7VP504I/MSJ/RDMST8BfCp/8Eda9C+md4cy/QfdTAEsbj6xbR9206ez78G1cdRHCdvCdcgr/9C7DraucuScD78ptqIakPDWhafdS2JlHq+TyBd4CPrrgIyuIKBqAPYvYt/JjatbNorfY0yLneAOi0sZ8sz+zzSFUyRSesT3JYrM3v4jfxnzHrWTtF7L/l/jlpIkgv9Y+OOS41EsXY7pYdk2fYeJXFTQpGzXHeO1gIqXntjLpNIfQatG8W1A9m7GltE6z0DkWp0JTCQqVM5caXDXH5Mbxt2Gmqcy1/4a/G2fzTEo+jqzPQNGJV4/BXjmS3yvvc7k2B9PA8lLZZr1W/+bnKnuzBUjJZfOsHCsbO9nRRlTTe6GGXuTEkxchf0QtNpdFpDfEbmGu2hnDYZG127EHu6OEqL0WM1HNVySKCXeLxekej9MuKlBiWQSihew027NJ5LA5pZxoxkoUWz15+7J4/JUypncexfMjhuDu+Bwv9HmMUYVW6trIl8+hVm1FwURBogMxIdCwvEsaIBsXi8zjByGsSHZ3Sk+4tUWbW5rEpRsDB/v2lHDdZT+l74BBbFi7gh49u/DeE/excetObr/vMQLBEK7MDF65ezJZHQo554rL6NxvMEuXLOKkM8fz8rMv40tJJcWXxjMvvcp5p57I7EUryMrOwSBMlDIcZPPBG++zZtUK/u+hx/jtr2/A6XSyfu0aqioreOTJf/De22+wYukSjhsylMee+ScevZY0vRKBpF7LwK9lsp963ahFgyQi64iY1ajChkfJQxW2Ftp3y2uabTXrUhGQ7rGjKQcJM90PSQ38MNEQIKDu+Yp1S/xk2zVG3H7/d3a/c7qeg5SSPy/8Mzd/djN/P+nvTSTuy4OL/gWDr4AZv4HXLiSafSr1wUHUzZlPfPcehM1GzrgTWdoV5u75jKFb53DlDonNgMqUKj4aIviyt8r2fFqR9jV9rqT7qne5yx7it5nDUD5/0NK0i5aCqdMOaLffb6xMpjHHGMxsczBfmn2JNHPSu1u/modsL3Cj/JB/66dzh+3tFtf+2fZai31TCnbJXDbIjhTJbApEJVlqCVt8NUz1Nfngjg2HSTNMpno9BLATKr0Avf64Q46t1NOI1w4nXjucSLGB5t2Eq/1/Go/vtNsYEwpzY1WU67Mu5TJtOufsnUlapg0dhdf006DaR2F9NiJnNkWZ8/Glfc6mUIhn9RRWOB2s6WxnysMW6d33qsFzZyoMXp/OSZurqOviZXzhXio+TMGIKbiH+CnqF+dzh4dNNie77Qrb7O/haPZHzmog6vo4eTEFLZZJMFLILrMDW2UhX5kFVJBKK8aoBqonoKWu4vIFHxC1SaaeuRqPbwlOQyHFMCiV6WTgx4FOVCqNGqfACr7RMZEoOBSBkKa1WCOJKsGeyESiI9AFaBI0JBKBXfOgeguIm3H8cT/heJiQUFBFBJMIhlLDzm3buO+Juxk84l7+dPOfuO+V95j90RymTHkCb04m0979hHv//ndeeOJeAFyqyrpVawnrYUp2lzD65NGcfu7ppDvTufXWmzl19GDGjRvHaaedxphzxqB5QmT5HKS4bHTK8uB1aETCAVYsXczUqVO54opLWLhwIX379mXYsGFU7dlK+0GDwMiB+mJSw1WkygCkFh7Ed9xNIOajKFBEwCymwFtAiuPrTQ5/V/ifI/BdlUHG/e3zwzr3pLRdkA/rP3ieTls1/ntcAf96egew4zuUUENLvZAv5Xsc98/LCRddAbLpdS4rVMtJxSdyQbEgtXYdiLWUZWXwYf+ziDnrGbJ1JUM+q6KXAVU++HSwpWlvK2hdoFgN5/Fb/04uDlXwwdYFrMr0o6QrDJv/VFPCiv2wzuzEHHMws43BrJOd2vRE8BFip2lNkt2kfXjAT7rO7EQ/ZRdlMo3x0ccI4QRMVM92bKlL0XxlCCUNXyQFqOfUQBCvafJaqo8+0SgPl5dycaBTs1jIw4WKHuiLf9NfcLZ7E1vKeu6tqKK+djT91NlcZe5lQW874zdupv+Afaia5EX7o3RU9rLRqbDMdPKy6cOvKnzga8osd8qGDKCcN8cqjF1n8tv/mkAVsX5hwiJG6fx0KjIkz1wq2JDf5NyeYRh0j8U5zx+kWzxG91icLvE4KYnvYKYxjA+M4/nS7Iufww0o0ei5I5MR2yO8MnAIfp/lMhlRTaoVBwY+qqWPjqN+g0dEKZPplMm0xqsVexVCiWDGspCmI1EEOI6TOA4Rw0G8cXE1s5ObUhDHhqwqx8COKh0oNompRugSi1EpM9gTyyKvXQGDBp+MlH7OvvhsXnz8BTZv2sbFl1ieSyJu0C4zm+1ujaAQjDx9OFsr1hOXDmK6RJp2TGlSFa7iJzf9hJFnjGfhZ0t5ccp/+NcrLzNl6ovsrfFTGYiypqiWSNzg7LPPRghB//79yc3NpX9/q0ZA37592bVrF4MGDQLVBumdwJUBdUWWidCVDikF1rH94LV76ZLahb3+vez17yXLyCLHlfO9huAfDP9zBL6zKnjokxqQ+GHqG00idoV3Ci85xAXfDvS6oUSQOPP/i6vwP2jbL+D44o2ML1pJ/yrr4bEpvT2z+w6kp3clnqpKrto4A4cONR6YM0jwZR+FLW2QtjQ1XPU9GFHn4az4Hs5QLR/lS7TPucKdS98opDYj76i08aXZhznmYOYYgymheQFeSTsq6aPspo/YnVjvooNSwYFwXex2XrA/xpP6+Tytn89GxyTeNsYR1iLY0xZgS12GYq9FGq6ExjwMfzSf9F6/Z5bHgxRwRq3g/poybMAS56/41BjCc/rZrJTdDuja1iakjUjx5XSsXsIF8u/MF2UoQvJLdSb/6ZGGY62b2SXpbBhkMN+ps8uRhy4EipT0jsUYHIlSqmnMc7mIKYLCPfXENCslwdhmFfjs61zkAZ8dB8uPN+hrxhlfaUOLZhCNFlCsd2KrWcBbsoDaRFbC9qKME5U1jFXWcIKyljPUpehSYYXszjxjIPPNAQd8gFqfTXLtuulUOlP4sHdfVFYzNBwFw01MMVAdpZiGm516NgWyllxRg4MYRTIbE4EZy0BxVKDYqzCjOUipWf7e2EC2fIho0mhF7B6ipIsgCIgbsFW1Ua5pFOrVVFKLKsBtKERN60GW4vHQt0dXXvhwHoqjjMIqHVDBiGKXkO10YpcmQkRQRByhxFrI0KFrNh26nsnFV5/K2F5jqa2uRdHqEUJHKFFihoEtUQdWURQcjqa3RUVRWqR0BSyt294LgmXgL4NIveWp4s5q9fZqV+10Tu1MSbCEylAlET1ipab9nkLwD4YfXoJvGeN75rDrwbMO69x3P5pG6YYtdNuqsGViX9Y8ct0hr6kMRJk8fUOi6FSTP7ho5sbU5BMONPcdp8l3XI3lYV9dTcHK2QzacT+qKfHnFLDupDOptFeTsnczZ235Emccat0qnw+wvEc2FYKM5RAN9Eff1QfVvRNn7gwATqpK40Z/ET2lFeRSpGSx2OzFQLEdXdFZ67BzTV2TrXqh0Zfr4r8hhBMNnW5iH6PE+haEnSYSIfpSsFPmsUZ25c34SWyQHVlvdsSGwTTHH1ExSRNBxiprKJXp5FFDR6WY2V4n73r24nE/hBASPdiNaMXp6P6+zd48JHFUpOEisu9i3g72okB7l1s0ay7iVHU5p6rLKZEZzDSGM8MYwQrZvU1ysxOnh9hLf2Un/cUOay13ATDQto45ThfLnQ4+9bhJaQfezU6mjNXoHdO5qi7I0EiUQZEo3oR9uF66uFK/gU1pexi1ZQ77MuBPbxpkBCTFHU0Kdjf5LfdwZzOn6HzelQWtA7P2w16Zy6vGBF41JmBDZ7DYylh1NWOVNdxhe5s7eJsq6WOB2Z/5xgDmm/2poEmrH12yjt41u3li8Dko7T+gU1QyuSTCedG7kYNTmgJf1BDFphO/nkp76rALnd0ylzgqZiwTxV6OsFchY9kg235Y6KgEUAnIlhPripRNWroepE6LkyEEKSJMcXEx5Ss+ZuTQgdz93gwGD+3H26++x+4Vs+k2sjdKOMa60n2MHtiOuLRTp+dhi6fSUVRR6HaRUldHt3icGILXZ69g9IRxKFqI3Tt2o6gKvlTrQSiUKIq9EpMQxf5i1lVspLiyhKgeY13ZLqTUqAmH2V1dx5riqsaMiU1w4aCAdqIKX10RodoKimUWYewt/tOWXduLUAWBWA2bq7ejGlko2BM8YJ3UcH5DPAiJYykuG+nuwyuBeCT4nyPwI4FRu5eLFphE7DDy9gcO65rHZ23hg1X7vtb9NFNncPkWxhWtZFTJOpxGnAq3i0+Pi+B3KeRXVDN0wUf0i0G9C+b3EyzqLdjQXmBGOjI6qPN+0Qq6GnuZb8TYK/cyMrCBxaqfB7IyiDjL2VLThfX0wU2U7qKIEcomAOa6XBhCMCoc4azo/bxhv58x6nqmiz8Qwkl3UYQjkZgoLO1skh34yBjBBtmRDWZHNskOCRNIa9wUv5lXbX8F4CfqPJbY0liQUUSV91/coWWhxkPEKscTrxuKjGe00YMgvOfnmNFspGH9MZ/Sz+NMZTHdlWIMKVATE6uXq3O4RvuYEpnBZ8ZxbJPtiGGjr9hJf2UnPcVe7AnXxZ2Kh6n2Al51F7DVZbDVbv2BVFMwOBrmkyF2fj3NZMbSCgpzrIx69dLFJ+YYTlFWoKNyZewu1stO9N/sJD04h/TEC57fDQW7VUQevNnzJAav2kz3hcUM7rqJNX27tK4BcRDE0Vgse7NY780jXEomdRyvrOVEdQ0nKGs4V/0SgA1mR+abA/hC78fV66ez25fLgpE7sCshHikp4/bYnVSQjpSqFVGopyDUIEIN4LebbDJd5JsxuhrF7JZ5hKUdGc9EsVei2KpaB8gcAiaCMHbC2EF3o6il7FUd2IGuXbvy0JRprP/NZDr37MwF1/2Ms8eO4va7H6C2LoCIGdzy858yeuD52ImTIeoxpMJW2Y7RZ/+M++68laf/9SbvPv8wc999k0fvuReHy42pOnjg6adQE+YOq7xbFlLaMU0XmC5AsQhXDSVs/lEUrR7VUWpdIxWLyKUKaMSkyi6Zile6yBV1dKWYalIok+kYDQ+1hpdW040wNIStGkMtIx5PQxqHNnvVheOkOG2oyrdrevlRe6H8455OjH/LxeozunLp49MP65qYbjJzXUmj73dzn2ma+YE3+oQbJp7Na8lYPI+MpV+gBf3EPT729u9Bmb0eUV3EgK1h3FHwO2FxL6si+6aOdvLdg+jkGk4nrR/dgttpX7OUQSVvo8mm10u/LZvVOefyhdjN67bNjArHeaqsBA2NXd7BlLm7UWfLZrpYxhJlDwt3790/up19zq5s9o6g2NmdYmc3ym0FSNRGf3Qz8Rnlfv7wMvEZTQnjal7Fpr/D+z4P6xwONAk9YtncXLOelzLeICo8zcZGNo6b3G+74V5ISf/YaibX/4HZ9pM4Lr6KTFnNEm0oA/S1ONsoe7ZbdfK8ZzhrnAr7HLXENCsBkmaqDIsG2Bw7nUB0AJnRFD7Vb2CjaqPu/WzM7DgjR1XyNqcyj8E8IJ8iio3r5J/YKguRSJ79aDJ5waaESroKr45TmN67O+Hyc1HDGVy7fjrn7ljApvT2PDD0Cso9bT2sjgwCkz5iD2MT5pahymb82xyULU9j4QQ3Tw6NcUdVDcVVE/mncTYAL5yTT26HLs16kYkAmgBC6NikJMMwCRgZ1EsvQg2h2GoSCa7SW1yHMK36mUJi5Qw3G9tBJsqvmYloyGgzudsO3QfIqgdfSOJKjyMERKQNB/EWlou4VIliw4beqFg0xx6ZRZ2wNYbl74+8VCc+pw0pDXRTR5c6uhlPbCfWZhxD6piytS+8JiU2QFVdqJoLTbGhKho2Ya0FUBbeR0QPk2JPJ8ORDYjG/wTQ4nfttKk4bYeTyDcZyHN4qNnFS1efycCdkrSpb9K148BvrWspJdGNG6mbPoP6jz5CLy1FuJwEh/Rij62O2spi+m+J4YlCwGmV41rUW7Cuo8BQBQ+Onsx4xYd7z2LLH7toGZhxq0pI4XDoPBYCZbDuPYjWg1DBjPOe18N9WRmMisOTZjrOmt0QsXx3zy7Ip1A4eDZofXYAzv8nTL8N8gfCVdPanMQ51OdcXrac97e9z6e7PiViROgWi3GBP8jEQJD0PhfA3iVw29ojG8B4BMrWQ8lKyyOnrXt3HkdRegHL/LtYHillGWGKE0mkvEJjQGov+heOp2/GYPpv+5yshX9h+zUbcOyeS+GcXzX289L2PEYsU6h95AFUr8aQBdcTs6fx+YgXqXUWoBuSdrNep/CNJo+WeGYKC6/8HQszdrAh9C46UTpoE+iknkeX9Ws5aerzSCGYccZ1bOw6iLgh0Q0T3ZTEdGutG6bVbprohiRuNLRL4qbJgf6WmfFanp/9CJFUhdsmRRgQi/N8aTnFZjbzzAHMNwdwycSJZHfomiDfBrJNkKwaakG0QMKnu3ka2IPRb3OIxKIgpAIIpLD6zjAM4sJFRHgwRQyDIJmGiQ0X9hoDFAWfN0ClrR0R1YcQEk3GsZkxNJlYzCiaGUPh4MFGAXd7dJsXhNJovvA5tcOeaDRMg3iC3ONm3FriYeLxILo0iQvRpgSqojZ6srltbjqmdGzM7/9NkCTww8DOf15B5PFlvDtGcPtzS/HYvnnYbGzXLupmzKB++gxiO3eCpuIf0Jk99gB19RUM2GHgjUDQAUt7WJp2aZ9cTux8Eid5OrFjz3werFzEuHCMR0tLLetau0HQ+USLtDuMArsbwrVQtg52LYDPW5p+3vd6uCcrg+HSzlOZY3DlD6I0rZAJX/6WO4bewZV9r4Q9i+E/50NqAQy9Fj6+E0b8As54qNVnMk2LUOKGJK6bxA2T0mA5n+yeway90ygJ7cWpuhmefTJj0sdx2UcXN9r+Ap4O+N0dmDPkH8QNM7FIool+4rqJjIfJDG4lL7iJdqHNFIQ3kx/bhUbLUG8J3Oe5ks7KR6x3wlxnBpFELmxpuBGhDnQLq0yMlHCpvh6PiFMm05hpDKe3socRyiYWGn0Zo7b0Ef/c9JD9Tipz+3bg2r4r2CczuTz2B0rJJFtW88D2FyhY1zRpu6BLfx7rfzmmzY5NVVBtQUifielZjJAePMGJdKrsztVzXqJ9xW4WHjeBz064GMVuR1MFmqJgUwU2VUFTBTYlsVYVNEWgKgJFjWMSwRRhDELohNFliLgMMfCjeQz5dAV3XaWyvZ2gdzSG4mpHVK8nbIbxC/hL/6fIPUAo/eHAptpIc6ShCAVVqI3rhm1FaWrfH/6Ynz31e8hT7GRGgpDdC0O1sal6E9moZBuCSEUcLc2DTamF3H4HVxykBFMHPZJYopZSYsTaPt/us+pZag7QnNZa0VpNTB4WpIRgBdJfggnEPVnEHT7iUm8ie8MifFOadErthEM9cKzC4SLpB34oBCrYPHUJ2Q6FGcMU7hCH92rTFuLl5fhnzqRu+gwia9eCENT1zKdoYBr+SB19Nm6jbxhCdljW3dK0A8d1ZWzeEO40NPrs24iY9yKEaxgFaPldmOyC3xx3Ko+d/DS2eARK11r+2stegtI1ULunSQBvLuT1b1zOzxuAVruBP315N79yBHl60GUs2mXlGx/ZbqR1TYcRvNnjUc5ffwv7Pv0HW7VxnLr4Oe5Z7mQGxxPTE4Sd0AotWL7VtrRlqN7NCGGihzoRr70Yf31/Zkg7M4jzL/EIcxx3WKIF9/BmXV8mf2Dl33YQo4/YTT9lJwPVnfQTO+kmitAS+k0dPraq3fjSeQE77N3Y6Eql1FZNmvyMMrWEavVzwE26ITgxXI3DdTzRjJ+SYS/ErmpoqiCiKrxlhuhcs5Au5bP4WeU8NNPSCpuTd8yZxZYxj3HCnCt5r2saI7fvhuO7Is59g7fsDtKWP0Poxdep3dL0ir7pvFSu/etbXNfKjnkBG6o28NCSh1hR/iaVeXlELvkF8rWFjHl3JsNCW9h7x0+oybDhj/nxx/wE4gECsQBVMT/+uJ9ALEAgHqA+Vo9utjYZAKQFJD+da/Blb8H2dpYMRmY3XO4ssu0peDU33lgQj2IjVwoUw0qqqggF1eZGtftQHD4U1Y6S0Faj/n1URaqoVdRGndswDVLsKS0DzQ4TPrsPr91LRTxEKgpqzR5Eake80k7cMNCDUUBBVeOgOg/91ieEdY5qA0eiGEpqImeKaYC/BILNvKJifogFaPEGIVSLzG0NpJ4g9kQx5oPe25uDcKWh1hWjBspxRuqt+7u/uXns28CPUgPf9tINxB+ezztjBO+MVVlxxQpsyuGbD4y6OvyzZlE3fQahxYtBSuo6ZFDkDhPWI3TfJ0kNWWXLLNJWkEP6cKI3n/H1tXTcsxTqE8mZUgqhy4mWdu3NhWAFb+6Yxv2B9YyPxHm0pKQxBIOs7i3Imtz+4Gs709pHOz7irgV3MSh7ECmOFNZVruOziz9DCMGSndX85J+LuCJnJ3/230eFrQAhICu2j+e6/5Nqbw/smqUpBs0StofnsiU0l5BRi0dNZ2D6KQzLPI18d4fG8+yqgk1TsKkK7RffR+a6lwCIZfXFyO2PvXwNSuVmhExo1u5MyB9kvWHkD0LP689mM8SysmUsL1vOivIV1EUt80+eO5ehteUM1RWGXvIOHbwFiHcmWYUwTr7HSj9wINQVweN9W7fftt76I96byvv1afT6yI35u6sp7CapX/gsgbkK7GnSptZ1EGy+bTTOrF4E4oFGEm5OyP6Yn6jR0jwxfLPJL2eYSAHPnqWwtIeCW3Pjs/sssrN58dq91r7N1+a212atlUeeJzp1Jrf+XDDIFuThUfdZgV/7oVGDM+IQ9Vtmtqjf0mQBNBfS4bO0Vc2FjNRh1BVTJ1T8igKmiU1o5DpzEFKCaSJNCdJEmiYklsZtKVu3H4RXhKriSI0hPBlWEYZvA6YBwXLLv1u1Wxq6Hm2pueuRpjGwJElo6vsRu+Zsu7pPpM76PRkx6z4p7Y7Y7Hg4SJpQDoaon48vGEZWkWDubZ14RRSx+srVh7RdmZEIgc8/p276dALz5kM8jj/bQ7EnRgydDhWStKBF2iu6CRb3seEc1JUThcaJpdvJqtxmdeTKgPwB1tqdAbEglK6Dik2WnRvA5uaN/M78VfVzkq8rfxv6e2x5/eEIs6N9susT7px/J4Y0mNhlIg+c8ABSSi59/it2VAaZf8d4XHvmwhuXgScHwjXgzSF0zUw+LVvM+1vfZ0X5ClShckLhCVzQ7QKOLzy+9cMuFrLeEkpWwb5V1rq8maO0O6uRqBvWcV8u66s2NBL2yvKVBOOWi0cHXweG5A5haN5QhuQOocBbANtmw6sXNhG2EYf3f2GlHhhzK+bJdxPSwxahxv0EwjUE1v+X+vXvEjAiBBQF/7Cr8a95k0BKPv68PgSqd+Cv20WpqnL/CxK/Gx68WOWO9wz67IW4Cms6C4ZskzxygcLSngpO1dmKZBsItoFsNUXjnS3vUBywHtIXuMdw2ev7kBu3knbFz8i74w6E/eAuZVJKZDyODIUww2HC69ZR/OubKc7X+HCMzh8dvXH2vxIZiWCGwpjhEDIcxgxHqDz5ZHq0L2xJsqaJNA0wTKQ0D8/EvT+EglAEKAooCkJRLC21YbtZu18PEDaiZCLQpEHIk05VrJZ8w8DuzUCJVFopI1xpX0OQbwBTb0ns8cT2fg9eFNuBiT1QBoFyEIpF4u7Mr2eiOQCSBH4QbH3lNvS/fsy2iT3ZPdzk+dAO1kxa1+a5Mh4n+NVX1E+fTv2sWchQmGCKnRKvji5McuogIwAxDVZ0Fazs6yCldw5jI3WM2bcZT/PZbbvXIm1pQn1TMQS8eQmNul9iPQAyuoCi8vrG13lgyQOc3OFkHjnxkSN6S2jA7N2zueuLu3h47MOM7zCehdsqufzFxUzok8vEAfkoQpBX+jmDv7qZdXaV971uZvp8hIQk21nA6JwzGZVzGumOTIQQaEYYb+1GPFVr8VStw125FmfdNkTis+quLCJZ/VH1EK6SxRT9dB5GRjd0GWdz7XrWV69ibdUKNlavJWJYNuyOvs70yuhD97QedE/vgcfmJhgPEIoHCMT9BHXLvBBY/z5+fzGBLifil3FL663bQyAeIqAqh+Qkm1Dw6XG83nb4TBNv7V58EryGTmyjm0lzmr6veIc8onf/isCr/yH1yy1sv2cY5533rxZ5omU8jhkOY4bDCfJMLAlCra4t4dNNU9lRupE808dpc+sar3UOHICWmWURbyhxXSTSYp820rQeDMJmQ7jdRB98kB4FBa3JVlGaSFgIkHGEEQUjipAJ5aEhfsGbDd5sayKwoY8jIKm4EWdb7Ta8mov2wRoirnS2G0EKdJ20hs+V2x/Uo8SKK03QY601dj0Kstl8jFAs0wuAnijmbPNAWnvL9v4tIEngB4IeY+a5A8kpgs6zPuU/c3/BlPBuVjYjcGmahFetom7adGo//ghq6og4Vcp9BqaAlFCCtFVY2VWwoa+DjE52TqzZydDIQXKcCQUy9zOB5PW3CrIeBK9tfI0HlzzIKR1O4eETH/5aJB4349gUG/tqw/zpg3V8tqkpOF2oQbTUFaSnfUHYUY/LNJkQDBGvHca0wE/oI/ZYwTBKg826uNEnu0KmstbszEo6soYCNopcKhUHQo0itHo0z2Y038b9PBxaQ0ol4Y528HMU006+GQDDxR6jveVCZjgYzF5Gy+3EDA+YTvrKEuqNDN6Jnsa6aE/+I5+gLJZHH72Iaj2FdbFOnC8WYOoKpi7w6y7CFWAWNZlMFuT3xyZ1RpRuBGBPWg6aLnEYMZx6FIcRRzMPlVO7JXQFtP0+5q68rsRtDuI2e2JtLbrNgW63lryy3fRfv5At7aB8qE7AezNV7gIMhwPd7sRwODFtDoSmIQRc1EXQoUv3/VKeihZK4v4pUVUzjsMI4DCC2IwQCiYVnh5IRUUgyPTYUY7Qh7k8VE5FqILX//YCaXbBzlCUsSOHcOnowZY2m9OSpD744AN69OhBnz59ALj77rsZO3Ysp5xyCuPGjeNvf/sbQ4cOpVOnTvh8PlRVxTAMJk+ezLnnnntEsj3xxBNcf/31uN2WD/dLL73E448/jhAC0zS5//77Offcc7n7z39m7PFjOGXcmNbE3mIiVVifR0tOYn5n2PzOfXTaDtvP6saQnPYYptlY/yOyeQu106ZSNf1DlNJK4pqg2isx0sEVM+hQYb1Sr+oimNVTJTu3jnHxMFfFYoiS/W5kc1uz641EPcD6cu1HXjj18t6XI6XkoaUPcef8O3lo7ENHTOIN57+zrChB3iaqZyu2tGVovnUIIQnpbgbWpXFNZCsAfucCBri/xK8I/IrCV8LJ+4qXCqUPdUIjpIChxhFKHUJZCVhFMg5l5DHjqZavsWGRry1qxxmz44hqOGIq9riKI6rgiAucMYEjDo44OHUDpxFjlLme4+Q25sXa4Y9LnEY9KYbGANOGqccxdYOgkU2eDveZHwMfA4LOlBHGhosww9hIUYuUAS0RV1S61u0jP1TV2LbPmUVEtRPR7ERVe+N2RLUTVW1N7Qc8riIyV+DImkWOP8Qt7znpURFglTufl/pOJH4gTVRKHl81nxovvHaRibf4p3wW6gKNGVQjiaUJZxTmUx06gJfGQeEG3AgkGgZxfxywHr5xw6Rd2pFpmJnOTGoiNQQwSRN2fvf7myzfoli0aUKyGT744AMmTpzYSOD/93//d8C+586dS1ZWFps3b+bUU089IgI3DIMnnniCn/3sZ7jdboqKirj//vtZsWIFqampBAIBKiqsidH/+0uzsor7y2wazWzrhmV2+Z7x4yFw02THa++T7YAxv38WAHt1lLNWS9a8cRK23SUYCvhdYPjApktyay2taXVnKO5mkp8f5EQ9zJW6Dg0pVzQndDqhJVlndG57EuRr4md9foZE8vDSh2E+jSRumIZlWkh4NOw/obb/JNu62o34eu9p8x5CC7EmNcStqc2i8aTEpkOK4STVdOMznXSJOfCZDmxRHX+gBDMcbyLZuMQeB2cc8kQ2WaSTanqwxw1EJIqIhhHRKCJSgYhEELHWwTiHgnQ4qRceBtt2EEtrj02px0E5igM0m0BVoth9KVR1OZ2gOxVpd9Bl3cOoqo6iSRRVYro8lPW4iIoe52O6U5AOJ6gS8d65ZL4r2XLhOHKuuIe9f/kpmauLKLn/Wrr2vOrrfn3NcAKB+CRm7J3CvVdP5ap5Ds5bvICzlArqf3cfZl67VlfYFswl7cMi/nmG4JdpJxI/8Zf8pFnBEFPKZgVFrLZ0tYbCdBcSeGrVo2yr3XzocT3gTkIOTWl0D+2V0Ys7h9950P7uv/9+Xn75ZTKyMsjIyyB7yFDu/NVdjDxtHDefPo4//t/DTJ05G03TOPXUU7nggguYOnUq8+bNY/Lkybz33nv85S9/YeLEiVx00UUHvE99fT3p6U3BR6+++ip///vficVijBgxgn/84x+oqorX6+WGG25g9uzZXHjhhezbt4/x48eTlZXFo48+is/nw+u1Epd5vd7G7UmTJjXK0KlTJy677DJmzpyJpmk8//zz3HXXXWzbto077riDX/zi8EsMflv40RD4xvfup9M2yd4TCsmd/hE7PnyPkzdZqnPYXkLUaQWbpQUt0l7fCSq66BS0C3K6ESbLMC1Fp+Px0OPUJi8Q75GFHzdASkkwHmybbGOBRtcyfzM3M4BZu2cx6z+zcKpOInoYzaCRQB0J8rS2pbWONbWNbNbui0k6hXVcuoZdOrCZdlRTQzUUlFAQQiHQGwI1gjQ9sQ4O4XCguFwIt4rijKO4otZ+RgqK05U45kJxuVGcThS3C+FK7LucTdtuF4rTiWjYdrkQDodly908E964FLQqS/sZcAmc9GfLDrnmHXj/BjrmfQE/+6/1SvvAny3hep8NhcNgyCRSnKnsX7JDav/Hx1/+ibw5nzPqT4+yem0Rm7sJLjvz1m/RVpvNmX0ns6XmSh4qeIg1hV9x00dbybjtGgofeICUU06xJtpMHRmLsublR9mTBQN62hl/5p+AWOK4kVh0y0bbrG1jvZMMLQZS4hI6tjbNU/IAE5myjdoF0qoI0YCoP+HK2hg2m/A6sfaXr1rLm6+9wqrZ7xE3dAacdC79B/ZBS9ywuKaO96fNZNPmLQghqK2tJS0tjXPOOeeQhN2A8ePHI6Vkx44dvP22lcp448aNvPXWWyxcuBCbzcaNN97Ia6+9xpVXXkkwGGTEiBE8+uijgGUyadDiDcMgNzeXzp07c/LJJ3PBBRdw9tlnt3nfDh06sGrVKm677TYmTZrEwoULiUQi9OvX79gjcCHE6cCTgAq8KKV88FuR6luG4ffDn18HoP0XRQS+eBynsIjaFOCKgSFgU0eo7RKnY16Qs2UYDwKG3gR9zoPcPo2TFFJKIkbEItvaHW2SbQMZB8P1RIP1RIN1xAL16KEg8XAQGQ5jj5k4YuDQLaJt0GIbyNirC3J1Dbeu4tIFl8TtmKGQdVwP4IwLlAOkhT0gVIHisKG4XCieFITX2yaZhms2UlK+hL1OlZ0pHko0nagNpNNB++xudMvrS692A+nRbgAObwrC6bIIWD2MNw/TbCSpJvJpq80A0w+xGog2a3OmQVpHi0RO+pP11lO6FvattIi23wWw9h14pAsMvrLpvnkDLJ/gZf9O3KP5fXSEqaP0DJM5183Ca0aSHQFf+yBMvanFeZjGfjK21dY2uTa09TB1XjRNZmcqPHKFi8umhRA330JFzxCdB9SiqLBruxd7WQqLzzX4Y+lOeKL/4X3Hp70NCevPnZ3aJqIjg2jmZZHYjtS13Y7gi0Vfcf4Zp+B2OUA4OPv0kzAAPeHpZUvz4XTYufbaa5k4cSITJ048YokayHf79u2cfPLJjBs3jjlz5rB8+XKGDRsGQDgcJifHmmNSVZULL7ywzb5UVeXjjz9m6dKlzJkzh9tuu43ly5dz7733tjr3nHPOAaB///4EAgF8Ph8+nw+Hw9H4IPo+8bUJXAihAs8AE4AiYKkQYqqUcsPBr/x+oEfCbJj2ClUfvEve8qJWxxMFUNhcCDs7S2z5UTp5s0nJGUSpI5s3QwFiwXriqzZjfHlPo5uWjERRIjFsMbNRs7U3I96MGOTrCTKOtZ60OiQUBeG0ozgdicWOYreheG0Ihw3FYWObUcWXsSIKnW5OMnS0SDlCk0RtkhUeB/NTnGx12zA0yRAjwgRXNkPyB6Kl5SAU2Yq4pKFTZAZZGq9gmV7HctNPsYwBXnyGyeBokCGRCENTe9JLKtjMSqj6DCpmwcoDk9QBifnbxGeTD358xStN23MPUKxDqJbJS9E4ITvMWreb7OURgg4YnVYPuxY2Hm9cCyWx37zNtl9bQ7vaZptQNCYoKicIeKX9VrZP28DpS9wsDWTR4+qTqfpwKns6wDXHDUbtekpT3439KW20qRBKsybMoZFUm2Yt99/en4CbXdO4fYTw5kLMBtk9ALC7MnCoDmLCyswZt9lZMut95qzYxrvvvsvTTz/NZ599duT3wUqalZuby4YNG5BSctVVV/HAA60T0zmdTtSDKBdCCIYPH87w4cOZMGECV199dZsE3pCm9rBS1n4P+CYa+HBgm5RyB4AQ4k3gXOAHJXB/6Xbm334Z+ev9uKJwsIBiRULfPdB3jwCcgB/44qD9S8CwgWkDqUmEBkKTqKpEtZtoThO7amBXTRRNIlRp2V4TS8t9s+W+Kq1Eaof4zxQAO1J93J+hsiIQ5EK/zvs+L7PdLqKKQs9ojAsCNUwMhEg1TYhVQv3mxj+7FCo7bTaWOe0st6sssymUq9ZN00wYYqr8zHQw1LTRvWRD40QvgbXQYWRrkmokj0O0HZJ8vs021YpafWeSJfvJ98CQSfsRboOsTQPuBir2DSdlgZ/iHnaG3ldz8C/j60BKiIctLTZajzNSz/WROkq77uHdmW8y4p29lE6ehgeB+8JhFF76yqH7bI6NG8HhPfR53xHGjh3LpEmTuOuuu9B1nWnTpnHNddc02u0rQxEc4SBnnnkmY8aMoUsXK/GWz+fD7/cf0b3Ky8vZuXMnHTt2xOv1cu6553LbbbeRk5NDdXU1fr+fjh07trqu4V5ZWVns27eP0tJSBg8eDMCqVavavOZoxDch8AJgb7P9ImDE/icJIa4HrgfLfvRdo2T9F2Rt8qMrUJMCug2yq5odb2eipCoodpWvnCZhm+A8xYnDruC0a7jsdlx2Fc1hR9g1y9zgsCEcGorDjrDbEap2mMSl7Ecyh2hTNCuwp2ydFdxTutaKLtsfGV25unAophOeYBEzvR58Ng/ntT+Z87tMpE9Gb4Rqa7yPKQRba7c1Bs0sL1tOdcTKrJflymJo7lCG5lpBM13SurQOavriMZhznxV4dMELB4z+POrgbSbn0GsOO2hk8K8ns3X9LXS8+AAFPvSYFd0YqWskYWu7/gDbDefUN2238SaSB9wErDzLxc4lacTzvJxz3ZQj/dQ/OAYPHswll1zCwIEDycnJYdiwYdgUG3bVCl6qDwS54erfEo3GkFLy2GOPAXDppZdy3XXX8fe//5133333oPcYP348qqoSj8d58MEHyc3NJTc3l8mTJ3PqqadimiY2m41nnnmmTTK+/vrrOf3002nXrh1Tpkzht7/9Lfv27cPpdJKdnc1zzz337Q/Md4Cv7QcuhLgIOF1K+fPE/hXACCnlTQe65gf3A98Pd708ilUyzMxJq34YAcK1ULK6ZQRjdbNybqntrUyB7QZB/nHW9n6Tph/t+AgTk1M6nNKYu0I3dTZXb2ZZ2TKWlS1jRdkK6mNWIYd8T34jWQ/NG0oHX4fDC9D4/CH4/K/QcQxc+eF3EkL8rWPJC/DRb+GaT6FDQrcwjdZk2mq7tmm7LULWIwe9LQCOFGtxNqxTD7Kd2rrd7vna0X1t+REfDYibcbbVbMOUJoW+QlIdqT+0SEclvi8/8GKgeSKDwkTbMQNDmmgHqb79rSJcY5F1A1HvWwU1O5uOp3aAdgNh0OVNYeeerEN2e2aXM4kbcdZXrW8k7FXlq1qEpZ/S8RSLsHOH0s7b2lXtsHDi76zAhS/+ZhHjqBu/Xj/fJ5Y8DwiYdksTIccO4xVdc+1HsmmQ1qGJkJ2p4Ehttp3Sctvh+1bdSP9XYFNsZLmyKA+VE9JDSQL/FvBNCHwp0F0I0RmLuC8FfvqtSPU9wZAS5bsoThqqbq1ZN+TgBosM8gdZiYjyByXI+sCBJfsjakRZU7Gm0SSyunx1Y1h619SunNX5rMY8Ijnug0d6HjaEsDw+0jtZHjnHAjqOBl/egQm3lTacZpGv9u2XvkrCQqYrk+pINVH9yGMAkmiNr03gUkpdCHET8AmWG+FLUsr1h7jsqIKBifpNCTxU3ZKo962C2t1NxxvJ+spEMqfjjjgVZSgeYlXFKpaVWoS9tnItcTOOQNAjvQcX9riQoblDGZw7mAznd5jmUog2s98dtTj7yR9agiT2gyIUOqZ0bLMKThJHjm/kBy6l/Aj46FuS5XuHIeWRmVBC1ZavcXPCbp6bO62jZf4YMqnJDPI18gb7Y35Wlq+0NOzS5Wyo2oAudVSh0jujN5f3vpwhuUM4Lue45GtoEsccvk6e8STaxo8mErMtGJgo4gBDEKyySno1ataroa4ZWad3gnaDLe+G/EHWBOPXTPJeG6llednyRpPI5prNmNJEUzT6Z/VnUr9JDM0dyqCcQd9K5aAkkkjifwM/bgKXEhXlMMi6MxQOgWHXJjTrgeBKP0Cvh0ZluNKacEyYRLbVWrnCHaqDgdkDuWHADQzNHUr/7P64tG8nRWUSSSTxv4cfN4Ej0cI1Vsh1AzK6QOFQGP7zJs36GyadLw2WsrR0aaMP9q76XQC4NBfH5RzHmZ3PZGjeUPpm9m30lU0iif9lfP755/ztb39j+vTpX7uPKVOmcOqpp9Ku3cE9qyZNmsS8efNITU0lEolw2WWXcc899xzRvfZPdfvVV19xyy23EI1GiUajXHLJJdx7771MnTqVDRs28Pvf//5rf64jwY+bwL05KLEgTLjJ0qzzBnxjspZSUuQvanTpW162vLEyi8/mY3DuYC7sfiFDcofQO7M3mvKj/gqS+B+HlBIpJYryzau1N4dhGEyZMoV+/fodksABHnnkES666CIikQh9+vThyiuvpHPnzod1L13XW6W6veqqq3j77bcZOHAghmGwebOV8fGcc85pzJfyfeBHzR5GSr6l8Y65+Wv3IaVkZ93OFoRdHrKiJ9Md6QzJHcLPev+MoXlD6Z7WHTXpH5zE94jSv/6V6MZN32qfjt69yPvDHw54fNeuXZx22mmMGDGC5cuXM3z4cJYuXYoQgj/96U9ccokV4VpfX89ZZ53Ftm3bGD9+PP/4xz9QFIVPP/2Ue+65h2g0SteuXfn3v/+N1+ulU6dOXHLJJcyaNYvbb7+dZcuWcfnll+NyuVi0aBGPPPII06ZNIxwOM3r0aP75z3+2ClKLRCx3W4/Hmktavnw5t99+O4FAgKysLKZMmUJ+fj7jxo1j0KBBLFiwgPPPP79Vqtvy8nLy8/MBKxlWA7FPmTKFZcuW8fTTTzNp0iRcLhcrV66kvLycl156iVdeeYVFixYxYsQIpkyZ8o2/ix83gZsGmnZkQ2BKk601W9sMS892ZbeIcuyS2uWIylAlkcT/CrZu3crLL79McXExzz33HKtXr6ayspJhw4YxduxYAJYsWcKGDRvo2LEjp59+Ov/9738ZN24ckydPZvbs2Xg8Hh566CEee+wx7r77bgAyMzNZsWIFAC+++GJjlR6Am266qfG8K664gunTpzemhb3jjjuYPHky27Zt4+abbyYnJ4d4PM6vf/1rPvzwQ7Kzs3nrrbf44x//yEsvJQpyx2I0RI5v3bq1Rarb2267jZ49ezJu3DhOP/10rrrqKpzO1t41NTU1LFq0iKlTp3LOOeewcOFCXnzxRYYNG8aqVasYNGjQNxrnHzeBS+OQxYwPFpbeztOOMe3GMDTPyiXS3tc+SdhJHFU4mKb8XaJjx46MHDmS2267jcsuuwxVVcnNzeXEE09k6dKlpKSkMHz48MZEVpdddhkLFizA6XSyYcMGxowZA1gkOmrUqMZ+G7T3tjB37lwefvhhQqEQ1dXV9O3bt5HAG0wogUCAk08+mS+//JKUlBTWrVvHhAkTAMss06BVH+ped999N5dffjmffvopr7/+Om+88Qaff/55q/POPvtshBD079+f3Nxc+ve3UgL37duXXbt2JQn8m8CQRiuTxuGEpTdo2V87LD2JJP7H0WCiOBj2V3aEEEgpmTBhAm+88cYR9RuJRLjxxhtZtmwZ7du359577200lzSH1+tl3LhxLFiwgDPOOIO+ffuyaNGir/UZunbtyi9/+Uuuu+46srOzqaqqanXOd51+9tudWTjGoJs6hmmwtHQpz65+lp9/8nNGvzGaK2ZewZMrnqQ0UMrELhN5eOzDzLl4DjMumMF9o+/j7K5nJ8k7iSQOAyeccAJvvfUWhmFQUVHB/PnzGT58OGCZUHbu3Ilpmrz11lscf/zxjBw5koULF7Jtm+VaGwwG2bJlS5t9N08/20DWWVlZBAKBA2Yz1HWdxYsX07VrV3r27ElFRUUjgcfjcdavbzuYfP9UtzNmzKAhEeDWrVtRVfV7L+YAP3IN3JQmXxR/wRfFXyAQ9Mzo+f2FpSeRxI8A559/PosWLWLgwIEIIXj44YfJy8tj06ZNDBs2jJtuuqlxEvP8889HURSmTJnCZZddRjRq5UuZPHkyPXr0aNX3pEmT+MUvftE4iXndddfRr18/8vLyGqvyNKDBBh6LxRrLpgkhePfdd7n55pupq6tD13VuvfVW+vbt2+pe+6e6/c9//sNtt92G2+1G0zRee+21gxaM+K7wtdPJfh0cbelk39/6PjvqdjRGOSbD0pP4X8DRmk42icPD95VO9pjH+d3P/6FFSCKJJJL42vhR28CTSCKJJI5lJAk8iST+B/F9mkaT+PZwpN9bksCTSOJ/DE6nk6qqqiSJH2OQUlJVVdVmQNCB8KO2gSeRxP8iCgsLKSoqoqKi4ocWJYkjhNPppLCw8LDPTxJ4Ekn8j8Fmsx12oqYkjm0kTShJJJFEEscokgSeRBJJJHGMIkngSSSRRBLHKL7XSEwhRAWw+5Anto0soPJbFOe7RlLe7w7HkqyQlPe7xLEkK3x9eTtKKbP3b/xeCfybQAixrK1Q0qMVSXm/OxxLskJS3u8Sx5Ks8O3LmzShJJFEEkkco0gSeBJJJJHEMYpjicCf/6EFOEIk5f3ucCzJCkl5v0scS7LCtyzvMWMDTyKJJJJIoiWOJQ08iSSSSCKJZkgSeBJJJJHEMYpjgsCFEKcLITYLIbYJIX7/Q8uzP4QQu4QQa4UQq4QQyxJtGUKIWUKIrYl1+g8o30tCiHIhxLpmbW3KJyz8PTHWa4QQg48See8VQhQnxniVEOLMZsfuSsi7WQhx2vcsa3shxFwhxAYhxHohxC2J9qNyfA8i79E6vk4hxBIhxOqEvPcl2jsLIRYn5HpLCGFPtDsS+9sSxzsdBbJOEULsbDa2gxLt3/y3IKU8qhdABbYDXQA7sBro80PLtZ+Mu4Cs/doeBn6f2P498NAPKN9YYDCw7lDyAWcCMwEBjAQWHyXy3gv8to1z+yR+Ew6gc+K3on6PsuYDgxPbPmBLQqajcnwPIu/ROr4C8Ca2bcDixLi9DVyaaH8O+GVi+0bgucT2pcBbR4GsU4CL2jj/G/8WjgUNfDiwTUq5Q0oZA94Ezv2BZTocnAu8nNh+GTjvhxJESjkfqN6v+UDynQu8Ii18BaQJIfK/F0ETOIC8B8K5wJtSyqiUciewDes3871ASlkipVyR2PYDG4ECjtLxPYi8B8IPPb5SShlI7NoSiwROAhpKz+8/vg3j/i5wshBC/MCyHgjf+LdwLBB4AbC32X4RB//B/RCQwKdCiOVCiOsTbblSypLEdimQ+8OIdkAcSL6jebxvSrxqvtTMJHXUyJt4XT8OS/M66sd3P3nhKB1fIYQqhFgFlAOzsN4CaqWUehsyNcqbOF4HZP5QskopG8b2/sTYPi6EcOwvawJHPLbHAoEfCzheSjkYOAP4lRBibPOD0npfOmr9NY92+RJ4FugKDAJKgEd/UGn2gxDCC7wH3CqlrG9+7Ggc3zbkPWrHV0ppSCkHAYVY2n+vH1aiA2N/WYUQ/YC7sGQeBmQAd35b9zsWCLwYaN9svzDRdtRASlmcWJcD72P9yMoaXocS6/IfTsI2cSD5jsrxllKWJf4cJvACTa/xP7i8QggbFhm+JqX8b6L5qB3ftuQ9mse3AVLKWmAuMArL3NBQkKa5TI3yJo6nAlXfr6QtZD09YbaSUsoo8G++xbE9Fgh8KdA9Metsx5qYmPoDy9QIIYRHCOFr2AZOBdZhyXhV4rSrgA9/GAkPiAPJNxW4MjFDPhKoa2YK+MGwn23wfKwxBkveSxPeB52B7sCS71EuAfwL2CilfKzZoaNyfA8k71E8vtlCiLTEtguYgGW3nwtclDht//FtGPeLgM8Sb0A/lKybmj3IBZatvvnYfrPfwvc1Q/tNFqzZ2i1Ytq8//tDy7CdbF6xZ+tXA+gb5sOxuc4CtwGwg4weU8Q2s1+I4lp3t2gPJhzUj/kxirNcCQ48Sef+TkGdN4oef3+z8Pybk3Qyc8T3LejyWeWQNsCqxnHm0ju9B5D1ax3cAsDIh1zrg7kR7F6wHyTbgHcCRaHcm9rcljnc5CmT9LDG264BXafJU+ca/hWQofRJJJJHEMYpjwYSSRBJJJJFEG0gSeBJJJJHEMYokgSeRRBJJHKNIEngSSSSRxDGKJIEnkUQSSRyjSBJ4EkkkkcQxiiSBJ5FEEkkco/h/tOY8rDxb7/AAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.sort_values(by=['Human (Mean)']).plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "prospective-international",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordsimDF1.plot.line()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "appointed-tablet",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# cosine_similarity([encodingsMap['Q847337']], [encodingsMap['Q551800']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "adaptive-bride",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "beautiful-wages",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "equivalent-wireless",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37556037504702056, pvalue=1.7903693658447951e-25)"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "informal-error",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3529686820781901, pvalue=1.0899920454225404e-22)"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "allied-vulnerability",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3156122362936971, pvalue=1.8520533516011967e-18)"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "victorian-seeking",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3583059753020537, pvalue=2.481962318957373e-23)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fallen-action",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "smart-musician",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "stock-agent",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5316995237579449, pvalue=7.350680368606645e-27)"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLargeSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "wrapped-batman",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5043971026064306, pvalue=6.35665360448366e-24)"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['distilBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "fallen-professor",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4564962071637959, pvalue=2.286865863469491e-19)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBaseBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "fewer-stock",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5120576203985243, pvalue=1.0133475563672188e-24)"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['robertaBertSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-dressing",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "royal-bahrain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "threatened-roman",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'wordsimQNodesListWAbstractsLabelsDF2' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-53506c913363>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwordsimQNodesListWAbstractsLabelsDF2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'wordsimQNodesListWAbstractsLabelsDF2' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "flexible-metropolitan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF[['Human (Mean)']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "superb-machinery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "excess-sight",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.580217361450195 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-13-d93d87c23bc8>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-      "<ipython-input-13-d93d87c23bc8>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "metropolitan-sunglasses",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "entire-branch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.25719783185089823, pvalue=9.11953585838357e-13)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "positive-clone",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.37036443456409784, pvalue=8.677074016866423e-13)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "choice-alfred",
-   "metadata": {},
-   "source": [
-    "# Amandeep's file generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "attached-persian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# q1 = \"kgtk query -i ../data/wikidataos.for.text-embedding.tsv.gz --match '(node1)-[]->(node2)' --where 'node1 in [\" + \",\".join(['\"'+word1+'\"' for word1 in wordsimQNodesListWAbstractsLabelsDF2.index.to_list()]) + \"]' -o ../data/wikidataos.for.text-embedding.subset2.tsv\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "fleet-finder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import os\n",
-    "# os.system(\"screen -dm \"+q1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "fancy-personality",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSDF = pd.read_csv('../data/wikidataos.for.text-embedding.subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "broke-grave",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>node2;wikidatatype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q104021-P1245-868aab-b3f18d7b-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1245</td>\n",
-       "      <td>1412</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q104021-P1296-7a59c3-5f49b5a7-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1296</td>\n",
-       "      <td>0108342</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>external-id</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q104021-P1343-Q19180675-e305e6f3-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q19180675</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q104021-P1343-Q2657718-2d01d189-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q2657718</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q104021-P1343-Q4091875-50bd890f-0</td>\n",
-       "      <td>Q104021</td>\n",
-       "      <td>P1343</td>\n",
-       "      <td>Q4091875</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>wikibase-item</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   id    node1  label      node2    rank  \\\n",
-       "0     Q104021-P1245-868aab-b3f18d7b-0  Q104021  P1245       1412  normal   \n",
-       "1     Q104021-P1296-7a59c3-5f49b5a7-0  Q104021  P1296    0108342  normal   \n",
-       "2  Q104021-P1343-Q19180675-e305e6f3-0  Q104021  P1343  Q19180675  normal   \n",
-       "3   Q104021-P1343-Q2657718-2d01d189-0  Q104021  P1343   Q2657718  normal   \n",
-       "4   Q104021-P1343-Q4091875-50bd890f-0  Q104021  P1343   Q4091875  normal   \n",
-       "\n",
-       "  node2;wikidatatype  \n",
-       "0        external-id  \n",
-       "1        external-id  \n",
-       "2      wikibase-item  \n",
-       "3      wikibase-item  \n",
-       "4      wikibase-item  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "behavioral-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk query -i ../data/wikidataos.for.text-embedding.subset2.tsv \\\n",
-    "#     --match '(node1)-[]->()' \\\n",
-    "#     --return 'count(distinct node1)'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "resistant-jungle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset2.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "humanitarian-divide",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(set(wordsimQNodesListWAbstractsLabelsDF2.index.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "unique-accuracy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF = pd.read_csv('../data/wikidataos-text-embeddings-subset2.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "smooth-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataOSEmbDF1 = wikidataOSEmbDF[wikidataOSEmbDF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "wired-surveillance",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.50568223,-0.10802102,-0.3486761,0.78598565,-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.4146536,0.36972183,-0.9731038,0.72235125,0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>0.085437864,0.50590533,-0.7758463,0.575578,1.1...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.50568223,-0.10802102,-0.3486761,0.78598565,-...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.4146536,0.36972183,-0.9731038,0.72235125,0....\n",
-       "8      Q9635  0.085437864,0.50590533,-0.7758463,0.575578,1.1..."
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataOSEmbDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "loved-serial",
-   "metadata": {},
-   "source": [
-    "# Version 2 - Comparison of cosine similarity measures with Amandeep's original file new pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "novel-milwaukee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "computational-vinyl",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-28-6aed93388a22>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = amanEncSim * 10\n",
-      "<ipython-input-28-6aed93388a22>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "amanEncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataOSEmbDF1.iterrows()}\n",
-    "amanEncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(amanEncMap, ))\n",
-    "wordsimDF1['amanSim'] = amanEncSim * 10\n",
-    "wordsimDF1['amanSim'] = wordsimDF1.amanSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "virtual-sodium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(amanEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "dated-delta",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3654839025870469, pvalue=3.731075991381434e-24)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "realistic-belize",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5075204852629356, pvalue=3.0234963712614295e-24)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['amanSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-armor",
-   "metadata": {},
-   "source": [
-    "# Version 3 - P31, P279 edges from lexicalized version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "empty-laugh",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# !kgtk text-embedding -i ../data/wikidataos.for.text-embedding.subset2.tsv   \\\n",
-    "#     --model roberta-large-nli-mean-tokens   \\\n",
-    "#     --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#     --isa-properties P31 P279  \\\n",
-    "#     --save-embedding-sentence > ../data/wikidataos-text-embeddings-subset3.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "biological-enhancement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = pd.read_csv('../data/wikidataos-text-embeddings-subset3.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "silent-logging",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikidataV3DF = wikidataV3DF[wikidataV3DF.property == 'text_embedding'].loc[:,['node','value']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "color-sword",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q998</td>\n",
-       "      <td>0.60533667,-0.32207027,-0.19160181,0.70176995,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q99496317</td>\n",
-       "      <td>-0.663889,1.3066527,0.7410443,0.7310645,-0.086...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q98069877</td>\n",
-       "      <td>0.79087067,0.6158045,0.18239851,-0.44384396,-0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q964401</td>\n",
-       "      <td>-0.3510175,0.29355207,-1.0211854,0.35043457,1....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9635</td>\n",
-       "      <td>-0.010522888,0.4303582,-0.76446587,0.26379517,...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                              value\n",
-       "0       Q998  0.60533667,-0.32207027,-0.19160181,0.70176995,...\n",
-       "2  Q99496317  -0.663889,1.3066527,0.7410443,0.7310645,-0.086...\n",
-       "4  Q98069877  0.79087067,0.6158045,0.18239851,-0.44384396,-0...\n",
-       "6    Q964401  -0.3510175,0.29355207,-1.0211854,0.35043457,1....\n",
-       "8      Q9635  -0.010522888,0.4303582,-0.76446587,0.26379517,..."
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikidataV3DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "essential-virtue",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-36-7dbb17313628>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-      "<ipython-input-36-7dbb17313628>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "wikiV3EncMap = {row['node']: json.loads('['+row['value']+']') for _, row in wikidataV3DF.iterrows()}\n",
-    "wikiV3EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(wikiV3EncMap, ))\n",
-    "wordsimDF1['wikiV3EncSim'] = wikiV3EncSim * 10\n",
-    "wordsimDF1['wikiV3EncSim'] = wordsimDF1.wikiV3EncSim.apply(lambda p: p[0][0] if p != 0 else p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "charming-indicator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(330, 349, 0.9455587392550143)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(wikiV3EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "dependent-sensitivity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.33429987563573715, pvalue=1.805960166533804e-20)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "fresh-correspondence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4737260118146468, pvalue=6.343179394616253e-21)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['wikiV3EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "specified-tomorrow",
-   "metadata": {},
-   "source": [
-    "# Version 4 - First sentence of abstract vs human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "opposite-discipline",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "single-antigua",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "structured-specialist",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.abstract.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "unnecessary-outdoors",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF1.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "breathing-purchase",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q213449</th>\n",
-       "      <td>Eating</td>\n",
-       "      <td>Q213449-wikipedia_sitelink-1e88be-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Eating</td>\n",
-       "      <td>104645.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Eating&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "      <td>eating</td>\n",
-       "      <td>Eating (also known as consuming) is the ingest...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              index                                    id               label  \\\n",
-       "node1                                                                           \n",
-       "Q8068         Flood     Q8068-wikipedia_sitelink-410669-0  wikipedia_sitelink   \n",
-       "Q1368         Money     Q1368-wikipedia_sitelink-59ade8-0  wikipedia_sitelink   \n",
-       "Q872          Radio      Q872-wikipedia_sitelink-1e49e4-0  wikipedia_sitelink   \n",
-       "Q5070802  Emergency  Q5070802-wikipedia_sitelink-41cebc-0  wikipedia_sitelink   \n",
-       "Q213449      Eating   Q213449-wikipedia_sitelink-1e88be-0  wikipedia_sitelink   \n",
-       "\n",
-       "                                           node2     ignore  \\\n",
-       "node1                                                         \n",
-       "Q8068         http://en.wikipedia.org/wiki/Flood    23841.0   \n",
-       "Q1368         http://en.wikipedia.org/wiki/Money  1312979.0   \n",
-       "Q872          http://en.wikipedia.org/wiki/Radio  1866867.0   \n",
-       "Q5070802  http://en.wikipedia.org/wiki/Emergency    80429.0   \n",
-       "Q213449      http://en.wikipedia.org/wiki/Eating   104645.0   \n",
-       "\n",
-       "                                              url  \\\n",
-       "node1                                               \n",
-       "Q8068         <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368         <http://dbpedia.org/resource/Money>   \n",
-       "Q872          <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802  <http://dbpedia.org/resource/Emergency>   \n",
-       "Q213449      <http://dbpedia.org/resource/Eating>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q213449   <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract node2_labelsDF  \\\n",
-       "node1                                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...          flood   \n",
-       "Q1368     Money is any item or verifiable record that is...          money   \n",
-       "Q872      Radio is the technology of signaling and commu...          radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...      emergency   \n",
-       "Q213449   Eating (also known as consuming) is the ingest...         eating   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  \n",
-       "Q213449   Eating (also known as consuming) is the ingest...  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "surgical-title",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "grateful-harassment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getFirstSentEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getFirstSentMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "owned-helen",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.5808649063110352 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-47-9075dca18092>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-      "<ipython-input-47-9075dca18092>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV4Enc = getFirstSentEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV4EncMap = getFirstSentMap(bertV4Enc)\n",
-    "bertV4EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV4EncMap, ))\n",
-    "wordsimDF1['bertV4EncSim'] = bertV4EncSim\n",
-    "wordsimDF1['bertV4EncSim'] = wordsimDF1.bertV4EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV4EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "christian-median",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(239, 349, 0.6848137535816619)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV4EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "nervous-cycling",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548\n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040\n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217\n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000\n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "retired-cookie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "future-boring",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3633652092919623, pvalue=2.1070516667335453e-22)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "composite-lebanon",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.49261494527923566, pvalue=9.798975953928462e-23)"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV4EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupational-rough",
-   "metadata": {},
-   "source": [
-    "# Version 5 - DBPedia's short abstracts vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "effective-elements",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "graduate-congress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "little-yukon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF1.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF1.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "choice-seating",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8821778297424316 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-56-da3fd1f5e2ad>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV5EncSim'] = bertV5EncSim\n",
-      "<ipython-input-56-da3fd1f5e2ad>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV5EncSim'] = wordsimDF1.bertV5EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV5Enc = getAbsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV5EncMap = getAbsMap(bertV5Enc)\n",
-    "bertV5EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV5EncMap, ))\n",
-    "wordsimDF1['bertV5EncSim'] = bertV5EncSim\n",
-    "wordsimDF1['bertV5EncSim'] = wordsimDF1.bertV5EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV5EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "computational-crime",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(239, 349, 0.6848137535816619)"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV5EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "durable-import",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  \n",
-       "0      4.268633  \n",
-       "1      3.399217  \n",
-       "2      6.865782  \n",
-       "3      0.000000  \n",
-       "4      0.000000  "
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "champion-waste",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "pretty-observation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3628353158835648, pvalue=2.428880959404515e-22)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "adverse-calculator",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4963804164345332, pvalue=4.1353639478514766e-23)"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV5EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sexual-leone",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "elder-refund",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                     school holiday  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "inclusive-gender",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "dental-choice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "understanding-insured",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "political-manufacturer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.902165412902832 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-66-ef38b58ef6d6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-      "<ipython-input-66-ef38b58ef6d6>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "oriental-olympus",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "simplified-recipient",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  \n",
-       "0      4.268633      4.268633  \n",
-       "1      3.399217      3.399217  \n",
-       "2      6.865782      6.865782  \n",
-       "3      0.000000      2.961755  \n",
-       "4      0.000000      3.408112  "
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "convenient-glory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "split-doctor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "criminal-tragedy",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "certified-compact",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "billion-southwest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "relevant-looking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "based-duplicate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \\\n",
-       "node1                                                                         \n",
-       "Q1514190                                     school holiday  school holiday   \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood   \n",
-       "Q1368     Money is any item or verifiable record that is...           money   \n",
-       "Q872      Radio is the technology of signaling and commu...           radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q1514190                                     school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  "
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "starting-parcel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "independent-consultancy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48781871795654297 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-76-ef0e3d1b2360>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-      "<ipython-input-76-ef0e3d1b2360>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "incomplete-dispute",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "pregnant-message",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  bertV7EncSim  \n",
-       "0      4.268633      4.268633      3.770548  \n",
-       "1      3.399217      3.399217      4.168040  \n",
-       "2      6.865782      6.865782      4.997217  \n",
-       "3      0.000000      2.961755      5.296330  \n",
-       "4      0.000000      3.408112      5.621890  "
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "broke-oregon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "recent-moment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3793557735858387, pvalue=5.886665529373508e-26)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "varying-prescription",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5330950015966213, pvalue=5.11634460488722e-27)"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accessible-serum",
-   "metadata": {},
-   "source": [
-    "# Version 8, 9, 10 - complex, transe, text, class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "japanese-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-12-99910acabed9>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-      "<ipython-input-12-99910acabed9>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-      "<ipython-input-12-99910acabed9>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['text'] = wordsimDF.text * 10\n",
-      "<ipython-input-12-99910acabed9>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['class'] = wordsimDF['class'] * 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordsimDF1['complex'] = wordsimDF.complex * 10\n",
-    "wordsimDF1['transe'] = wordsimDF.transe * 10\n",
-    "wordsimDF1['text'] = wordsimDF.text * 10\n",
-    "wordsimDF1['class'] = wordsimDF['class'] * 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "fitting-jamaica",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>0.361115</td>\n",
-       "      <td>0.446632</td>\n",
-       "      <td>0.485653</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>0.302163</td>\n",
-       "      <td>0.274388</td>\n",
-       "      <td>0.479313</td>\n",
-       "      <td>0.005851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>0.694462</td>\n",
-       "      <td>0.429289</td>\n",
-       "      <td>0.626784</td>\n",
-       "      <td>0.393657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>0.324825</td>\n",
-       "      <td>0.379077</td>\n",
-       "      <td>0.618644</td>\n",
-       "      <td>0.208812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>0.361214</td>\n",
-       "      <td>0.360383</td>\n",
-       "      <td>0.863846</td>\n",
-       "      <td>0.888621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  \n",
-       "0        5.5360  0.361115  0.446632  0.485653       NaN  \n",
-       "1        4.1250  0.302163  0.274388  0.479313  0.005851  \n",
-       "2        6.6250  0.694462  0.429289  0.626784  0.393657  \n",
-       "3        2.0625  0.324825  0.379077  0.618644  0.208812  \n",
-       "4        7.1875  0.361214  0.360383  0.863846  0.888621  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "educational-phoenix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF1 = wordsimDF1.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "classical-bandwidth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>3.611154</td>\n",
-       "      <td>4.466324</td>\n",
-       "      <td>4.856529</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.021635</td>\n",
-       "      <td>2.743883</td>\n",
-       "      <td>4.793135</td>\n",
-       "      <td>0.058511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.944618</td>\n",
-       "      <td>4.292894</td>\n",
-       "      <td>6.267841</td>\n",
-       "      <td>3.936571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>3.248249</td>\n",
-       "      <td>3.790767</td>\n",
-       "      <td>6.186442</td>\n",
-       "      <td>2.088125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.612144</td>\n",
-       "      <td>3.603834</td>\n",
-       "      <td>8.638458</td>\n",
-       "      <td>8.886211</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)   complex    transe      text     class\n",
-       "0        5.5360  3.611154  4.466324  4.856529  0.000000\n",
-       "1        4.1250  3.021635  2.743883  4.793135  0.058511\n",
-       "2        6.6250  6.944618  4.292894  6.267841  3.936571\n",
-       "3        2.0625  3.248249  3.790767  6.186442  2.088125\n",
-       "4        7.1875  3.612144  3.603834  8.638458  8.886211"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "amazing-expense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.052735</td>\n",
-       "      <td>4.286958</td>\n",
-       "      <td>3.438595</td>\n",
-       "      <td>5.578697</td>\n",
-       "      <td>1.937397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.044971</td>\n",
-       "      <td>1.645163</td>\n",
-       "      <td>2.033467</td>\n",
-       "      <td>2.021548</td>\n",
-       "      <td>2.753170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.312500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1.436633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.375000</td>\n",
-       "      <td>3.317798</td>\n",
-       "      <td>2.151662</td>\n",
-       "      <td>4.626498</td>\n",
-       "      <td>0.145023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.937500</td>\n",
-       "      <td>4.171686</td>\n",
-       "      <td>3.240109</td>\n",
-       "      <td>5.767745</td>\n",
-       "      <td>0.639914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5.375000</td>\n",
-       "      <td>4.996030</td>\n",
-       "      <td>4.731990</td>\n",
-       "      <td>6.812977</td>\n",
-       "      <td>2.350565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Human (Mean)     complex      transe        text       class\n",
-       "count    349.000000  349.000000  349.000000  349.000000  349.000000\n",
-       "mean       4.052735    4.286958    3.438595    5.578697    1.937397\n",
-       "std        2.044971    1.645163    2.033467    2.021548    2.753170\n",
-       "min        0.312500    0.000000   -1.436633    0.000000    0.000000\n",
-       "25%        2.375000    3.317798    2.151662    4.626498    0.145023\n",
-       "50%        3.937500    4.171686    3.240109    5.767745    0.639914\n",
-       "75%        5.375000    4.996030    4.731990    6.812977    2.350565\n",
-       "max       10.000000   10.000000   10.000000   10.000000   10.000000"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "headed-reality",
-   "metadata": {},
-   "source": [
-    "# Agreement Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "typical-highland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "versionNames = ['bertLabelsSim', 'amanSim', 'wikiV3EncSim', 'bertV4EncSim', 'bertV5EncSim', 'bertV6EncSim', 'bertV7EncSim', 'complex', 'transe', 'text', 'class']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "irish-yesterday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAM = []\n",
-    "spearmanAM = []\n",
-    "for ver in versionNames:\n",
-    "    kAMTemp = []\n",
-    "    sAMTemp = []\n",
-    "    for ver2 in versionNames:\n",
-    "        kAMTemp.append(stats.kendalltau(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "        sAMTemp.append(stats.spearmanr(wordsimDF1[ver], wordsimDF1[ver2]).correlation)\n",
-    "    kendallAM.append(kAMTemp)\n",
-    "    spearmanAM.append(sAMTemp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "massive-optimum",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF = pd.DataFrame(kendallAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "higher-shirt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
-       "      <th>Version 2</th>\n",
-       "      <th>Version 3</th>\n",
-       "      <th>Version 4</th>\n",
-       "      <th>Version 5</th>\n",
-       "      <th>Version 6</th>\n",
-       "      <th>Version 7</th>\n",
-       "      <th>Version 8</th>\n",
-       "      <th>Version 9</th>\n",
-       "      <th>Version 10</th>\n",
-       "      <th>Version 11</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Version 1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.174948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 2</th>\n",
-       "      <td>0.412089</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.238586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 3</th>\n",
-       "      <td>0.383523</td>\n",
-       "      <td>0.838947</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.235619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 4</th>\n",
-       "      <td>0.160567</td>\n",
-       "      <td>0.296893</td>\n",
-       "      <td>0.275386</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.259318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 5</th>\n",
-       "      <td>0.146498</td>\n",
-       "      <td>0.266232</td>\n",
-       "      <td>0.250581</td>\n",
-       "      <td>0.820504</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.248043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 6</th>\n",
-       "      <td>0.287018</td>\n",
-       "      <td>0.369277</td>\n",
-       "      <td>0.351252</td>\n",
-       "      <td>0.611740</td>\n",
-       "      <td>0.782150</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.220891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 7</th>\n",
-       "      <td>0.383673</td>\n",
-       "      <td>0.458566</td>\n",
-       "      <td>0.429853</td>\n",
-       "      <td>0.619886</td>\n",
-       "      <td>0.449513</td>\n",
-       "      <td>0.650565</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.241190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.208911</td>\n",
-       "      <td>0.309217</td>\n",
-       "      <td>0.304272</td>\n",
-       "      <td>0.229670</td>\n",
-       "      <td>0.246373</td>\n",
-       "      <td>0.265199</td>\n",
-       "      <td>0.260021</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.334855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
-       "      <td>0.209933</td>\n",
-       "      <td>0.247839</td>\n",
-       "      <td>0.249097</td>\n",
-       "      <td>0.135532</td>\n",
-       "      <td>0.143346</td>\n",
-       "      <td>0.201692</td>\n",
-       "      <td>0.239819</td>\n",
-       "      <td>0.447656</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>0.255377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 10</th>\n",
-       "      <td>0.400400</td>\n",
-       "      <td>0.912377</td>\n",
-       "      <td>0.839894</td>\n",
-       "      <td>0.299409</td>\n",
-       "      <td>0.269472</td>\n",
-       "      <td>0.368089</td>\n",
-       "      <td>0.453371</td>\n",
-       "      <td>0.309583</td>\n",
-       "      <td>0.244593</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.232037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 11</th>\n",
-       "      <td>0.174948</td>\n",
-       "      <td>0.238586</td>\n",
-       "      <td>0.235619</td>\n",
-       "      <td>0.259318</td>\n",
-       "      <td>0.248043</td>\n",
-       "      <td>0.220891</td>\n",
-       "      <td>0.241190</td>\n",
-       "      <td>0.334855</td>\n",
-       "      <td>0.255377</td>\n",
-       "      <td>0.232037</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
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-       "Version 6    0.287018   0.369277   0.351252   0.611740   0.782150   1.000000   \n",
-       "Version 7    0.383673   0.458566   0.429853   0.619886   0.449513   0.650565   \n",
-       "Version 8    0.208911   0.309217   0.304272   0.229670   0.246373   0.265199   \n",
-       "Version 9    0.209933   0.247839   0.249097   0.135532   0.143346   0.201692   \n",
-       "Version 10   0.400400   0.912377   0.839894   0.299409   0.269472   0.368089   \n",
-       "Version 11   0.174948   0.238586   0.235619   0.259318   0.248043   0.220891   \n",
-       "\n",
-       "            Version 7  Version 8  Version 9  Version 10  Version 11  \n",
-       "Version 1    0.383673   0.208911   0.209933    0.400400    0.174948  \n",
-       "Version 2    0.458566   0.309217   0.247839    0.912377    0.238586  \n",
-       "Version 3    0.429853   0.304272   0.249097    0.839894    0.235619  \n",
-       "Version 4    0.619886   0.229670   0.135532    0.299409    0.259318  \n",
-       "Version 5    0.449513   0.246373   0.143346    0.269472    0.248043  \n",
-       "Version 6    0.650565   0.265199   0.201692    0.368089    0.220891  \n",
-       "Version 7    1.000000   0.260021   0.239819    0.453371    0.241190  \n",
-       "Version 8    0.260021   1.000000   0.447656    0.309583    0.334855  \n",
-       "Version 9    0.239819   0.447656   1.000000    0.244593    0.255377  \n",
-       "Version 10   0.453371   0.309583   0.244593    1.000000    0.232037  \n",
-       "Version 11   0.241190   0.334855   0.255377    0.232037    1.000000  "
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kendallAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "distant-level",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spearmanAMDF = pd.DataFrame(spearmanAM, columns=['Version '+str(i+1) for i in range(len(versionNames))], index=['Version '+str(i+1) for i in range(len(versionNames))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "skilled-battery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Version 1</th>\n",
-       "      <th>Version 2</th>\n",
-       "      <th>Version 3</th>\n",
-       "      <th>Version 4</th>\n",
-       "      <th>Version 5</th>\n",
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-       "      <th>Version 11</th>\n",
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-       "      <td>0.350908</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 3</th>\n",
-       "      <td>0.539416</td>\n",
-       "      <td>0.959002</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.371106</td>\n",
-       "      <td>0.337087</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.596281</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.352982</td>\n",
-       "      <td>0.959131</td>\n",
-       "      <td>0.336037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Version 4</th>\n",
-       "      <td>0.213744</td>\n",
-       "      <td>0.392378</td>\n",
-       "      <td>0.371106</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.946527</td>\n",
-       "      <td>0.733577</td>\n",
-       "      <td>0.628222</td>\n",
-       "      <td>0.325008</td>\n",
-       "      <td>0.189124</td>\n",
-       "      <td>0.399380</td>\n",
-       "      <td>0.355801</td>\n",
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-       "      <th>Version 5</th>\n",
-       "      <td>0.192074</td>\n",
-       "      <td>0.350908</td>\n",
-       "      <td>0.337087</td>\n",
-       "      <td>0.946527</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.356645</td>\n",
-       "      <td>0.343931</td>\n",
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-       "      <th>Version 6</th>\n",
-       "      <td>0.400162</td>\n",
-       "      <td>0.515853</td>\n",
-       "      <td>0.493746</td>\n",
-       "      <td>0.733577</td>\n",
-       "      <td>0.793177</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.298756</td>\n",
-       "      <td>0.513084</td>\n",
-       "      <td>0.321041</td>\n",
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-       "      <th>Version 7</th>\n",
-       "      <td>0.532950</td>\n",
-       "      <td>0.628626</td>\n",
-       "      <td>0.596281</td>\n",
-       "      <td>0.628222</td>\n",
-       "      <td>0.556256</td>\n",
-       "      <td>0.829959</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>0.347957</td>\n",
-       "      <td>0.624048</td>\n",
-       "      <td>0.345162</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 8</th>\n",
-       "      <td>0.301924</td>\n",
-       "      <td>0.440326</td>\n",
-       "      <td>0.430792</td>\n",
-       "      <td>0.325008</td>\n",
-       "      <td>0.343765</td>\n",
-       "      <td>0.381097</td>\n",
-       "      <td>0.373446</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.619056</td>\n",
-       "      <td>0.439102</td>\n",
-       "      <td>0.477997</td>\n",
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-       "    <tr>\n",
-       "      <th>Version 9</th>\n",
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-       "      <td>0.352982</td>\n",
-       "      <td>0.189124</td>\n",
-       "      <td>0.203396</td>\n",
-       "      <td>0.298756</td>\n",
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-       "      <td>0.619056</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>0.364509</td>\n",
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-       "    <tr>\n",
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-       "      <td>0.985972</td>\n",
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-       "      <td>0.356645</td>\n",
-       "      <td>0.513084</td>\n",
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-       "      <td>0.439102</td>\n",
-       "      <td>0.346778</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.333301</td>\n",
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-       "    <tr>\n",
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-       "      <td>0.339260</td>\n",
-       "      <td>0.336037</td>\n",
-       "      <td>0.355801</td>\n",
-       "      <td>0.343931</td>\n",
-       "      <td>0.321041</td>\n",
-       "      <td>0.345162</td>\n",
-       "      <td>0.477997</td>\n",
-       "      <td>0.364509</td>\n",
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-       "            Version 1  Version 2  Version 3  Version 4  Version 5  Version 6  \\\n",
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-       "Version 2    0.576425   1.000000   0.959002   0.392378   0.350908   0.515853   \n",
-       "Version 3    0.539416   0.959002   1.000000   0.371106   0.337087   0.493746   \n",
-       "Version 4    0.213744   0.392378   0.371106   1.000000   0.946527   0.733577   \n",
-       "Version 5    0.192074   0.350908   0.337087   0.946527   1.000000   0.793177   \n",
-       "Version 6    0.400162   0.515853   0.493746   0.733577   0.793177   1.000000   \n",
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-       "Version 7    1.000000   0.373446   0.347957    0.624048    0.345162  \n",
-       "Version 8    0.373446   1.000000   0.619056    0.439102    0.477997  \n",
-       "Version 9    0.347957   0.619056   1.000000    0.346778    0.364509  \n",
-       "Version 10   0.624048   0.439102   0.346778    1.000000    0.333301  \n",
-       "Version 11   0.345162   0.477997   0.364509    0.333301    1.000000  "
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spearmanAMDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "indoor-coordinator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kendallAMDF.to_csv('../data/kendallAMDF.csv')\n",
-    "spearmanAMDF.to_csv('../data/spearmanAMDF.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "innocent-prerequisite",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "active-leisure",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "boolean-impossible",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suitable-brazil",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "desirable-collect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for ver in versionNames:\n",
-    "    wordsimDF[ver] = wordsimDF1[ver]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "flexible-fifth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word1_kg_label</th>\n",
-       "      <th>word1_kg_description</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>word2_kg_label</th>\n",
-       "      <th>word2_kg_description</th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>admission</td>\n",
-       "      <td>Q847337</td>\n",
-       "      <td>confession</td>\n",
-       "      <td>statement made by a person or a group of perso...</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>Q551800</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>paper or cardboard document showing payment fo...</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>3.611154</td>\n",
-       "      <td>4.466324</td>\n",
-       "      <td>4.856529</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>Q154</td>\n",
-       "      <td>alcoholic beverage</td>\n",
-       "      <td>drink containing alcohols, typically ethanol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>Q2329</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>branch of physical science concerned with the ...</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>3.021635</td>\n",
-       "      <td>2.743883</td>\n",
-       "      <td>4.793135</td>\n",
-       "      <td>0.058511</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>Q663</td>\n",
-       "      <td>aluminium</td>\n",
-       "      <td>metallic chemical element of silvery appearanc...</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>Q11426</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>element, compound or alloy that is a good cond...</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>6.944618</td>\n",
-       "      <td>4.292894</td>\n",
-       "      <td>6.267841</td>\n",
-       "      <td>3.936571</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>Q14536140</td>\n",
-       "      <td>exertion</td>\n",
-       "      <td>use of physical or perceived energy by a person</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>3.248249</td>\n",
-       "      <td>3.790767</td>\n",
-       "      <td>6.186442</td>\n",
-       "      <td>2.088125</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>announcement</td>\n",
-       "      <td>Q567303</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>printed, spoken, or published statement or not...</td>\n",
-       "      <td>news</td>\n",
-       "      <td>Q38926</td>\n",
-       "      <td>news</td>\n",
-       "      <td>communication of selected information on curre...</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>3.612144</td>\n",
-       "      <td>3.603834</td>\n",
-       "      <td>8.638458</td>\n",
-       "      <td>8.886211</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Word 1 word1_kg_id      word1_kg_label  \\\n",
-       "0     admission     Q847337          confession   \n",
-       "1       alcohol        Q154  alcoholic beverage   \n",
-       "2      aluminum        Q663           aluminium   \n",
-       "3  announcement     Q567303        announcement   \n",
-       "4  announcement     Q567303        announcement   \n",
-       "\n",
-       "                                word1_kg_description     Word 2 word2_kg_id  \\\n",
-       "0  statement made by a person or a group of perso...     ticket     Q551800   \n",
-       "1       drink containing alcohols, typically ethanol  chemistry       Q2329   \n",
-       "2  metallic chemical element of silvery appearanc...      metal      Q11426   \n",
-       "3  printed, spoken, or published statement or not...     effort   Q14536140   \n",
-       "4  printed, spoken, or published statement or not...       news      Q38926   \n",
-       "\n",
-       "  word2_kg_label                               word2_kg_description  \\\n",
-       "0         ticket  paper or cardboard document showing payment fo...   \n",
-       "1      chemistry  branch of physical science concerned with the ...   \n",
-       "2          metal  element, compound or alloy that is a good cond...   \n",
-       "3       exertion    use of physical or perceived energy by a person   \n",
-       "4           news  communication of selected information on curre...   \n",
-       "\n",
-       "   Human (Mean)   complex    transe      text     class  bertLabelsSim  \\\n",
-       "0        5.5360  3.611154  4.466324  4.856529  0.000000       6.809672   \n",
-       "1        4.1250  3.021635  2.743883  4.793135  0.058511       4.554877   \n",
-       "2        6.6250  6.944618  4.292894  6.267841  3.936571       7.643815   \n",
-       "3        2.0625  3.248249  3.790767  6.186442  2.088125       7.194899   \n",
-       "4        7.1875  3.612144  3.603834  8.638458  8.886211       8.137879   \n",
-       "\n",
-       "    amanSim  wikiV3EncSim  bertV4EncSim  bertV5EncSim  bertV6EncSim  \\\n",
-       "0  4.949738      5.145601      3.770548      4.268633      4.268633   \n",
-       "1  4.818231      3.987047      4.168040      3.399217      3.399217   \n",
-       "2  6.407051      5.669631      4.997217      6.865782      6.865782   \n",
-       "3  6.186497      6.186497      0.000000      0.000000      2.961755   \n",
-       "4  8.556664      9.098408      0.000000      0.000000      3.408112   \n",
-       "\n",
-       "   bertV7EncSim  \n",
-       "0      3.770548  \n",
-       "1      4.168040  \n",
-       "2      4.997217  \n",
-       "3      5.296330  \n",
-       "4      5.621890  "
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "ambient-netherlands",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDF.to_csv('../data/wordsim_updated.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "careful-pharmacy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "domestic-hello",
-   "metadata": {},
-   "source": [
-    "# Part 2 - Pedro's updated file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "dated-lender",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimDFNew = pd.read_csv('../data/SimIP_new.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "patient-processor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(set(wordsimDFNew['g_qnode'].to_list() + wordsimDFNew['p_qnode'].to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "southern-today",
-   "metadata": {},
-   "source": [
-    "# Version 1 - Embeddings of Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "theoretical-myanmar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cutting-construction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getV1Embeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.node2_labelsDF.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "def getCoverage(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dominant-traveler",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.580217361450195 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-13-d93d87c23bc8>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-      "<ipython-input-13-d93d87c23bc8>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertLabelsEnc = getV1Embeddings('bert-base-nli-mean-tokens')\n",
-    "bertLabelsEncMap = getMap(bertLabelsEnc)\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF.apply(checkSimilarity, axis=1, args=(bertLabelsEncMap, ))\n",
-    "wordsimDF1['bertLabelsSim'] = wordsimDF1.bertLabelsSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertLabelsSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "equivalent-indicator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertLabelsEncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "verbal-karma",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.25719783185089823, pvalue=9.11953585838357e-13)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "charitable-collaboration",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.37036443456409784, pvalue=8.677074016866423e-13)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertLabelsSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changing-brazil",
-   "metadata": {},
-   "source": [
-    "# Version 6 - DBPedia's short abstracts + labels vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "accessible-potential",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \n",
-       "node1                                                                        \n",
-       "Q1514190                                     school holiday  school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood  \n",
-       "Q1368     Money is any item or verifiable record that is...           money  \n",
-       "Q872      Radio is the technology of signaling and commu...           radio  \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency  "
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "stainless-promotion",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "418"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordsimQNodesListWAbstractsLabelsDF2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "owned-strap",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "hollow-consumer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAbsLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstract.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def getAbsLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "leading-terrorism",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.902165412902832 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-66-ef38b58ef6d6>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-      "<ipython-input-66-ef38b58ef6d6>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV6Enc = getAbsLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV6EncMap = getAbsLabsMap(bertV6Enc)\n",
-    "bertV6EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV6EncMap, ))\n",
-    "wordsimDF1['bertV6EncSim'] = bertV6EncSim\n",
-    "wordsimDF1['bertV6EncSim'] = wordsimDF1.bertV6EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV6EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "suitable-might",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV6EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "saving-harvey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  \n",
-       "0      4.268633      4.268633  \n",
-       "1      3.399217      3.399217  \n",
-       "2      6.865782      6.865782  \n",
-       "3      0.000000      2.961755  \n",
-       "4      0.000000      3.408112  "
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "accompanied-release",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "coordinate-display",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.37975882843235137, pvalue=5.235465664921023e-26)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "weighted-istanbul",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5388523544692222, pvalue=1.1269721214604893e-27)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV6EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integrated-marina",
-   "metadata": {},
-   "source": [
-    "# Version 7 - 1st sentence + label vs Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "limiting-clothing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from nltk.tokenize import sent_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "patient-groove",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2['abstractFirstSent'] = wordsimQNodesListWAbstractsLabelsDF2.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "thrown-initial",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>node2_labelsDF</th>\n",
-       "      <th>abstractFirstSent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>node1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Q1514190</th>\n",
-       "      <td>School_holiday</td>\n",
-       "      <td>Q1514190-wikipedia_sitelink-a7b9f1-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/School_holiday</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "      <td>school holiday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q8068</th>\n",
-       "      <td>Flood</td>\n",
-       "      <td>Q8068-wikipedia_sitelink-410669-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Flood</td>\n",
-       "      <td>23841.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Flood&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "      <td>flood</td>\n",
-       "      <td>A flood is an overflow of water that submerges...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q1368</th>\n",
-       "      <td>Money</td>\n",
-       "      <td>Q1368-wikipedia_sitelink-59ade8-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Money</td>\n",
-       "      <td>1312979.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Money&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "      <td>money</td>\n",
-       "      <td>Money is any item or verifiable record that is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q872</th>\n",
-       "      <td>Radio</td>\n",
-       "      <td>Q872-wikipedia_sitelink-1e49e4-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Radio</td>\n",
-       "      <td>1866867.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Radio&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "      <td>radio</td>\n",
-       "      <td>Radio is the technology of signaling and commu...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q5070802</th>\n",
-       "      <td>Emergency</td>\n",
-       "      <td>Q5070802-wikipedia_sitelink-41cebc-0</td>\n",
-       "      <td>wikipedia_sitelink</td>\n",
-       "      <td>http://en.wikipedia.org/wiki/Emergency</td>\n",
-       "      <td>80429.0</td>\n",
-       "      <td>&lt;http://dbpedia.org/resource/Emergency&gt;</td>\n",
-       "      <td>&lt;http://www.w3.org/2000/01/rdf-schema#comment&gt;</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "      <td>emergency</td>\n",
-       "      <td>An emergency is a situation that poses an imme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index                                    id  \\\n",
-       "node1                                                            \n",
-       "Q1514190  School_holiday  Q1514190-wikipedia_sitelink-a7b9f1-0   \n",
-       "Q8068              Flood     Q8068-wikipedia_sitelink-410669-0   \n",
-       "Q1368              Money     Q1368-wikipedia_sitelink-59ade8-0   \n",
-       "Q872               Radio      Q872-wikipedia_sitelink-1e49e4-0   \n",
-       "Q5070802       Emergency  Q5070802-wikipedia_sitelink-41cebc-0   \n",
-       "\n",
-       "                       label                                        node2  \\\n",
-       "node1                                                                       \n",
-       "Q1514190  wikipedia_sitelink  http://en.wikipedia.org/wiki/School_holiday   \n",
-       "Q8068     wikipedia_sitelink           http://en.wikipedia.org/wiki/Flood   \n",
-       "Q1368     wikipedia_sitelink           http://en.wikipedia.org/wiki/Money   \n",
-       "Q872      wikipedia_sitelink           http://en.wikipedia.org/wiki/Radio   \n",
-       "Q5070802  wikipedia_sitelink       http://en.wikipedia.org/wiki/Emergency   \n",
-       "\n",
-       "             ignore                                      url  \\\n",
-       "node1                                                          \n",
-       "Q1514190        NaN                                      NaN   \n",
-       "Q8068       23841.0      <http://dbpedia.org/resource/Flood>   \n",
-       "Q1368     1312979.0      <http://dbpedia.org/resource/Money>   \n",
-       "Q872      1866867.0      <http://dbpedia.org/resource/Radio>   \n",
-       "Q5070802    80429.0  <http://dbpedia.org/resource/Emergency>   \n",
-       "\n",
-       "                                                 ignore2  \\\n",
-       "node1                                                      \n",
-       "Q1514190                                             NaN   \n",
-       "Q8068     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q1368     <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q872      <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "Q5070802  <http://www.w3.org/2000/01/rdf-schema#comment>   \n",
-       "\n",
-       "                                                   abstract  node2_labelsDF  \\\n",
-       "node1                                                                         \n",
-       "Q1514190                                     school holiday  school holiday   \n",
-       "Q8068     A flood is an overflow of water that submerges...           flood   \n",
-       "Q1368     Money is any item or verifiable record that is...           money   \n",
-       "Q872      Radio is the technology of signaling and commu...           radio   \n",
-       "Q5070802  An emergency is a situation that poses an imme...       emergency   \n",
-       "\n",
-       "                                          abstractFirstSent  \n",
-       "node1                                                        \n",
-       "Q1514190                                     school holiday  \n",
-       "Q8068     A flood is an overflow of water that submerges...  \n",
-       "Q1368     Money is any item or verifiable record that is...  \n",
-       "Q872      Radio is the technology of signaling and commu...  \n",
-       "Q5070802  An emergency is a situation that poses an imme...  "
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimQNodesListWAbstractsLabelsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "fifty-nashville",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get1stSentLabsEmbeddings(modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(wordsimQNodesListWAbstractsLabelsDF2.abstractFirstSent.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "\n",
-    "def get1stSentLabsMap(encodings):\n",
-    "    return {qnode: enc for qnode, enc in zip(wordsimQNodesListWAbstractsLabelsDF2.index.to_list(), encodings)}\n",
-    "\n",
-    "def checkSimilarity(row, encodingsMap):\n",
-    "    if row['word1_kg_id'] in encodingsMap and row['word2_kg_id'] in encodingsMap:\n",
-    "        return cosine_similarity([encodingsMap[row['word1_kg_id']]], [encodingsMap[row['word2_kg_id']]])\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "stylish-encounter",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48781871795654297 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-76-ef0e3d1b2360>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-      "<ipython-input-76-ef0e3d1b2360>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
-     ]
-    }
-   ],
-   "source": [
-    "bertV7Enc = get1stSentLabsEmbeddings('bert-base-nli-mean-tokens')\n",
-    "bertV7EncMap = get1stSentLabsMap(bertV7Enc)\n",
-    "bertV7EncSim = wordsimDF.apply(checkSimilarity, axis=1, args=(bertV7EncMap, ))\n",
-    "wordsimDF1['bertV7EncSim'] = bertV7EncSim\n",
-    "wordsimDF1['bertV7EncSim'] = wordsimDF1.bertV7EncSim.apply(lambda p: p[0][0] if p != 0 else p)\n",
-    "wordsimDF1.bertV7EncSim *= 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "cheap-anaheim",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(345, 349, 0.9885386819484241)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnt = wordsimDF.apply(getCoverage, axis=1, args=(bertV7EncMap, )).sum()\n",
-    "cnt, len(wordsimDF), cnt / len(wordsimDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "conscious-remove",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Human (Mean)</th>\n",
-       "      <th>bertLabelsSim</th>\n",
-       "      <th>amanSim</th>\n",
-       "      <th>wikiV3EncSim</th>\n",
-       "      <th>bertV4EncSim</th>\n",
-       "      <th>bertV5EncSim</th>\n",
-       "      <th>bertV6EncSim</th>\n",
-       "      <th>bertV7EncSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>3.770548</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>4.168040</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>4.997217</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Human (Mean)  bertLabelsSim   amanSim  wikiV3EncSim  bertV4EncSim  \\\n",
-       "0        5.5360       6.809672  4.949738      5.145601      3.770548   \n",
-       "1        4.1250       4.554877  4.818231      3.987047      4.168040   \n",
-       "2        6.6250       7.643815  6.407051      5.669631      4.997217   \n",
-       "3        2.0625       7.194899  6.186497      6.186497      0.000000   \n",
-       "4        7.1875       8.137879  8.556664      9.098408      0.000000   \n",
-       "\n",
-       "   bertV5EncSim  bertV6EncSim  bertV7EncSim  \n",
-       "0      4.268633      4.268633      3.770548  \n",
-       "1      3.399217      3.399217      4.168040  \n",
-       "2      6.865782      6.865782      4.997217  \n",
-       "3      0.000000      2.961755      5.296330  \n",
-       "4      0.000000      3.408112      5.621890  "
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordsimDF1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "dress-soldier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "local-specialist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KendalltauResult(correlation=0.3793557735858387, pvalue=5.886665529373508e-26)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.kendalltau(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "studied-fluid",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5330950015966213, pvalue=5.11634460488722e-27)"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['bertV7EncSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "billion-parish",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "generic-allowance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.45231140660502644, pvalue=5.300007087753043e-19)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['complex'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "outer-symposium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.3426034487185167, pvalue=4.783676915903063e-11)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['transe'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "dated-fossil",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.5033109176274052, pvalue=8.216396781208269e-24)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['text'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "intermediate-assumption",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.3995842133820817, pvalue=8.237702943903066e-15)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordsimDF1['Human (Mean)'].to_list(), wordsimDF1['class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "offensive-tamil",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "399px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/TextEmbed UnitTest.ipynb b/TextEmbed UnitTest.ipynb
deleted file mode 100644
index 0bc93c9..0000000
--- a/TextEmbed UnitTest.ipynb	
+++ /dev/null
@@ -1,1093 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "addressed-titanium",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import gzip\n",
-    "from tqdm.notebook import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "returning-matthew",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_emb = pd.read_csv('../output/text-embeddings/missing_nodes.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "familiar-investment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Q1000959', 'Q100156149', 'Q100276118', 'Q100287557', 'Q100306102',\n",
-       "       'Q100325978', 'Q100345932', 'Q100347932', 'Q100347974',\n",
-       "       'Q100348014', 'Q100348021', 'Q100348032', 'Q100348054',\n",
-       "       'Q100348078', 'Q100385591', 'Q100385624', 'Q100385642',\n",
-       "       'Q100385667', 'Q100388742', 'Q100388938'], dtype=object)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text_emb.node1.unique()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "announced-andorra",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['P279', 'P31', 'P373', 'P452', 'P1382', 'P106'], dtype=object)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text_emb.label.unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "upper-facing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subset_nodes = set(['Q1000959', 'Q100156149', 'Q100276118', 'Q100287557', 'Q100306102',\n",
-    "       'Q1000959', 'Q105046695', 'Q105416259', 'Q105135334'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "separate-fishing",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "text_emb = text_emb[text_emb.node1.apply(lambda p: p in subset_nodes)].reset_index().drop(columns=['index'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "impossible-spanking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_emb.to_csv('../TEMP/kgtk_unittest/text_embedding.edges.tsv', sep='\\t', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "permanent-worship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels(node_set):\n",
-    "    labels_dict = {}\n",
-    "    lines = []\n",
-    "    first_line = None\n",
-    "    with gzip.open(\"../source_dataset_files/wikidata-20210215-dwd-v2/labels.en.tsv.gz\", 'r') as labelsFile:\n",
-    "        for ogline in tqdm(labelsFile, total=41845781):\n",
-    "            if first_line:\n",
-    "                first_line = False\n",
-    "                lines.append(ogline)\n",
-    "                continue\n",
-    "            line = ogline.decode('utf-8').strip().split('\\t')\n",
-    "            line[3] = line[3][1:-5]\n",
-    "            qnode, label = line[1], line[3]\n",
-    "    #         print(qnode, label)\n",
-    "            if qnode in node_set:\n",
-    "                lines.append(ogline)\n",
-    "    return lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "visible-fashion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e18b3e37f3ad45c5b4ecc4165cc1a08a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/41845781 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lines = get_labels(set(text_emb.node1.to_list() + text_emb.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "digital-exhibition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lines = [line.decode('utf-8') for line in lines]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "usual-register",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lines = ['id\\tnode1\\tlabel\\tnode2\\n'] + lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "played-emergency",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../TEMP/kgtk_unittest/text_embedding.labels.tsv', 'w') as f:\n",
-    "    f.writelines(lines)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "exceptional-spray",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.read_csv('~/kgtk/tests/data/text_embedding.edges.tsv', sep='\\t').node1.nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "collect-bleeding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp1 = pd.read_csv('~/kgtk/tests/data/out.tsv', sep='\\t')\n",
-    "temp2 = pd.read_csv('~/kgtk/tests/data/text_embedding.output.tsv', sep='\\t')"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": 72,
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-       "          node            property  \\\n",
-       "0   Q105135334      text_embedding   \n",
-       "1   Q105135334  embedding_sentence   \n",
-       "2   Q105046695      text_embedding   \n",
-       "3   Q105046695  embedding_sentence   \n",
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-       "12    Q1000959      text_embedding   \n",
-       "13    Q1000959  embedding_sentence   \n",
-       "\n",
-       "                                                value  \n",
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-       "1                                                 NaN  \n",
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-       "9                                                 NaN  \n",
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-       "11                                                NaN  \n",
-       "12  -0.6744056,-0.2023162,-0.27134913,-0.13181135,...  \n",
-       "13                                                NaN  "
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-     },
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-    "temp1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "chronic-isaac",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>It is costume component, hatband.</td>\n",
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-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Q100306102</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is bridged compound, pair of enantiomers.</td>\n",
-       "    </tr>\n",
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-       "      <th>8</th>\n",
-       "      <td>Q100287557</td>\n",
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-       "    <tr>\n",
-       "      <th>9</th>\n",
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-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is mushroom dish, veggie burger.</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Q100276118</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Q100276118</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is group of stereoisomers, sugar acid.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q100156149</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.34179834,0.9796913,-1.2001768,0.9455819,0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
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-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is food brand, meat analogue.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q1000959</td>\n",
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-       "    <tr>\n",
-       "      <th>15</th>\n",
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-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is Reanda (apple), apple, apple cultivar.</td>\n",
-       "    </tr>\n",
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-      ],
-      "text/plain": [
-       "          node            property  \\\n",
-       "0   Q105416259      text_embedding   \n",
-       "1   Q105416259  embedding_sentence   \n",
-       "2   Q105135334      text_embedding   \n",
-       "3   Q105135334  embedding_sentence   \n",
-       "4   Q105046695      text_embedding   \n",
-       "5   Q105046695  embedding_sentence   \n",
-       "6   Q100306102      text_embedding   \n",
-       "7   Q100306102  embedding_sentence   \n",
-       "8   Q100287557      text_embedding   \n",
-       "9   Q100287557  embedding_sentence   \n",
-       "10  Q100276118      text_embedding   \n",
-       "11  Q100276118  embedding_sentence   \n",
-       "12  Q100156149      text_embedding   \n",
-       "13  Q100156149  embedding_sentence   \n",
-       "14    Q1000959      text_embedding   \n",
-       "15    Q1000959  embedding_sentence   \n",
-       "\n",
-       "                                                value  \n",
-       "0   0.062068786,-0.04527851,0.08445225,0.020385189...  \n",
-       "1            It is Content creation, group of humans.  \n",
-       "2   -0.5932912,0.15151715,0.12169738,-0.57092476,0...  \n",
-       "3                   It is costume component, hatband.  \n",
-       "4   0.78254557,0.7347305,-0.24301007,-0.40795732,0...  \n",
-       "5   It is field of work, health care industry, ser...  \n",
-       "6   1.0986179,0.4818771,-0.46546745,0.28162912,1.0...  \n",
-       "7        It is bridged compound, pair of enantiomers.  \n",
-       "8   0.91020757,0.6681585,-0.3698121,2.749119,1.556...  \n",
-       "9                 It is mushroom dish, veggie burger.  \n",
-       "10  0.52650917,0.6521489,0.014877399,0.8474146,1.1...  \n",
-       "11          It is group of stereoisomers, sugar acid.  \n",
-       "12  -0.34179834,0.9796913,-1.2001768,0.9455819,0.6...  \n",
-       "13                   It is food brand, meat analogue.  \n",
-       "14  0.026072552,1.0901248,-1.0570514,-0.83307713,1...  \n",
-       "15       It is Reanda (apple), apple, apple cultivar.  "
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "specific-listening",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv2/lib/python3.8/site-packages/rdflib_jsonld/__init__.py:9: DeprecationWarning: The rdflib-jsonld package has been integrated into rdflib as of rdflib==6.0.1.  Please remove rdflib-jsonld from your project's dependencies.\n",
-      "  warnings.warn(\n",
-      "Batches:   0%|                                            | 0/1 [00:00<?, ?it/s]/nas/home/kshenoy/miniconda3/envs/kgtkEnv2/lib/python3.8/site-packages/transformers/tokenization_utils_base.py:2227: UserWarning: `max_length` is ignored when `padding`=`True`.\n",
-      "  warnings.warn(\"`max_length` is ignored when `padding`=`True`.\")\n",
-      "Batches: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 18.93it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "!~/miniconda3/envs/kgtkEnv2/bin/kgtk text-embedding -i ~/kgtk/tests/data/text_embedding.edges.tsv   \\\n",
-    "    --model roberta-large-nli-mean-tokens   \\\n",
-    "    --property-labels-file ~/kgtk/tests/data/text_embedding.labels.tsv  --debug   \\\n",
-    "    --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "    --save-embedding-sentence --out-file ../output/text-embeddings/P279-text-embedding-7-props-missing-qnodes.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "greenhouse-justice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!cp ../TEMP/kgtk_unittest/text_embedding.edges.tsv ~/kgtk/tests/data/text_embedding.edges.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "hydraulic-springer",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Q105416259\tembedding_sentence\tIt is Content creation, group of humans.\r\n",
-      "Q105135334\tembedding_sentence\tIt is costume component, hatband.\r\n",
-      "Q105046695\tembedding_sentence\tIt is field of work, health care industry, service, transportation industry.\r\n",
-      "Q100306102\tembedding_sentence\tIt is bridged compound, pair of enantiomers.\r\n",
-      "Q100287557\tembedding_sentence\tIt is mushroom dish, veggie burger.\r\n",
-      "Q100276118\tembedding_sentence\tIt is group of stereoisomers, sugar acid.\r\n",
-      "Q100156149\tembedding_sentence\tIt is food brand, meat analogue.\r\n",
-      "Q1000959\tembedding_sentence\tIt is Reanda (apple), apple, apple cultivar.\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!grep \"sentence\" ~/kgtk/tests/data/text_embedding.output.tsv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "christian-roots",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first_line = None\n",
-    "with open('../output/text-embeddings/missing_nodes.tsv') as f:\n",
-    "    for line in f:\n",
-    "        if first_line is None:\n",
-    "            first_line = line\n",
-    "            continue\n",
-    "        line1 = line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "strategic-edition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "approximate-ozone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "out_df = pd.read_csv('~/kgtk/tests/data/out.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "dedicated-elevation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expected_out_df = pd.read_csv('~/kgtk/tests/data/text_embedding.output.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "approved-cincinnati",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>It is costume component, hatband.</td>\n",
-       "    </tr>\n",
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-       "      <th>4</th>\n",
-       "      <td>Q105046695</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.78254557,0.7347305,-0.24301007,-0.40795732,0...</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Q105046695</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is field of work, health care industry, ser...</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
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-       "      <td>text_embedding</td>\n",
-       "      <td>1.0986179,0.4818771,-0.46546745,0.28162912,1.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Q100306102</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is bridged compound, pair of enantiomers.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q100287557</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.91020757,0.6681585,-0.3698121,2.749119,1.556...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Q100287557</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is mushroom dish, veggie burger.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Q100276118</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.52650917,0.6521489,0.014877399,0.8474146,1.1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Q100276118</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is group of stereoisomers, sugar acid.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q100156149</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.34179834,0.9796913,-1.2001768,0.9455819,0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Q100156149</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is food brand, meat analogue.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q1000959</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.026072552,1.0901248,-1.0570514,-0.83307713,1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Q1000959</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is Reanda (apple), apple, apple cultivar.</td>\n",
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-       "  </tbody>\n",
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-       "          node            property  \\\n",
-       "0   Q105416259      text_embedding   \n",
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-       "7   Q100306102  embedding_sentence   \n",
-       "8   Q100287557      text_embedding   \n",
-       "9   Q100287557  embedding_sentence   \n",
-       "10  Q100276118      text_embedding   \n",
-       "11  Q100276118  embedding_sentence   \n",
-       "12  Q100156149      text_embedding   \n",
-       "13  Q100156149  embedding_sentence   \n",
-       "14    Q1000959      text_embedding   \n",
-       "15    Q1000959  embedding_sentence   \n",
-       "\n",
-       "                                                value  \n",
-       "0   0.062068786,-0.04527851,0.08445225,0.020385189...  \n",
-       "1            It is Content creation, group of humans.  \n",
-       "2   -0.5932912,0.15151715,0.12169738,-0.57092476,0...  \n",
-       "3                   It is costume component, hatband.  \n",
-       "4   0.78254557,0.7347305,-0.24301007,-0.40795732,0...  \n",
-       "5   It is field of work, health care industry, ser...  \n",
-       "6   1.0986179,0.4818771,-0.46546745,0.28162912,1.0...  \n",
-       "7        It is bridged compound, pair of enantiomers.  \n",
-       "8   0.91020757,0.6681585,-0.3698121,2.749119,1.556...  \n",
-       "9                 It is mushroom dish, veggie burger.  \n",
-       "10  0.52650917,0.6521489,0.014877399,0.8474146,1.1...  \n",
-       "11          It is group of stereoisomers, sugar acid.  \n",
-       "12  -0.34179834,0.9796913,-1.2001768,0.9455819,0.6...  \n",
-       "13                   It is food brand, meat analogue.  \n",
-       "14  0.026072552,1.0901248,-1.0570514,-0.83307713,1...  \n",
-       "15       It is Reanda (apple), apple, apple cultivar.  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "sharing-equilibrium",
-   "metadata": {},
-   "outputs": [
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-       "      <th>7</th>\n",
-       "      <td>Q100306102</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is bridged compound, pair of enantiomers.</td>\n",
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-       "      <th>9</th>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Q100276118</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is group of stereoisomers, sugar acid.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Q100156149</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>-0.34179834,0.9796913,-1.2001768,0.9455819,0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Q100156149</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is food brand, meat analogue.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Q1000959</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>0.026072552,1.0901248,-1.0570514,-0.83307713,1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Q1000959</td>\n",
-       "      <td>embedding_sentence</td>\n",
-       "      <td>It is Reanda (apple), apple, apple cultivar.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          node            property  \\\n",
-       "0   Q105416259      text_embedding   \n",
-       "1   Q105416259  embedding_sentence   \n",
-       "2   Q105135334      text_embedding   \n",
-       "3   Q105135334  embedding_sentence   \n",
-       "4   Q105046695      text_embedding   \n",
-       "5   Q105046695  embedding_sentence   \n",
-       "6   Q100306102      text_embedding   \n",
-       "7   Q100306102  embedding_sentence   \n",
-       "8   Q100287557      text_embedding   \n",
-       "9   Q100287557  embedding_sentence   \n",
-       "10  Q100276118      text_embedding   \n",
-       "11  Q100276118  embedding_sentence   \n",
-       "12  Q100156149      text_embedding   \n",
-       "13  Q100156149  embedding_sentence   \n",
-       "14    Q1000959      text_embedding   \n",
-       "15    Q1000959  embedding_sentence   \n",
-       "\n",
-       "                                                value  \n",
-       "0   0.062068786,-0.04527851,0.08445225,0.020385189...  \n",
-       "1            It is Content creation, group of humans.  \n",
-       "2   -0.5932912,0.15151715,0.12169738,-0.57092476,0...  \n",
-       "3                   It is costume component, hatband.  \n",
-       "4   0.78254557,0.7347305,-0.24301007,-0.40795732,0...  \n",
-       "5   It is field of work, health care industry, ser...  \n",
-       "6   1.0986179,0.4818771,-0.46546745,0.28162912,1.0...  \n",
-       "7        It is bridged compound, pair of enantiomers.  \n",
-       "8   0.91020757,0.6681585,-0.3698121,2.749119,1.556...  \n",
-       "9                 It is mushroom dish, veggie burger.  \n",
-       "10  0.52650917,0.6521489,0.014877399,0.8474146,1.1...  \n",
-       "11          It is group of stereoisomers, sugar acid.  \n",
-       "12  -0.34179834,0.9796913,-1.2001768,0.9455819,0.6...  \n",
-       "13                   It is food brand, meat analogue.  \n",
-       "14  0.026072552,1.0901248,-1.0570514,-0.83307713,1...  \n",
-       "15       It is Reanda (apple), apple, apple cultivar.  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
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-  }
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diff --git a/Untitled.ipynb b/Untitled.ipynb
deleted file mode 100644
index 363fcab..0000000
--- a/Untitled.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Wikidata CS Exploration.ipynb b/Wikidata CS Exploration.ipynb
deleted file mode 100644
index df3389c..0000000
--- a/Wikidata CS Exploration.ipynb	
+++ /dev/null
@@ -1,10807 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "familiar-forest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "indonesian-mambo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF = pd.read_csv('../data/wikidata-cs-20200504.tsv',sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "distributed-spice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    .dataframe thead th {\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node2</th>\n",
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-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1449</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>P2561</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>name</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1449</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>Q49614</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>nickname</td>\n",
-       "      <td>subject item of this property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1547</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>Q56859575</td>\n",
-       "      <td>depends on software</td>\n",
-       "      <td>software dependency</td>\n",
-       "      <td>subject item of this property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1592</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>P1593</td>\n",
-       "      <td>prosecutor</td>\n",
-       "      <td>defender</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1593</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>P1592</td>\n",
-       "      <td>defender</td>\n",
-       "      <td>prosecutor</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node1      relation      node2          node1;label          node2;label  \\\n",
-       "0  P1449        /r/IsA      P2561             nickname                 name   \n",
-       "1  P1449  /r/RelatedTo     Q49614             nickname             nickname   \n",
-       "2  P1547  /r/RelatedTo  Q56859575  depends on software  software dependency   \n",
-       "3  P1592  /r/RelatedTo      P1593           prosecutor             defender   \n",
-       "4  P1593  /r/RelatedTo      P1592             defender           prosecutor   \n",
-       "\n",
-       "                  relation;label  relation;dimension source  sentence  \n",
-       "0                 subproperty of                 NaN     WD       NaN  \n",
-       "1  subject item of this property                 NaN     WD       NaN  \n",
-       "2  subject item of this property                 NaN     WD       NaN  \n",
-       "3                       see also                 NaN     WD       NaN  \n",
-       "4                       see also                 NaN     WD       NaN  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "proof-toddler",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF['dim'] = wikiDF['relation'].apply(lambda p: mapper1[p])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "offshore-millennium",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['category'] = wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' or p == 'similarity' else 'M')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "finished-fashion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     72707\n",
-       "meronymy         6886\n",
-       "context          5541\n",
-       "distinctness     4934\n",
-       "utility          2243\n",
-       "antonymy         2184\n",
-       "ordering         2107\n",
-       "material         1426\n",
-       "synonymy         1070\n",
-       "properties       1049\n",
-       "derivation        540\n",
-       "causation         510\n",
-       "similarity        345\n",
-       "attribution       187\n",
-       "other              42\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF['dim'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "working-terrain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100701\n",
-       "I      1070\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF['dim'].apply(lambda p: 'I' if p == 'synonymy' else 'M').value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "artificial-publisher",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF[wikiDF.apply(lambda p: p['node1'].startswith('Q') and p['node2'].startswith('Q'), axis=1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "fourth-metabolism",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    100110\n",
-       "I      1415\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF1.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "numeric-optics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1 = wikiDF1.rename(columns={'node1':'word1_kg_id', 'node2':'word2_kg_id'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "optical-bradley",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF1[['word1_kg_id', 'word2_kg_id', 'category', 'node1;label', 'node2;label']].to_csv('../data/wikidata-cs_categorized.csv',index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "confidential-pasta",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "waiting-layout",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "masterP279Nodes = json.load(open('../data/Master_P279_dataset/P279ChildPar_transP279_filtered_nodes.json'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "advised-small",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>dim</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>Q1006348</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>Q15916540</td>\n",
-       "      <td>indirect speech</td>\n",
-       "      <td>semantic unit</td>\n",
-       "      <td>subclass of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>inheritance</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>Q1006428</td>\n",
-       "      <td>/r/HasContext</td>\n",
-       "      <td>Q11452</td>\n",
-       "      <td>two-body problem in general relativity</td>\n",
-       "      <td>general relativity</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>context</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>Q10082670</td>\n",
-       "      <td>/r/Antonym</td>\n",
-       "      <td>Q10943095</td>\n",
-       "      <td>elder sister</td>\n",
-       "      <td>younger sister</td>\n",
-       "      <td>opposite of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>antonymy</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>Q10082670</td>\n",
-       "      <td>/r/Antonym</td>\n",
-       "      <td>Q3972282</td>\n",
-       "      <td>elder sister</td>\n",
-       "      <td>elder brother</td>\n",
-       "      <td>opposite of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>antonymy</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>Q1010947</td>\n",
-       "      <td>/r/DistinctFrom</td>\n",
-       "      <td>Q390104</td>\n",
-       "      <td>glamour photography</td>\n",
-       "      <td>glamour</td>\n",
-       "      <td>different from</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>distinctness</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101729</th>\n",
-       "      <td>Q996599</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>Q211391</td>\n",
-       "      <td>long weekend</td>\n",
-       "      <td>weekend</td>\n",
-       "      <td>subclass of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>inheritance</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101737</th>\n",
-       "      <td>Q997419</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>Q1151733</td>\n",
-       "      <td>closer</td>\n",
-       "      <td>baseball position</td>\n",
-       "      <td>instance of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>inheritance</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101738</th>\n",
-       "      <td>Q997419</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>Q1198553</td>\n",
-       "      <td>closer</td>\n",
-       "      <td>relief pitcher</td>\n",
-       "      <td>subclass of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>inheritance</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101759</th>\n",
-       "      <td>Q998922</td>\n",
-       "      <td>/r/UsedFor</td>\n",
-       "      <td>Q1770547</td>\n",
-       "      <td>blackout light bulb</td>\n",
-       "      <td>blackout</td>\n",
-       "      <td>use</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>utility</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101767</th>\n",
-       "      <td>Q999803</td>\n",
-       "      <td>/r/IsA</td>\n",
-       "      <td>Q55387456</td>\n",
-       "      <td>sublime</td>\n",
-       "      <td>aesthetic concept</td>\n",
-       "      <td>instance of</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>inheritance</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>13195 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       word1_kg_id         relation word2_kg_id  \\\n",
-       "119       Q1006348           /r/IsA   Q15916540   \n",
-       "122       Q1006428    /r/HasContext      Q11452   \n",
-       "132      Q10082670       /r/Antonym   Q10943095   \n",
-       "134      Q10082670       /r/Antonym    Q3972282   \n",
-       "146       Q1010947  /r/DistinctFrom     Q390104   \n",
-       "...            ...              ...         ...   \n",
-       "101729     Q996599           /r/IsA     Q211391   \n",
-       "101737     Q997419           /r/IsA    Q1151733   \n",
-       "101738     Q997419           /r/IsA    Q1198553   \n",
-       "101759     Q998922       /r/UsedFor    Q1770547   \n",
-       "101767     Q999803           /r/IsA   Q55387456   \n",
-       "\n",
-       "                                   node1;label         node2;label  \\\n",
-       "119                            indirect speech       semantic unit   \n",
-       "122     two-body problem in general relativity  general relativity   \n",
-       "132                               elder sister      younger sister   \n",
-       "134                               elder sister       elder brother   \n",
-       "146                        glamour photography             glamour   \n",
-       "...                                        ...                 ...   \n",
-       "101729                            long weekend             weekend   \n",
-       "101737                                  closer   baseball position   \n",
-       "101738                                  closer      relief pitcher   \n",
-       "101759                     blackout light bulb            blackout   \n",
-       "101767                                 sublime   aesthetic concept   \n",
-       "\n",
-       "        relation;label  relation;dimension source  sentence           dim  \\\n",
-       "119        subclass of                 NaN     WD       NaN   inheritance   \n",
-       "122           facet of                 NaN     WD       NaN       context   \n",
-       "132        opposite of                 NaN     WD       NaN      antonymy   \n",
-       "134        opposite of                 NaN     WD       NaN      antonymy   \n",
-       "146     different from                 NaN     WD       NaN  distinctness   \n",
-       "...                ...                 ...    ...       ...           ...   \n",
-       "101729     subclass of                 NaN     WD       NaN   inheritance   \n",
-       "101737     instance of                 NaN     WD       NaN   inheritance   \n",
-       "101738     subclass of                 NaN     WD       NaN   inheritance   \n",
-       "101759             use                 NaN     WD       NaN       utility   \n",
-       "101767     instance of                 NaN     WD       NaN   inheritance   \n",
-       "\n",
-       "       category  \n",
-       "119           M  \n",
-       "122           M  \n",
-       "132           M  \n",
-       "134           M  \n",
-       "146           M  \n",
-       "...         ...  \n",
-       "101729        M  \n",
-       "101737        M  \n",
-       "101738        M  \n",
-       "101759        M  \n",
-       "101767        M  \n",
-       "\n",
-       "[13195 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF1[wikiDF1.word1_kg_id.apply(lambda p: p not in masterP279Nodes) & wikiDF1.word2_kg_id.apply(lambda p: p not in masterP279Nodes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "alpine-password",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "238889"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(masterP279Nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "civilian-induction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimSet = list(set(wikiDF.node1.to_list() + wikiDF.node2.to_list()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "peripheral-alignment",
-   "metadata": {},
-   "source": [
-    "# Fetch Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-magnet",
-   "metadata": {},
-   "source": [
-    "## Complex + Transe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "advisory-tablet",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64865436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74433572\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047190\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47708201\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16939336\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75815335\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638383\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74201282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29352977\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047182\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61948580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57636639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636435\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10384086\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18384500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618753\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49553430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56003279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7257714\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36708708\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71834669\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21993815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84288054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5102\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78849858\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71569224\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56267862\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61160732\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23699967\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3730867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4819858\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45813532\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84687204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38456828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036634\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48667794\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2842797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59158264\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705316\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P8047\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36249200\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29543009\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20026782\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50332811\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60621802\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10520944\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84757986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29578707\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5589518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12600952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46785334\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17158079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34768712\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42517203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3783318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50350470\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58902427\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22269594\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18603649\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50902564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61798329\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5460182\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53556002\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5328\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59383129\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61090213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10340288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17997011\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87977249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61067884\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2404985\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80432189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59211677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77667126\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q140702\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46256234\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56977400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59282004\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34200888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7442\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73863282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21162274\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6720214\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66368425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047187\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58997978\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11194929\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56021177\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64905317\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79679221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2695\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37286659\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58979956\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11497457\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965916\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52399926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58603740\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77340449\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51881207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17164790\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58998468\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2995983\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667095\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680038\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56030389\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20975596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851517\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84271640\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585136\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71470174\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17231698\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68065658\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585141\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5901\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36536067\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38069463\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038346\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60431603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1413969\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58647874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5280\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91515771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53496919\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59079146\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851567\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3746141\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56681204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78097957\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71283344\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73865001\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62404518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25504044\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6570\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58143241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88178923\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29306015\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59078556\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84298834\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87938350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2319440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1538854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59475077\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45799055\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45266300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72283908\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89184820\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3930265\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60723856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25380544\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29545850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57604775\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705289\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915169\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62585847\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56114341\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59033819\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090823\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24899199\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60547335\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29037088\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85715771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44294891\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693095\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12796995\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46830821\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28008313\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769133\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681531\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1720112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667270\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43473459\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41835716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38506410\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58612101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36734639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78090690\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681942\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68199869\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63792261\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5004618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746666\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7936630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58240553\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56658227\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73848146\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21002290\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73756526\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61105129\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38313140\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36430165\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54961374\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25039503\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37905234\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7233740\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66502807\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58876537\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78048467\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2184\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59450702\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734473\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11002263\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42790808\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072416\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5477678\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16937917\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72342517\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49585733\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58179448\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5028\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78090598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69361740\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P968\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73366170\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63395686\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63886763\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2515879\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57470203\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57398400\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681838\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54070999\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11902783\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75838596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47772347\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67141864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10380381\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89350258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7283951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29819756\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37862578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30047267\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6571\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56625581\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47226657\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58966165\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74474288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6422240\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47576960\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746662\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965282\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52171487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6710\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58813489\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68731010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60363191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58043865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87885144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692762\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87169767\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71898438\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769097\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90865542\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1327189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72492945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77893681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73041322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627072\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980469\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55312002\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35922832\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53911696\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11739050\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18119757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88009688\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14405929\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10546472\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93003322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77556661\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45763191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765317\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35760525\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87937643\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89223871\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74217532\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50454993\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18553215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29513828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56334790\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57657179\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42157065\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45945663\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58966208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17144564\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78029495\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11379779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61699843\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68848439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5991\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56019945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71916144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6534\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59076817\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3710530\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46384387\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61249011\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693069\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71802787\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667303\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78967694\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30921616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41909633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17123440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59628350\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2506578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64763437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61811214\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12486429\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24577909\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15706459\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56658228\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56337509\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84242091\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q628338\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59037848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36945766\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30918204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70556965\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41376951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47122316\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68279721\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53919919\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57705019\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51692243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5027\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55951022\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5706\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2361\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57287735\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6427\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618755\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57407542\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6731581\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67055629\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2535710\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40287877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58926227\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37415888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60357352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69368835\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59973208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29585699\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246614\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59915587\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60962156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78553203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q874632\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66525126\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61986723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1552\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57535936\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5307082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87070600\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7725\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5202\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681949\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5832\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12308638\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65096040\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58257544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54469554\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40423311\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091694\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90476672\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980351\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56641102\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627074\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681813\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27500609\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84861637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59043198\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56972008\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59289286\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036691\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44705203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56593453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55729353\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78086915\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77088052\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75755497\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51969935\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41359681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6424\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21198084\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38964612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56158192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38887569\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2535605\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29963683\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64864078\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30914189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48277039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90992880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7213\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1546\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55829848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58979822\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74477506\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60550474\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84274233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769137\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56672312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57612933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5281737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21014021\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64374415\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705292\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73375715\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74447306\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35219907\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708709\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74113345\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2579\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90117379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48930682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20980701\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q332209\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734421\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12593257\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33545318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84479168\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006766\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039381\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43787914\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61311233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734481\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56639043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667775\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25633436\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61534824\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36526633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58772354\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69585145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693077\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42293501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734613\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7532\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37782758\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56030423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68406538\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56866696\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58079010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59002045\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70996933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1504425\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39433287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57206946\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62101992\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45813280\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047183\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070275\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55632487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965337\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56604579\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28599815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5895\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55280602\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59261899\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030201\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59077205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49617371\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66556076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35033263\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52294396\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7239021\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63323005\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56557308\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56671877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72037756\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56013865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56990609\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60048131\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47615292\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88513819\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54943967\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56386521\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19314934\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60163121\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746782\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56170072\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q86413049\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48228719\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7462\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15223670\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5899\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6533\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41845366\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37056861\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41226557\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23923844\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58626642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36967806\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60066070\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746809\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2729443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49137994\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60537443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38554054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73298134\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032333\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13415408\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72376329\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030519\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58993316\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41673723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18014941\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12041905\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68287507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88524690\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13164480\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73010357\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60723552\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58926708\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667515\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59406504\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28132458\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89680732\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3625632\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39210110\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047186\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55968809\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64006842\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39974398\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705297\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60481617\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83365205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84276764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091067\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12016773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75224630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5994\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75798762\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34307362\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33947320\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87282988\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89184453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56639568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82452792\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5828\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63041650\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73797856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68861473\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29789760\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75800566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12593409\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27437064\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275833\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58988215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91901288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59005988\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21082583\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33339426\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741273\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38023488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60059934\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84273997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61877212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59043484\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29035123\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852617\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57579202\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66425262\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84481119\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56430961\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708707\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1679325\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72839445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38268291\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80809031\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63616194\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10593480\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62001428\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2717928\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37983307\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89866637\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638153\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1916384\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46031408\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55579288\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746754\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24289083\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66615778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56626243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20741385\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47880328\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q681501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25036082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59092300\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56656109\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70569900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7888\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q86748173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49490137\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56522048\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1752\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84365227\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43906807\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61797590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3440287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66018184\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2557\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18393083\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58991654\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70529933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66573890\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75066484\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73128185\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53380051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59135880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90316618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73544861\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1114\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92314923\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74522432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56626440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66591864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773858\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71471711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19480970\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89680730\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87997922\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92349858\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19921713\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49978699\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84287042\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30070550\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52129798\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21517123\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62917831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6840\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29618407\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17014863\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4457167\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58325003\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20731983\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11246094\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11885950\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58133296\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57497365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58626655\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70082418\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58033479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29032132\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1366\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681537\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24798621\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59520568\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34084891\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965858\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7407\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2572045\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37176024\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667560\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58628265\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71404012\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611103\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55933001\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032676\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59345268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7209\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89757030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22329445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73862249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66503444\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57539737\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44464056\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75630156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16287483\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58985929\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3712\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667841\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2353\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22675364\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33945850\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79448203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072435\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80467138\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611025\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58918513\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090470\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047196\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57395864\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60431620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91848848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964833\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708854\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038117\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667503\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55880952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58173479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70710519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82434561\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66591130\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1269\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58169884\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66501877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6510\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734519\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42759688\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16327012\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16710805\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037753\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852627\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72076483\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q9311444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3643\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039442\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73301796\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81368221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7649212\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88013874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57535318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58982742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33887149\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45820870\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047205\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q426745\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65602465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851595\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56658776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58135900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78085108\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11285593\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q498187\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88355601\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61311241\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59078491\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22809145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7160\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7162\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22330256\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7253\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66558559\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66821598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82969330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87988388\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73848127\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74412363\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1581\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64357639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55025113\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60517680\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638152\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56040042\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88192695\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17142722\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40866874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34197343\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29802965\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66560167\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60103941\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56675254\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155479\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82154252\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56851764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1697092\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681495\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852631\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84276996\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93239268\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58169679\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692770\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55789150\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52438272\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55931410\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2561\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068077\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915193\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7318444\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45810671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33979925\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57760761\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18980173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637871\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47553356\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92416090\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58925773\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74210507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47115461\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57535356\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q8034325\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5004\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2194191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60059926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73848149\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88447596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58898396\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74438741\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35219848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765322\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006620\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14851902\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33980030\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708657\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636727\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59034803\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80458698\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49576178\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66590580\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90278705\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49688339\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28050380\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30323970\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71331747\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047174\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68087009\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q24338379\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5898\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39570919\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84078018\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59036022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q13373900\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74387518\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694107\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2198\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047203\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67696175\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77655668\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74205111\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667274\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42518897\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3343005\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64870206\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36536076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60565106\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59031039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87977781\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3037\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667537\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60680507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87289789\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q26454986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59039938\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6875\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40840080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q76000631\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5905651\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7081\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84481066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88420616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12094812\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73118411\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692947\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56376142\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2566026\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090766\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55884446\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42641723\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4495716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93290588\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91141252\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047191\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q11224008\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091794\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44883975\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79213588\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35647276\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30082658\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29011738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15504007\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64812851\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16924633\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38279875\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67735768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63431159\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74472668\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89722738\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30891544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79388814\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55981170\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63436639\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58283387\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60085543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q82785806\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63400219\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741259\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36757992\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60682186\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20850849\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60123818\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692914\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84248466\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59002580\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59035851\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q8261440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54399662\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3730\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15850590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030918\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708577\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38331872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47690568\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56214810\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57591831\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66505181\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20820011\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q846360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30019717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17542926\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12336608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12183006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1227747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69336485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2987450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708572\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73864627\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2921726\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q85809429\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667090\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78182962\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43950725\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84471501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91250837\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71880890\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68408145\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708744\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35961983\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81120140\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44068575\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92701441\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694233\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611132\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047209\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14905949\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59038281\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25041611\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28242379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71872821\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36775166\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56626751\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58255465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667851\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52426347\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58925772\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q69876814\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58926711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87885168\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56384597\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54431752\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28954843\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87940127\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91642204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5422\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54214049\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60594464\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91463287\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23038556\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66589334\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3956339\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70460306\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5670\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980244\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59216528\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71872824\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2782326\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59006354\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65028437\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33701841\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1449\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17991986\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746751\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21075823\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q21504918\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18391611\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16474028\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1859784\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56445754\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12027742\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84298625\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1592\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3822\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P4295\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58558163\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56698946\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50451671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7318358\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61249010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P559\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59616003\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43195545\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58812539\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51159225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746791\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53931299\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55396945\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84247973\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43916273\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61248612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33038168\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74030064\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636702\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030394\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12051308\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56605590\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q14905700\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49553500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068075\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84274685\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63410620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66789593\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6440\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78027215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33082998\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71125249\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91666984\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66544192\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12599076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59037526\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22911774\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61248784\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56384971\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852619\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78119547\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73923194\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37138983\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155052\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38247154\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88284939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851578\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57826006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2030245\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60094972\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41806836\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746675\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52076135\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16034400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87939741\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40362623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22236200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73305703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619771\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60727419\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42463776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734359\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84299551\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3006801\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28502569\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681586\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58133302\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72358764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35026666\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57629324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852599\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27885123\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58730533\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56687451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67785005\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7261\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74437022\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27889310\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63437359\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62555520\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6587\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17781874\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61834409\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79485608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037745\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83512382\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681591\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637101\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741215\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637169\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56638154\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56611318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89508939\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18236098\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59076898\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091862\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6166\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58981315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2281630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52171144\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980125\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55890146\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2381062\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2852\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62554494\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6646014\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56236880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28008243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59090560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6902\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068258\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41446476\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q180043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91432327\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67040294\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44007369\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35867112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89705318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56659498\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708507\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5900655\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58983008\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66571827\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46151838\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q50603365\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746733\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67144180\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40984512\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57608808\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89741235\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33767112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56680078\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12046878\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7727\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2215100\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59157150\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2795\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46036445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63979927\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6563\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34585815\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q75801718\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64260953\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70568725\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55880194\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63090339\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1821278\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746835\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1047129\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81570471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7252\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63252853\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49575935\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708764\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36465471\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44329936\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59037465\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734515\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91858696\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56636913\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63770443\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55789049\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37546082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q49490023\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59486367\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694125\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2144951\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74616066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091616\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66507099\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43184554\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091159\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61745706\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52089653\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55891501\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61535026\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6224\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91739076\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89693072\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57933311\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58965603\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6619716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89055460\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55092971\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59089543\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7824266\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59001713\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16321430\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45182324\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36084786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66507330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66515634\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59034548\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q83069379\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59155028\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q40648173\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59731975\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P520\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66688937\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66724450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5769\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7415\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78086635\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58081477\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q571112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5897\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18168614\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34388618\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q130596\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692952\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55670964\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73215457\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55933978\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58381920\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87915157\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681756\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59040112\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55883560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89454997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q515487\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30595453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23037749\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58981855\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2914865\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3034808\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88013896\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q941413\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35689748\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16578059\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694221\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12043671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17061485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67391763\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74506663\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46086677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57072374\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068243\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59618768\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36260332\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56852613\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681445\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5967378\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2551\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765360\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59077764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657164\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58390623\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56657157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15788778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57858242\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708560\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5465488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80120211\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047208\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59261901\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18356933\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77527200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59040057\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3030630\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18762207\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36475676\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708817\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87913717\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47223703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77420921\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4533225\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P6545\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q17152310\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627421\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60729272\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58713397\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q47004757\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89663016\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53027591\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q902733\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30625279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61452488\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48015805\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35417711\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q68304097\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58024844\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q44042015\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42904932\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q62089315\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58980816\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34995584\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72681620\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q37669027\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52486321\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57826013\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734562\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57416641\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88909747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59611218\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58082867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56773003\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61631779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71751401\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74517369\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58979818\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89694131\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56627877\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1759797\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77481453\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q81568330\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43555634\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71880954\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q7623420\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59401950\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q38247151\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q45845277\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047180\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57261880\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091516\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84246795\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q41639531\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59091275\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P3464\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57943840\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77253277\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681557\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52081983\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q67767032\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60098797\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q39292352\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64385953\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66511786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7719\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q1022439\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27918922\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89692779\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42559432\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12148487\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55623778\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5423\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18340291\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55789494\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25933403\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89756950\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q42888697\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18679117\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59475250\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q2313210\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61067917\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7561\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q46410656\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59757607\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q30279388\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56344544\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59032189\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57773849\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q90467686\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681450\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29040297\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87979188\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79091747\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q19829402\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60568764\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33695242\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708835\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58964868\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q70943739\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q80927264\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q29547799\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66591079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q25446819\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18611872\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88180294\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52022204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89722728\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7604\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22980233\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7165\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22808118\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77982012\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84288305\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61979400\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q6815348\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q10385896\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q61068061\n",
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-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74202066\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q92700703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77696500\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56672039\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56047232\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89765312\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72041259\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q15077340\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681871\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84479664\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56060497\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59070402\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q33461541\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35717610\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q3177867\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64922682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q66590089\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84567767\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q34127834\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74424079\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681908\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q78295452\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P5304\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7122\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89681785\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89734410\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60208424\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q54452490\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q65683204\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84275997\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28590574\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53964612\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89308296\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59000318\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56637063\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P2341\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q74200193\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73851598\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q328566\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q79366664\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746848\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q76523972\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P156\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56092582\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35464229\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59493528\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q53559051\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q719419\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q84286737\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73935677\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58129147\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q93390200\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q87885164\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56297970\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64101776\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q22074608\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58297595\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60110\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28816010\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89667856\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q51599582\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q36388823\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28667703\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q57926311\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58158716\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q4569238\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q12893940\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72729171\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58392821\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7469\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18012653\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18553681\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q5193377\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59030279\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q91463220\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q55788456\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27865780\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q23067080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q27983054\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q28129958\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q77166485\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58506813\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P7309\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q71044374\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q31087626\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73862359\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35554451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q88020063\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/P1451\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59092080\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q52883628\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q35803642\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89746671\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56659539\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q20948792\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q16637260\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60095043\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q59035285\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q56659547\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q73221682\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q63459006\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q58901786\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q64768673\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q72463157\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q18059915\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89769082\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q89708731\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q43108199\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q60045937\n",
-      "Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/Q48340037\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "compEmbeddings = {}\n",
-    "transeEmbeddings = {}\n",
-    "\n",
-    "for wordID in wordSimSet:\n",
-    "    try:\n",
-    "        resp = requests.get(\"http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID).json()['_source']\n",
-    "        compEmbeddings[wordID] = resp['graph_embedding_complex']\n",
-    "        transeEmbeddings[wordID] = resp['graph_embeddings_transe']\n",
-    "    except:\n",
-    "        print(\"Failure returned for http://ckg07:9200/wikidatadwd-augmented/_doc/\"+wordID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "complicated-puzzle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node1_compl_emb'] = wikiDF['node1'].apply(lambda p: compEmbeddings[p] if p in compEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "canadian-divorce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node2_compl_emb'] = wikiDF['node2'].apply(lambda p: compEmbeddings[p] if p in compEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "typical-delay",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node1_transe_emb'] = wikiDF['node1'].apply(lambda p: transeEmbeddings[p] if p in transeEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "passive-magazine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF['node2_transe_emb'] = wikiDF['node2'].apply(lambda p: transeEmbeddings[p] if p in transeEmbeddings else None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interested-sympathy",
-   "metadata": {},
-   "source": [
-    "## Abstract"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "special-paradise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sitelinksDF = pd.read_csv(\"../data/sitelinks.en.tsv.gz\", sep='\\t')\n",
-    "# sitelinksDF['trimmedNode2'] = sitelinksDF.node2.apply(lambda p: p.split(\"/\")[-1] if p.split(\"/\")[-1] != '' else p.split(\"/\")[-2])\n",
-    "# sitelinksDF1 = sitelinksDF[sitelinksDF.label == 'wikipedia_sitelink']\n",
-    "# sitelinksDF2 = sitelinksDF1.set_index('trimmedNode2')\n",
-    "# print(f\"There are {len(sitelinksDF2)} sitelinks present in the dataset corresponding to {sitelinksDF2.node1.nunique()} unique node1s (Qxxx), {sitelinksDF2.index.nunique()} unique labels (text)\")\n",
-    "# # sitelinksDF2.loc[sitelinksDF2[sitelinksDF2.index.duplicated()].index]\n",
-    "\n",
-    "# df1 = pd.read_csv(\"../data/short-abstracts_lang=en.csv\", skiprows=1, skipfooter=1, header=None, engine='python')\n",
-    "# df1.columns = ['ignore', 'node1', 'url', 'ignore2', 'abstract']\n",
-    "# df1 = df1.set_index('node1')\n",
-    "# df1[df1.url.apply(lambda p: 'http://dbpedia.org/resource' not in p)]\n",
-    "\n",
-    "# print(f\"DBPedia dataset has {len(df1)} records with unique {df1.index.nunique()} index values\")\n",
-    "# # df1.loc[df1[df1.index.duplicated()].index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "committed-bathroom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF2 = sitelinksDF2.join(df1).reset_index()\n",
-    "# print(f\"From {len(abstractsDF2)} Qnodes, there are {abstractsDF2.ignore2.isna().sum()} sitelink Qnodes which do not have a short abstract i.e {len(abstractsDF2) - abstractsDF2.ignore2.isna().sum()} have a short abstract\")\n",
-    "# abstractsDF2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "given-sapphire",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print(f\"We have {len(wordSimSet)} no. of Qnodes extracted from node1, node2 of wikidata CS\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "black-throat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimSet = set(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "painful-survivor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF_subset = abstractsDF2[abstractsDF2.node1.apply(lambda p: p in wordSimSet)]\n",
-    "# abstractsDF_subset = abstractsDF_subset[~abstractsDF_subset.abstract.isna()]\n",
-    "# len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "democratic-priority",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from nltk.tokenize import sent_tokenize\n",
-    "# abstractsDF_subset['abstract_firstSent'] = abstractsDF_subset.abstract.apply(lambda p: sent_tokenize(p)[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "likely-north",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# abstractsDF_subset.to_csv('../data/M-Pairs/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "legendary-contract",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abstractsDF_subset = pd.read_csv('../data/M-Pairs/output/abstractsDF_subset.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "worst-municipality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31144"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(abstractsDF_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "durable-princeton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sentence_transformers import SentenceTransformer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from time import time\n",
-    "import pandas as pd\n",
-    "\n",
-    "def getBERTSentEmbeddings(valSeries, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(valSeries.to_list())\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings\n",
-    "def getBERTSentEmbeddingsForSent(val1, modelName):\n",
-    "    model = SentenceTransformer(modelName)\n",
-    "    start = time()\n",
-    "    encodings = model.encode(val1)\n",
-    "    print(time()-start,'s')\n",
-    "    return encodings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "victorian-orientation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "74.81354665756226 s\n",
-      "31.950600385665894 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "abstractsDF_subset['BERT_abstract_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract, 'bert-base-nli-mean-tokens')))\n",
-    "abstractsDF_subset['BERT_abstract_firstSent_emb'] = pd.Series(list(getBERTSentEmbeddings(abstractsDF_subset.abstract_firstSent, 'bert-base-nli-mean-tokens')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "stuffed-stamp",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb']:\n",
-    "    abstractsDF_subset[col] = abstractsDF_subset[col].apply(lambda p: p.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "excessive-compression",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>label</th>\n",
-       "      <th>node2</th>\n",
-       "      <th>ignore</th>\n",
-       "      <th>url</th>\n",
-       "      <th>ignore2</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [Unnamed: 0, index, id, node1, label, node2, ignore, url, ignore2, abstract, abstract_firstSent, BERT_abstract_emb, BERT_abstract_firstSent_emb]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abstractsDF_subset[abstractsDF_subset.node1.duplicated()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "instructional-wireless",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs = wikiDF.set_index('node1').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "flush-share",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.reset_index().set_index('node2').join(abstractsDF_subset.set_index('node1')[['abstract','abstract_firstSent', 'BERT_abstract_emb', 'BERT_abstract_firstSent_emb']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "changing-calculator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs1.reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "mathematical-denver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_emb'] = wikiDF_wabs1['BERT_abstract_emb'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_emb'] = wikiDF_wabs1['BERT_abstract_firstSent_emb'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_emb_right'] = wikiDF_wabs1['BERT_abstract_emb_right'].tolist()\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_emb_right'] = wikiDF_wabs1['BERT_abstract_firstSent_emb_right'].tolist()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "patent-capability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['node1_compl_emb'] = wikiDF_wabs1['node1_compl_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node2_compl_emb'] = wikiDF_wabs1['node2_compl_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node1_transe_emb'] = wikiDF_wabs1['node1_transe_emb'].apply(lambda p: p.split(',') if p else None)\n",
-    "wikiDF_wabs1['node2_transe_emb'] = wikiDF_wabs1['node2_transe_emb'].apply(lambda p: p.split(',') if p else None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "double-mediterranean",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>node1_transe_emb</th>\n",
-       "      <th>node2_transe_emb</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.178757623, -1.513463259, -1.526623845, 0.3...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.396865129, 0.509240150, -0.284384042, -0.5...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.363599300, -0.764536679, 0.060853526, -0.0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.337825149, -0.189670041, -0.480503321, -0.2...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.043086100, -0.041153915, -0.603652894, -0.4...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-1.035292268, 0.008348951, -1.242931604, -1.6...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.363599300, -0.764536679, 0.060853526, -0.0...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.043086100, -0.041153915, -0.603652894, -0.4...  ...   \n",
-       "\n",
-       "                                    node1_transe_emb node2_transe_emb  \\\n",
-       "0  [-0.178757623, -1.513463259, -1.526623845, 0.3...             None   \n",
-       "1  [-0.396865129, 0.509240150, -0.284384042, -0.5...             None   \n",
-       "2  [0.337825149, -0.189670041, -0.480503321, -0.2...             None   \n",
-       "3                                               None             None   \n",
-       "4  [-1.035292268, 0.008348951, -1.242931604, -1.6...             None   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                                NaN            NaN   \n",
-       "3                                                NaN            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                     NaN   \n",
-       "1                      NaN                     NaN   \n",
-       "2                      NaN                     NaN   \n",
-       "3                      NaN                     NaN   \n",
-       "4                      NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \n",
-       "0                               NaN  \n",
-       "1                               NaN  \n",
-       "2                               NaN  \n",
-       "3                               NaN  \n",
-       "4                               NaN  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dietary-function",
-   "metadata": {},
-   "source": [
-    "## Text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "seven-individual",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimList = list(wordSimSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "identical-brighton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bsize = len(wordSimList) // 20\n",
-    "# cnt = 1\n",
-    "# for i in range(0, len(wordSimList), bsize):\n",
-    "#     q1 = \"kgtk filter -i ../data/wikidataos.for.text-embedding.tsv.gz --word-separator '|' -p '\" + '|'.join(wordSimList[i:i+bsize]) + \";;' -o ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv -v True\"\n",
-    "#     cnt += 1\n",
-    "# #     print(q1)\n",
-    "#     os.system(\"screen -dm \" + q1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "front-acquisition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(1,11):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "designing-rugby",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for cnt in range(11,21):\n",
-    "#     q1 = \"kgtk text-embedding -i ../data/M-Pairs/output/wikidataos.for.text-embedding-ext-\" + str(cnt) + \".tsv   \\\n",
-    "#         --model roberta-large-nli-mean-tokens   \\\n",
-    "#         --property-labels-file ../data/labels.en.tsv  --debug   \\\n",
-    "#         --isa-properties P31 P279 P106 P39 P1382 P373 P452   \\\n",
-    "#         --save-embedding-sentence > ../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\"\n",
-    "# #     print(q1)\n",
-    "#     os.system(q1 + \" &amp;\")\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "incredible-cedar",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DFArr = []\n",
-    "for cnt in range(1,21):\n",
-    "    textEmb_7props_DFArr.append(pd.read_csv(\"../data/M-Pairs/output/P279-text-embedding-7-props-part-\" + str(cnt) + \".tsv\", sep='\\t'))\n",
-    "textEmb_7props_DF = pd.concat(textEmb_7props_DFArr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "underlying-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF.to_csv('../data/M-Pairs/output/P279-text-embedding-7-props-all.tsv', sep='\\t', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "central-product",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF = textEmb_7props_DF[textEmb_7props_DF.property == 'text_embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "broke-cathedral",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "textEmb_7props_DF['value'] = textEmb_7props_DF['value'].apply(lambda p: [float(p1) for p1 in p.split(',')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "renewable-brand",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>property</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q995878</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[1.3671061, 0.9123121, -0.79829437, 0.9297752,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q995609</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.27214786, 1.5027193, -1.0903299, -0.1850157...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q994507</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.35614717, 1.6611174, -0.2951356, 0.81781155...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Q986635</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[0.26672798, 0.63939905, -1.2968327, 0.1320171...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Q9813868</td>\n",
-       "      <td>text_embedding</td>\n",
-       "      <td>[-0.08139745, 0.39049774, 0.24617954, -0.07873...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       node        property                                              value\n",
-       "0   Q995878  text_embedding  [1.3671061, 0.9123121, -0.79829437, 0.9297752,...\n",
-       "2   Q995609  text_embedding  [0.27214786, 1.5027193, -1.0903299, -0.1850157...\n",
-       "4   Q994507  text_embedding  [0.35614717, 1.6611174, -0.2951356, 0.81781155...\n",
-       "6   Q986635  text_embedding  [0.26672798, 0.63939905, -1.2968327, 0.1320171...\n",
-       "8  Q9813868  text_embedding  [-0.08139745, 0.39049774, 0.24617954, -0.07873..."
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "textEmb_7props_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "gothic-california",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs2 = wikiDF_wabs1.set_index('node1').join(textEmb_7props_DF.set_index('node')[['value']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "smooth-albania",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs2 = wikiDF_wabs2.rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "basic-jumping",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs2.reset_index().set_index('node2').join(textEmb_7props_DF.set_index('node')[['value']], rsuffix='_right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "original-canada",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs3.rename(columns={'index': 'node1'}).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "deadly-atmosphere",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs3 = wikiDF_wabs3.rename(columns={'index': 'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "palestinian-trance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "difficult-athletics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "dutch-principal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1 = wikiDF_wabs1.drop(columns=['value','value_right'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "relative-clearing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['node2', 'node1', 'relation', 'node1;label', 'node2;label',\n",
-       "       'relation;label', 'relation;dimension', 'source', 'sentence',\n",
-       "       'node1_compl_emb', 'node2_compl_emb', 'node1_transe_emb',\n",
-       "       'node2_transe_emb', 'abstract', 'abstract_firstSent',\n",
-       "       'BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'abstract_right',\n",
-       "       'abstract_firstSent_right', 'BERT_abstract_emb_right',\n",
-       "       'BERT_abstract_firstSent_emb_right', 'value', 'value_right'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "existing-juice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cols = ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'BERT_abstract_emb_right', \n",
-    "#         'BERT_abstract_firstSent_emb_right', \n",
-    "#         'node1_compl_emb', 'node2_compl_emb', \n",
-    "#         'node1_transe_emb', 'node2_transe_emb', 'value', 'value_right']\n",
-    "# for col in cols:\n",
-    "#     wikiDF_wabs1[col] = wikiDF_wabs1[col].apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "unlimited-monster",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.363599300, -0.764536679, 0.060853526, -0.0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.043086100, -0.041153915, -0.603652894, -0.4...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.363599300, -0.764536679, 0.060853526, -0.0...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.043086100, -0.041153915, -0.603652894, -0.4...  ...   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                                NaN            NaN   \n",
-       "3                                                NaN            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                     NaN   \n",
-       "1                      NaN                     NaN   \n",
-       "2                      NaN                     NaN   \n",
-       "3                      NaN                     NaN   \n",
-       "4                      NaN                     NaN   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "overall-blues",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1.to_csv('../data/M-Pairs/wikidataCS_wemb.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "radio-flexibility",
-   "metadata": {},
-   "source": [
-    "# Summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "appropriate-integer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs = pd.read_csv('../data/M-Pairs/wikidataCS_wemb.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "rational-kansas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "suburban-cruise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs.copy()\n",
-    "cols = ['BERT_abstract_emb', 'BERT_abstract_firstSent_emb', 'BERT_abstract_emb_right', \n",
-    "        'BERT_abstract_firstSent_emb_right', \n",
-    "        'node1_compl_emb', 'node2_compl_emb', \n",
-    "        'node1_transe_emb', 'node2_transe_emb', 'value', 'value_right']\n",
-    "for col in cols:\n",
-    "    wikiDF_wabs1[col] = wikiDF_wabs1[col].apply(lambda p: [float(p1.replace(\"'\",\"\")) for p1 in p[1:-1].split(',')] if type(p) != float else None)\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "electronic-vietnamese",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>abstract</th>\n",
-       "      <th>abstract_firstSent</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>In the Latter Day Saint movement, a temple is ...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>A tram (in North America streetcar or trolley)...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>An awards ceremony is a type of ceremony where...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                            abstract  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                  abstract_firstSent  \\\n",
-       "0  In the Latter Day Saint movement, a temple is ...   \n",
-       "1  A tram (in North America streetcar or trolley)...   \n",
-       "2                                                NaN   \n",
-       "3                                                NaN   \n",
-       "4  An awards ceremony is a type of ceremony where...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...  \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-design",
-   "metadata": {},
-   "source": [
-    "## Fix Abstract stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "little-split",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF = pd.read_csv('../data/labels.en.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "separate-apache",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labelsDF1 = labelsDF.drop(columns=['id', 'label'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "medical-authority",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs1.set_index('node1').join(labelsDF1.set_index('node1'), rsuffix='_label')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "julian-sector",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.reset_index().rename(columns={'index':'node1'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "marine-lloyd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.set_index('node2').join(labelsDF1.set_index('node1'), rsuffix='_label_right').rename(columns={'node2':'node2_label_right'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "biblical-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.reset_index().rename(columns={'index':'node2'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "piano-pencil",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs4 = wikiDF_wabs4.rename(columns={'node2_label': 'node1_wiki_label', 'node2_label_right': 'node2_wiki_label'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "statistical-order",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "      <th>node1_wiki_label</th>\n",
-       "      <th>node2_wiki_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'temple'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'tram'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>'number of objects'@en</td>\n",
-       "      <td>'quantity'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "      <td>'student organization of'@en</td>\n",
-       "      <td>'represents'@en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "      <td>'award ceremony'@en</td>\n",
-       "      <td>'facet of'@en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right  \\\n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...   \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...   \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...   \n",
-       "\n",
-       "               node1_wiki_label node2_wiki_label  \n",
-       "0                   'temple'@en    'quantity'@en  \n",
-       "1                     'tram'@en    'quantity'@en  \n",
-       "2        'number of objects'@en    'quantity'@en  \n",
-       "3  'student organization of'@en  'represents'@en  \n",
-       "4           'award ceremony'@en    'facet of'@en  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs4.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "introductory-elizabeth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "563"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs4['node2_wiki_label'].isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "packed-simpson",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "established-preference",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['node1;label_temp'] = wikiDF_wabs1['node1;label'].fillna(\"\")\n",
-    "wikiDF_wabs1['node2;label_temp'] = wikiDF_wabs1['node2;label'].fillna(\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "sunrise-liverpool",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abstract_emb</th>\n",
-       "      <th>BERT_abstract_firstSent_emb</th>\n",
-       "      <th>abstract_right</th>\n",
-       "      <th>abstract_firstSent_right</th>\n",
-       "      <th>BERT_abstract_emb_right</th>\n",
-       "      <th>BERT_abstract_firstSent_emb_right</th>\n",
-       "      <th>value</th>\n",
-       "      <th>value_right</th>\n",
-       "      <th>node1;label_temp</th>\n",
-       "      <th>node2;label_temp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.1981331706047058, 0.9829303026199341, -0.00...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[-0.027918775, 0.40711772, -0.26630637, -0.669...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.005881215445697308, -0.6711483001708984, -...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6575391, -0.10703926, 0.73501605, -0.636591...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.15300399, -0.43896374, -0.17923762, 0.23122...</td>\n",
-       "      <td>[0.22757019, 1.3568109, 0.20142093, -0.136266,...</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.6896931, 0.5631939, 0.03267402, 0.30135256,...</td>\n",
-       "      <td>[-0.4857783, 1.0299897, -0.08442746, 0.0377928...</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.236312136054039, 0.7216204404830933, 1.821...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.7004955, 1.2301444, -1.3314008, 0.3130464, ...</td>\n",
-       "      <td>[-0.25474873, 1.0963922, -0.07334485, -0.05801...</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                                   BERT_abstract_emb  \\\n",
-       "0  [0.1981331706047058, 0.9829303026199341, -0.00...   \n",
-       "1  [-0.005881215445697308, -0.6711483001708984, -...   \n",
-       "2                                               None   \n",
-       "3                                               None   \n",
-       "4  [-0.236312136054039, 0.7216204404830933, 1.821...   \n",
-       "\n",
-       "                         BERT_abstract_firstSent_emb abstract_right  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...            NaN   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....            NaN   \n",
-       "2                                               None            NaN   \n",
-       "3                                               None            NaN   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...            NaN   \n",
-       "\n",
-       "  abstract_firstSent_right BERT_abstract_emb_right  \\\n",
-       "0                      NaN                    None   \n",
-       "1                      NaN                    None   \n",
-       "2                      NaN                    None   \n",
-       "3                      NaN                    None   \n",
-       "4                      NaN                    None   \n",
-       "\n",
-       "  BERT_abstract_firstSent_emb_right  \\\n",
-       "0                              None   \n",
-       "1                              None   \n",
-       "2                              None   \n",
-       "3                              None   \n",
-       "4                              None   \n",
-       "\n",
-       "                                               value  \\\n",
-       "0  [-0.027918775, 0.40711772, -0.26630637, -0.669...   \n",
-       "1  [0.6575391, -0.10703926, 0.73501605, -0.636591...   \n",
-       "2  [0.15300399, -0.43896374, -0.17923762, 0.23122...   \n",
-       "3  [0.6896931, 0.5631939, 0.03267402, 0.30135256,...   \n",
-       "4  [0.7004955, 1.2301444, -1.3314008, 0.3130464, ...   \n",
-       "\n",
-       "                                         value_right         node1;label_temp  \\\n",
-       "0  [0.22757019, 1.3568109, 0.20142093, -0.136266,...                   temple   \n",
-       "1  [0.22757019, 1.3568109, 0.20142093, -0.136266,...                     tram   \n",
-       "2  [0.22757019, 1.3568109, 0.20142093, -0.136266,...        number of objects   \n",
-       "3  [-0.4857783, 1.0299897, -0.08442746, 0.0377928...  student organization of   \n",
-       "4  [-0.25474873, 1.0963922, -0.07334485, -0.05801...           award ceremony   \n",
-       "\n",
-       "  node2;label_temp  \n",
-       "0         quantity  \n",
-       "1         quantity  \n",
-       "2         quantity  \n",
-       "3       represents  \n",
-       "4         facet of  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "advance-bradley",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "36.53356313705444 s\n",
-      "45.23650908470154 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['node1;label_emb'] = pd.Series(list(getBERTSentEmbeddings(wikiDF_wabs1['node1;label_temp'], 'bert-base-nli-mean-tokens')))\n",
-    "wikiDF_wabs1['node2;label_emb'] = pd.Series(list(getBERTSentEmbeddings(wikiDF_wabs1['node2;label_temp'], 'bert-base-nli-mean-tokens')))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "grand-tooth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0         [1.1099516, 0.96340746, 1.9986696, -0.26332825...\n",
-       "1         [0.11062625, -0.8170154, 1.9477333, 0.6333619,...\n",
-       "2         [0.6716414, -0.08505987, 1.6483774, 1.0254748,...\n",
-       "3         [0.11234443, -0.34427452, 1.2779658, 0.5219726...\n",
-       "4         [-0.20389149, 0.1330459, 2.6067894, 0.7318787,...\n",
-       "                                ...                        \n",
-       "101766    [-0.19040215, 0.31874508, 0.7144903, 0.2607778...\n",
-       "101767    [-0.1481771, -0.42037064, 1.2999005, 0.4722842...\n",
-       "101768    [-0.38610217, -0.26171663, 1.5519874, 0.358043...\n",
-       "101769    [-0.32396185, -0.2601691, 1.5890372, 0.6090804...\n",
-       "101770    [0.12058014, 0.57743263, 1.3856344, 0.03655950...\n",
-       "Name: node1;label_emb, Length: 101771, dtype: object"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['node1;label_emb']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "satisfactory-minutes",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractEmbsLeft(row):\n",
-    "    if row['BERT_abstract_emb']:\n",
-    "        return row['BERT_abstract_emb']\n",
-    "    elif row['node1;label']:\n",
-    "        return row['node1;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_label_emb'] = wikiDF_wabs1.apply(fixAbstractEmbsLeft, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "alpha-dressing",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractEmbsRight(row):\n",
-    "    if row['BERT_abstract_emb_right']:\n",
-    "        return row['BERT_abstract_emb_right']\n",
-    "    elif row['node2;label']:\n",
-    "        return row['node2;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_label_emb_right'] = wikiDF_wabs1.apply(fixAbstractEmbsRight, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "short-consent",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractFirstSentEmbsLeft(row):\n",
-    "    if row['BERT_abstract_emb']:\n",
-    "        return row['BERT_abstract_firstSent_emb']\n",
-    "    elif row['node1;label']:\n",
-    "        return row['node1;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_emb'] = wikiDF_wabs1.apply(fixAbstractFirstSentEmbsLeft, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "derived-wholesale",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def fixAbstractFirstSentEmbsRight(row):\n",
-    "    if row['BERT_abstract_emb_right']:\n",
-    "        return row['BERT_abstract_firstSent_emb_right']\n",
-    "    elif row['node2;label']:\n",
-    "        return row['node2;label_emb'].tolist()\n",
-    "    else:\n",
-    "        return None\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_emb_right'] = wikiDF_wabs1.apply(fixAbstractFirstSentEmbsRight, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "waiting-wholesale",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getCosSims(wikiDF_wabs1, col1, col2):\n",
-    "    return wikiDF_wabs1.apply(lambda p: cosine_similarity(np.array(p[col1]).reshape(1,-1), np.array(p[col2]).reshape(1,-1))[0][0] if (type(p[col1])==list and type(p[col2])==list) else -1, axis=1)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "invalid-recommendation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abstract_emb', 'BERT_abstract_emb_right')\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abstract_firstSent_emb', 'BERT_abstract_firstSent_emb_right')\n",
-    "wikiDF_wabs1['complex_cosSim'] = getCosSims(wikiDF_wabs1, 'node1_compl_emb', 'node2_compl_emb')\n",
-    "wikiDF_wabs1['transe_cosSim'] = getCosSims(wikiDF_wabs1, 'node1_transe_emb', 'node2_transe_emb')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "moderate-bulgarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def changeScale(val1, minVal=0, maxVal=1):\n",
-    "    return 4 - 3 * (val1 - minVal) / (maxVal - minVal) if val1 != -1 else -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "id": "imported-offer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    30740.000000\n",
-       "mean         0.664270\n",
-       "std          0.132545\n",
-       "min          0.054277\n",
-       "25%          0.577663\n",
-       "50%          0.678386\n",
-       "75%          0.764839\n",
-       "max          1.000000\n",
-       "Name: BERT_abstract_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_cosSim'][wikiDF_wabs1['BERT_abstract_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "id": "assumed-personality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    30740.000000\n",
-       "mean         0.614450\n",
-       "std          0.157435\n",
-       "min         -0.073644\n",
-       "25%          0.507432\n",
-       "50%          0.627924\n",
-       "75%          0.731958\n",
-       "max          1.000000\n",
-       "Name: BERT_abstract_firstSent_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_firstSent_cosSim'][wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "id": "competitive-practice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    98495.000000\n",
-       "mean         0.654858\n",
-       "std          0.154211\n",
-       "min         -0.033131\n",
-       "25%          0.560126\n",
-       "50%          0.671793\n",
-       "75%          0.764953\n",
-       "max          1.000000\n",
-       "Name: complex_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['complex_cosSim'][wikiDF_wabs1['complex_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "historical-andrews",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    98495.000000\n",
-       "mean         0.652252\n",
-       "std          0.176201\n",
-       "min         -0.137044\n",
-       "25%          0.528159\n",
-       "50%          0.656617\n",
-       "75%          0.784122\n",
-       "max          1.000000\n",
-       "Name: transe_cosSim, dtype: float64"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['transe_cosSim'][wikiDF_wabs1['transe_cosSim'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "buried-catalyst",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    97964.000000\n",
-       "mean         1.809035\n",
-       "std          0.387920\n",
-       "min          1.000000\n",
-       "25%          1.524458\n",
-       "50%          1.767705\n",
-       "75%          2.053570\n",
-       "max          4.000000\n",
-       "Name: textEmb_score, dtype: float64"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1['textEmb_score'][wikiDF_wabs1['textEmb_score'] != -1].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "unlimited-minnesota",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.05427744093567516"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "id": "false-sheep",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abstract_score'] = wikiDF_wabs1['BERT_abstract_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abstract_cosSim'].where(wikiDF_wabs1['BERT_abstract_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abstract_cosSim'].max()))\n",
-    "wikiDF_wabs1['BERT_abstract_firstSent_score'] = wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].where(wikiDF_wabs1['BERT_abstract_firstSent_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].max()))\n",
-    "wikiDF_wabs1['complex_score'] = wikiDF_wabs1['complex_cosSim'].apply(changeScale, args=(wikiDF_wabs1['complex_cosSim'].where(wikiDF_wabs1['complex_cosSim'] != -1).min(), wikiDF_wabs1['complex_cosSim'].max()))\n",
-    "wikiDF_wabs1['transe_score'] = wikiDF_wabs1['transe_cosSim'].apply(changeScale, args=(wikiDF_wabs1['transe_cosSim'].where(wikiDF_wabs1['transe_cosSim'] != -1).min(), wikiDF_wabs1['transe_cosSim'].max()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "caring-passenger",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1['BERT_abstract_score'] = wikiDF_wabs1['BERT_abstract_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['BERT_abstract_firstSent_score'] = wikiDF_wabs1['BERT_abstract_firstSent_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['complex_score'] = wikiDF_wabs1['complex_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n",
-    "# wikiDF_wabs1['transe_score'] = wikiDF_wabs1['transe_cosSim'].apply(lambda p: 4 - 3 * p if p != -1 else -1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "weighted-heavy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['textEmb_cosSim'] = getCosSims(wikiDF_wabs1, 'value', 'value_right')\n",
-    "wikiDF_wabs1['textEmb_score'] = wikiDF_wabs1['textEmb_cosSim'].apply(changeScale, args=(wikiDF_wabs1['textEmb_cosSim'].where(wikiDF_wabs1['textEmb_cosSim'] != -1).min(), wikiDF_wabs1['textEmb_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "international-neighborhood",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abs_label_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abs_label_emb', 'BERT_abs_label_emb_right')\n",
-    "wikiDF_wabs1['BERT_abs_label_score'] = wikiDF_wabs1['BERT_abs_label_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abs_label_cosSim'].where(wikiDF_wabs1['BERT_abs_label_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abs_label_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "sized-coupon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'] = getCosSims(wikiDF_wabs1, 'BERT_abs_firstSent_label_emb', 'BERT_abs_firstSent_label_emb_right')\n",
-    "wikiDF_wabs1['BERT_abs_firstSent_label_score'] = wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].where(wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'] != -1).min(), wikiDF_wabs1['BERT_abs_firstSent_label_cosSim'].max()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "emotional-affect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wikiDF_wabs1 = wikiDF_wabs1.rename(columns={'value_score': 'textEmb_score'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "pleased-medline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BERT_abs_label_cosSim</th>\n",
-       "      <th>BERT_abstract_cosSim</th>\n",
-       "      <th>BERT_abstract_firstSent_cosSim</th>\n",
-       "      <th>complex_cosSim</th>\n",
-       "      <th>transe_cosSim</th>\n",
-       "      <th>textEmb_cosSim</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "      <td>101771.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.560186</td>\n",
-       "      <td>-0.497306</td>\n",
-       "      <td>-0.512354</td>\n",
-       "      <td>0.601589</td>\n",
-       "      <td>0.599066</td>\n",
-       "      <td>0.659361</td>\n",
-       "      <td>0.577693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.219777</td>\n",
-       "      <td>0.767613</td>\n",
-       "      <td>0.746306</td>\n",
-       "      <td>0.329139</td>\n",
-       "      <td>0.339258</td>\n",
-       "      <td>0.351968</td>\n",
-       "      <td>0.201635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-0.199269</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.200145</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.405427</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.544812</td>\n",
-       "      <td>0.512282</td>\n",
-       "      <td>0.624796</td>\n",
-       "      <td>0.439193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.569074</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.665646</td>\n",
-       "      <td>0.648872</td>\n",
-       "      <td>0.731336</td>\n",
-       "      <td>0.582915</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.728068</td>\n",
-       "      <td>0.536618</td>\n",
-       "      <td>0.458004</td>\n",
-       "      <td>0.761654</td>\n",
-       "      <td>0.779334</td>\n",
-       "      <td>0.817482</td>\n",
-       "      <td>0.723494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       BERT_abs_label_cosSim  BERT_abstract_cosSim  \\\n",
-       "count          101771.000000         101771.000000   \n",
-       "mean                0.560186             -0.497306   \n",
-       "std                 0.219777              0.767613   \n",
-       "min                -0.199269             -1.000000   \n",
-       "25%                 0.405427             -1.000000   \n",
-       "50%                 0.569074             -1.000000   \n",
-       "75%                 0.728068              0.536618   \n",
-       "max                 1.000000              1.000000   \n",
-       "\n",
-       "       BERT_abstract_firstSent_cosSim  complex_cosSim  transe_cosSim  \\\n",
-       "count                   101771.000000   101771.000000  101771.000000   \n",
-       "mean                        -0.512354        0.601589       0.599066   \n",
-       "std                          0.746306        0.329139       0.339258   \n",
-       "min                         -1.000000       -1.000000      -1.000000   \n",
-       "25%                         -1.000000        0.544812       0.512282   \n",
-       "50%                         -1.000000        0.665646       0.648872   \n",
-       "75%                          0.458004        0.761654       0.779334   \n",
-       "max                          1.000000        1.000000       1.000000   \n",
-       "\n",
-       "       textEmb_cosSim  BERT_abs_firstSent_label_cosSim  \n",
-       "count   101771.000000                    101771.000000  \n",
-       "mean         0.659361                         0.577693  \n",
-       "std          0.351968                         0.201635  \n",
-       "min         -1.000000                        -0.200145  \n",
-       "25%          0.624796                         0.439193  \n",
-       "50%          0.731336                         0.582915  \n",
-       "75%          0.817482                         0.723494  \n",
-       "max          1.000000                         1.000000  "
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1[filter(lambda p: 'cosSim' in p, wikiDF_wabs1.columns)].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "impossible-navigator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wikiDF_wabs1.to_csv('../data/M-Pairs/wikidataCS_wemb.csv', index=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "welsh-salem",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapper1 = {'/r/DistinctFrom': 'distinctness',\n",
-    "          '/r/Antonym': 'antonymy',\n",
-    "          '/r/Synonym': 'synonymy',\n",
-    "          '/r/SimilarTo': 'similarity',\n",
-    "          '/r/DerivedFrom': 'derivation',\n",
-    "          '/r/IsA': 'inheritance',\n",
-    "          '/r/PartOf': 'meronymy',\n",
-    "          '/r/MadeOf': 'material',\n",
-    "          '/r/CreatedBy': 'attribution',\n",
-    "          '/r/UsedFor': 'utility',\n",
-    "          '/r/HasProperty': 'properties',\n",
-    "          '/r/Causes': 'causation',\n",
-    "          '/r/HasPrerequisite': 'ordering',\n",
-    "          '/r/HasContext': 'context',\n",
-    "          '/r/RelatedTo': 'other'}\n",
-    "\n",
-    "wikiDF_wabs1['dim'] = wikiDF_wabs1['relation'].apply(lambda p: mapper1[p])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "common-building",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>[-0.21400201320648193, -0.1853264421224594, 2....</td>\n",
-       "      <td>0.111954</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.570280</td>\n",
-       "      <td>3.949128</td>\n",
-       "      <td>3.664139</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.066402</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.662764</td>\n",
-       "      <td>3.914897</td>\n",
-       "      <td>3.800794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6716414093971252, -0.08505986630916595, 1.6...</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.701743</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.519090</td>\n",
-       "      <td>1.894770</td>\n",
-       "      <td>1.894770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.11234442889690399, -0.34427452087402344, 1....</td>\n",
-       "      <td>[0.1607293337583542, -0.1969822645187378, 2.40...</td>\n",
-       "      <td>0.546000</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.110830</td>\n",
-       "      <td>2.362001</td>\n",
-       "      <td>2.362001</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>[-0.03644487261772156, -0.5494412183761597, 2....</td>\n",
-       "      <td>0.372485</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.443632</td>\n",
-       "      <td>3.798896</td>\n",
-       "      <td>2.882546</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                        BERT_abs_firstSent_label_emb  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....   \n",
-       "2  [0.6716414093971252, -0.08505986630916595, 1.6...   \n",
-       "3  [0.11234442889690399, -0.34427452087402344, 1....   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...   \n",
-       "\n",
-       "                  BERT_abs_firstSent_label_emb_right  \\\n",
-       "0  [-0.21400201320648193, -0.1853264421224594, 2....   \n",
-       "1  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "2  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "3  [0.1607293337583542, -0.1969822645187378, 2.40...   \n",
-       "4  [-0.03644487261772156, -0.5494412183761597, 2....   \n",
-       "\n",
-       "  BERT_abs_firstSent_label_cosSim BERT_abstract_score  \\\n",
-       "0                        0.111954                -1.0   \n",
-       "1                        0.066402                -1.0   \n",
-       "2                        0.701743                -1.0   \n",
-       "3                        0.546000                -1.0   \n",
-       "4                        0.372485                -1.0   \n",
-       "\n",
-       "  BERT_abstract_firstSent_score complex_score transe_score textEmb_score  \\\n",
-       "0                          -1.0          -1.0         -1.0      2.570280   \n",
-       "1                          -1.0          -1.0         -1.0      2.662764   \n",
-       "2                          -1.0          -1.0         -1.0      2.519090   \n",
-       "3                          -1.0          -1.0         -1.0      2.110830   \n",
-       "4                          -1.0          -1.0         -1.0      2.443632   \n",
-       "\n",
-       "  BERT_abs_label_score BERT_abs_firstSent_label_score  \n",
-       "0             3.949128                       3.664139  \n",
-       "1             3.914897                       3.800794  \n",
-       "2             1.894770                       1.894770  \n",
-       "3             2.362001                       2.362001  \n",
-       "4             3.798896                       2.882546  \n",
-       "\n",
-       "[5 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "id": "polar-constitutional",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>0 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [node2, node1, relation, node1;label, node2;label, relation;label, relation;dimension, source, sentence, node1_compl_emb, node2_compl_emb, node1_transe_emb, node2_transe_emb, abstract, abstract_firstSent, BERT_abstract_emb, BERT_abstract_firstSent_emb, abstract_right, abstract_firstSent_right, BERT_abstract_emb_right, BERT_abstract_firstSent_emb_right, value, value_right, node1;label_temp, node2;label_temp, node1;label_emb, node2;label_emb, BERT_abs_label_emb, BERT_abs_label_emb_right, BERT_abs_label_cosSim, BERT_abstract_cosSim, BERT_abstract_firstSent_cosSim, complex_cosSim, transe_cosSim, textEmb_cosSim, dim, BERT_abs_firstSent_label_emb, BERT_abs_firstSent_label_emb_right, BERT_abs_firstSent_label_cosSim, BERT_abstract_score, BERT_abstract_firstSent_score, complex_score, transe_score, textEmb_score, BERT_abs_label_score, BERT_abs_firstSent_label_score]\n",
-       "Index: []\n",
-       "\n",
-       "[0 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1[(wikiDF_wabs1.dim == 'other') & (wikiDF_wabs1.complex_score != -1)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "id": "revolutionary-benchmark",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     72707\n",
-       "meronymy         6886\n",
-       "context          5541\n",
-       "distinctness     4934\n",
-       "utility          2243\n",
-       "antonymy         2184\n",
-       "ordering         2107\n",
-       "material         1426\n",
-       "synonymy         1070\n",
-       "properties       1049\n",
-       "derivation        540\n",
-       "causation         510\n",
-       "similarity        345\n",
-       "attribution       187\n",
-       "other              42\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 341,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.dim.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "id": "naughty-station",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "cnts = []\n",
-    "for col in scoreCols:\n",
-    "    cnts.append(wikiDF_wabs1[wikiDF_wabs1[col] > -1].groupby('dim')[col].agg('count'))\n",
-    "cnts = pd.concat(cnts, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 343,
-   "id": "missing-fighter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2131.0</td>\n",
-       "      <td>2131.0</td>\n",
-       "      <td>2154</td>\n",
-       "      <td>2184</td>\n",
-       "      <td>2184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>181.0</td>\n",
-       "      <td>181.0</td>\n",
-       "      <td>181</td>\n",
-       "      <td>187</td>\n",
-       "      <td>187</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>494.0</td>\n",
-       "      <td>494.0</td>\n",
-       "      <td>481</td>\n",
-       "      <td>510</td>\n",
-       "      <td>510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>5409.0</td>\n",
-       "      <td>5409.0</td>\n",
-       "      <td>5413</td>\n",
-       "      <td>5541</td>\n",
-       "      <td>5541</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>537.0</td>\n",
-       "      <td>537.0</td>\n",
-       "      <td>524</td>\n",
-       "      <td>540</td>\n",
-       "      <td>540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>4831.0</td>\n",
-       "      <td>4831.0</td>\n",
-       "      <td>4807</td>\n",
-       "      <td>4934</td>\n",
-       "      <td>4934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>70211.0</td>\n",
-       "      <td>70211.0</td>\n",
-       "      <td>69793</td>\n",
-       "      <td>72707</td>\n",
-       "      <td>72707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1403.0</td>\n",
-       "      <td>1403.0</td>\n",
-       "      <td>1372</td>\n",
-       "      <td>1426</td>\n",
-       "      <td>1426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>6791.0</td>\n",
-       "      <td>6791.0</td>\n",
-       "      <td>6504</td>\n",
-       "      <td>6886</td>\n",
-       "      <td>6886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2105.0</td>\n",
-       "      <td>2105.0</td>\n",
-       "      <td>2103</td>\n",
-       "      <td>2107</td>\n",
-       "      <td>2107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>833.0</td>\n",
-       "      <td>833.0</td>\n",
-       "      <td>1026</td>\n",
-       "      <td>1049</td>\n",
-       "      <td>1049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>343.0</td>\n",
-       "      <td>343.0</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1033.0</td>\n",
-       "      <td>1033.0</td>\n",
-       "      <td>1030</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>1070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2193.0</td>\n",
-       "      <td>2193.0</td>\n",
-       "      <td>2189</td>\n",
-       "      <td>2243</td>\n",
-       "      <td>2243</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>42</td>\n",
-       "      <td>42</td>\n",
-       "      <td>42</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy             2131.0        2131.0           2154   \n",
-       "attribution           181.0         181.0            181   \n",
-       "causation             494.0         494.0            481   \n",
-       "context              5409.0        5409.0           5413   \n",
-       "derivation            537.0         537.0            524   \n",
-       "distinctness         4831.0        4831.0           4807   \n",
-       "inheritance         70211.0       70211.0          69793   \n",
-       "material             1403.0        1403.0           1372   \n",
-       "meronymy             6791.0        6791.0           6504   \n",
-       "ordering             2105.0        2105.0           2103   \n",
-       "properties            833.0         833.0           1026   \n",
-       "similarity            343.0         343.0            345   \n",
-       "synonymy             1033.0        1033.0           1030   \n",
-       "utility              2193.0        2193.0           2189   \n",
-       "other                   NaN           NaN             42   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                      2184                            2184  \n",
-       "attribution                    187                             187  \n",
-       "causation                      510                             510  \n",
-       "context                       5541                            5541  \n",
-       "derivation                     540                             540  \n",
-       "distinctness                  4934                            4934  \n",
-       "inheritance                  72707                           72707  \n",
-       "material                      1426                            1426  \n",
-       "meronymy                      6886                            6886  \n",
-       "ordering                      2107                            2107  \n",
-       "properties                    1049                            1049  \n",
-       "similarity                     345                             345  \n",
-       "synonymy                      1070                            1070  \n",
-       "utility                       2243                            2243  \n",
-       "other                           42                              42  "
-      ]
-     },
-     "execution_count": 343,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "id": "endless-globe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                      98495.0\n",
-       "transe_score                       98495.0\n",
-       "textEmb_score                      97964.0\n",
-       "BERT_abs_label_score              101771.0\n",
-       "BERT_abs_firstSent_label_score    101771.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 344,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "id": "perfect-eagle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                     0.967810\n",
-       "transe_score                      0.967810\n",
-       "textEmb_score                     0.962592\n",
-       "BERT_abs_label_score              1.000000\n",
-       "BERT_abs_firstSent_label_score    1.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 345,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()/len(wikiDF_wabs1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 346,
-   "id": "apart-moisture",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>0.327302</td>\n",
-       "      <td>0.366009</td>\n",
-       "      <td>0.434431</td>\n",
-       "      <td>0.500274</td>\n",
-       "      <td>0.466038</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>0.364051</td>\n",
-       "      <td>0.417825</td>\n",
-       "      <td>0.322049</td>\n",
-       "      <td>0.476401</td>\n",
-       "      <td>0.398419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>0.320966</td>\n",
-       "      <td>0.372731</td>\n",
-       "      <td>0.395919</td>\n",
-       "      <td>0.511707</td>\n",
-       "      <td>0.480563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>0.386523</td>\n",
-       "      <td>0.408721</td>\n",
-       "      <td>0.419279</td>\n",
-       "      <td>0.540979</td>\n",
-       "      <td>0.504633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>0.331256</td>\n",
-       "      <td>0.388491</td>\n",
-       "      <td>0.327087</td>\n",
-       "      <td>0.433289</td>\n",
-       "      <td>0.425505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>0.370387</td>\n",
-       "      <td>0.420851</td>\n",
-       "      <td>0.373756</td>\n",
-       "      <td>0.526051</td>\n",
-       "      <td>0.463646</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>0.439842</td>\n",
-       "      <td>0.448856</td>\n",
-       "      <td>0.373036</td>\n",
-       "      <td>0.540227</td>\n",
-       "      <td>0.494134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>0.401528</td>\n",
-       "      <td>0.448309</td>\n",
-       "      <td>0.378352</td>\n",
-       "      <td>0.571034</td>\n",
-       "      <td>0.543981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>0.353069</td>\n",
-       "      <td>0.408219</td>\n",
-       "      <td>0.396728</td>\n",
-       "      <td>0.540599</td>\n",
-       "      <td>0.486378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>0.201187</td>\n",
-       "      <td>0.181561</td>\n",
-       "      <td>0.279172</td>\n",
-       "      <td>0.360187</td>\n",
-       "      <td>0.352700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>0.388960</td>\n",
-       "      <td>0.377157</td>\n",
-       "      <td>0.407805</td>\n",
-       "      <td>0.582726</td>\n",
-       "      <td>0.523747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>0.308485</td>\n",
-       "      <td>0.316569</td>\n",
-       "      <td>0.301411</td>\n",
-       "      <td>0.500654</td>\n",
-       "      <td>0.435636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>0.352660</td>\n",
-       "      <td>0.339445</td>\n",
-       "      <td>0.332432</td>\n",
-       "      <td>0.532540</td>\n",
-       "      <td>0.461569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>0.335069</td>\n",
-       "      <td>0.351518</td>\n",
-       "      <td>0.398808</td>\n",
-       "      <td>0.515661</td>\n",
-       "      <td>0.476737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.334116</td>\n",
-       "      <td>0.581674</td>\n",
-       "      <td>0.450678</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           0.327302      0.366009       0.434431   \n",
-       "attribution        0.364051      0.417825       0.322049   \n",
-       "causation          0.320966      0.372731       0.395919   \n",
-       "context            0.386523      0.408721       0.419279   \n",
-       "derivation         0.331256      0.388491       0.327087   \n",
-       "distinctness       0.370387      0.420851       0.373756   \n",
-       "inheritance        0.439842      0.448856       0.373036   \n",
-       "material           0.401528      0.448309       0.378352   \n",
-       "meronymy           0.353069      0.408219       0.396728   \n",
-       "ordering           0.201187      0.181561       0.279172   \n",
-       "properties         0.388960      0.377157       0.407805   \n",
-       "similarity         0.308485      0.316569       0.301411   \n",
-       "synonymy           0.352660      0.339445       0.332432   \n",
-       "utility            0.335069      0.351518       0.398808   \n",
-       "other                   NaN           NaN       0.334116   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  0.500274                        0.466038  \n",
-       "attribution               0.476401                        0.398419  \n",
-       "causation                 0.511707                        0.480563  \n",
-       "context                   0.540979                        0.504633  \n",
-       "derivation                0.433289                        0.425505  \n",
-       "distinctness              0.526051                        0.463646  \n",
-       "inheritance               0.540227                        0.494134  \n",
-       "material                  0.571034                        0.543981  \n",
-       "meronymy                  0.540599                        0.486378  \n",
-       "ordering                  0.360187                        0.352700  \n",
-       "properties                0.582726                        0.523747  \n",
-       "similarity                0.500654                        0.435636  \n",
-       "synonymy                  0.532540                        0.461569  \n",
-       "utility                   0.515661                        0.476737  \n",
-       "other                     0.581674                        0.450678  "
-      ]
-     },
-     "execution_count": 346,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summsStd = []\n",
-    "for col in scoreCols:\n",
-    "    summsStd.append(wikiDF_wabs1[(wikiDF_wabs1[col] > -1) & ~(wikiDF_wabs1[col].isna())].groupby('dim')[col].agg('std'))\n",
-    "summsStd = pd.concat(summsStd, axis=1)\n",
-    "summsStd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "id": "italic-rehabilitation",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
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-       "      <th>antonymy</th>\n",
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-       "      <td>1.890103</td>\n",
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-       "      <td>1.961361</td>\n",
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-       "      <th>attribution</th>\n",
-       "      <td>2.058412</td>\n",
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-       "      <th>causation</th>\n",
-       "      <td>1.878191</td>\n",
-       "      <td>2.074840</td>\n",
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-       "      <td>2.034320</td>\n",
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-       "      <td>2.142938</td>\n",
-       "      <td>2.086860</td>\n",
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-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>2.101236</td>\n",
-       "      <td>2.236187</td>\n",
-       "      <td>2.117177</td>\n",
-       "      <td>2.080477</td>\n",
-       "      <td>2.041239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>1.762127</td>\n",
-       "      <td>1.689348</td>\n",
-       "      <td>1.742884</td>\n",
-       "      <td>1.961034</td>\n",
-       "      <td>1.892194</td>\n",
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-       "      <th>inheritance</th>\n",
-       "      <td>2.052364</td>\n",
-       "      <td>1.924857</td>\n",
-       "      <td>1.802573</td>\n",
-       "      <td>2.141092</td>\n",
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-       "      <td>2.178909</td>\n",
-       "      <td>1.986060</td>\n",
-       "      <td>2.117815</td>\n",
-       "      <td>2.062357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>1.933547</td>\n",
-       "      <td>1.889699</td>\n",
-       "      <td>1.852277</td>\n",
-       "      <td>2.042396</td>\n",
-       "      <td>1.989395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1.244715</td>\n",
-       "      <td>1.161449</td>\n",
-       "      <td>1.337445</td>\n",
-       "      <td>1.319064</td>\n",
-       "      <td>1.322444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1.988557</td>\n",
-       "      <td>2.172564</td>\n",
-       "      <td>2.017539</td>\n",
-       "      <td>2.193653</td>\n",
-       "      <td>2.095989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>1.791589</td>\n",
-       "      <td>1.897246</td>\n",
-       "      <td>1.554273</td>\n",
-       "      <td>2.025497</td>\n",
-       "      <td>1.945071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1.769929</td>\n",
-       "      <td>1.696103</td>\n",
-       "      <td>1.678028</td>\n",
-       "      <td>1.998886</td>\n",
-       "      <td>1.880054</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>1.927094</td>\n",
-       "      <td>2.132264</td>\n",
-       "      <td>1.918617</td>\n",
-       "      <td>2.080665</td>\n",
-       "      <td>2.037705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.773898</td>\n",
-       "      <td>2.134766</td>\n",
-       "      <td>1.918897</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           1.472307      1.552708       1.890103   \n",
-       "attribution        2.058412      2.369082       1.795290   \n",
-       "causation          1.878191      2.074840       1.894913   \n",
-       "context            2.198949      2.287010       1.925819   \n",
-       "derivation         2.101236      2.236187       2.117177   \n",
-       "distinctness       1.762127      1.689348       1.742884   \n",
-       "inheritance        2.052364      1.924857       1.802573   \n",
-       "material           2.181508      2.178909       1.986060   \n",
-       "meronymy           1.933547      1.889699       1.852277   \n",
-       "ordering           1.244715      1.161449       1.337445   \n",
-       "properties         1.988557      2.172564       2.017539   \n",
-       "similarity         1.791589      1.897246       1.554273   \n",
-       "synonymy           1.769929      1.696103       1.678028   \n",
-       "utility            1.927094      2.132264       1.918617   \n",
-       "other                   NaN           NaN       1.773898   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  1.928249                        1.961361  \n",
-       "attribution               2.003034                        1.864052  \n",
-       "causation                 2.058835                        2.034320  \n",
-       "context                   2.142938                        2.086860  \n",
-       "derivation                2.080477                        2.041239  \n",
-       "distinctness              1.961034                        1.892194  \n",
-       "inheritance               2.141092                        2.098482  \n",
-       "material                  2.117815                        2.062357  \n",
-       "meronymy                  2.042396                        1.989395  \n",
-       "ordering                  1.319064                        1.322444  \n",
-       "properties                2.193653                        2.095989  \n",
-       "similarity                2.025497                        1.945071  \n",
-       "synonymy                  1.998886                        1.880054  \n",
-       "utility                   2.080665                        2.037705  \n",
-       "other                     2.134766                        1.918897  "
-      ]
-     },
-     "execution_count": 347,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summs = []\n",
-    "for col in scoreCols:\n",
-    "    summs.append(wikiDF_wabs1[(wikiDF_wabs1[col] > -1) & ~(wikiDF_wabs1[col].isna())].groupby('dim')[col].agg('mean'))\n",
-    "summs = pd.concat(summs, axis=1)\n",
-    "summs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "id": "abandoned-armor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dim\n",
-       "ordering        1.277024\n",
-       "antonymy        1.760945\n",
-       "synonymy        1.804600\n",
-       "distinctness    1.809517\n",
-       "similarity      1.842735\n",
-       "meronymy        1.941463\n",
-       "other           1.942520\n",
-       "causation       1.988220\n",
-       "inheritance     2.003874\n",
-       "attribution     2.017974\n",
-       "utility         2.019269\n",
-       "properties      2.093660\n",
-       "material        2.105330\n",
-       "derivation      2.115263\n",
-       "context         2.128315\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 348,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summs.apply(lambda p: np.mean(list(filter(lambda y12: y12 != -1, [p[x] if not(pd.isna(p[x])) else -1 for x in scoreCols]))), axis=1).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "id": "accurate-faculty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "      <th>antonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  1             2              3   \n",
-       "attribution               4             5              1   \n",
-       "causation                 1             5              2   \n",
-       "context                   4             5              1   \n",
-       "derivation                3             5              4   \n",
-       "distinctness              3             1              2   \n",
-       "inheritance               3             2              1   \n",
-       "material                  5             4              1   \n",
-       "meronymy                  3             2              1   \n",
-       "ordering                  2             1              5   \n",
-       "properties                1             4              2   \n",
-       "similarity                2             3              1   \n",
-       "synonymy                  3             2              1   \n",
-       "utility                   2             5              1   \n",
-       "other                     4             5              1   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         4                               5  \n",
-       "attribution                      3                               2  \n",
-       "causation                        4                               3  \n",
-       "context                          3                               2  \n",
-       "derivation                       2                               1  \n",
-       "distinctness                     5                               4  \n",
-       "inheritance                      5                               4  \n",
-       "material                         3                               2  \n",
-       "meronymy                         5                               4  \n",
-       "ordering                         3                               4  \n",
-       "properties                       5                               3  \n",
-       "similarity                       5                               4  \n",
-       "synonymy                         5                               4  \n",
-       "utility                          4                               3  \n",
-       "other                            3                               2  "
-      ]
-     },
-     "execution_count": 349,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosRowWise(row):\n",
-    "    return (np.array([row[col] for col in scoreCols])).argsort().argsort() + 1\n",
-    "pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 352,
-   "id": "substantial-seven",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='dim'>"
-      ]
-     },
-     "execution_count": 352,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = pyplot.subplots(figsize=(15,10))\n",
-    "sns.heatmap(pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "id": "spatial-wrist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>attribution</th>\n",
-       "      <td>11</td>\n",
-       "      <td>14</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>6</td>\n",
-       "      <td>8</td>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>14</td>\n",
-       "      <td>13</td>\n",
-       "      <td>12</td>\n",
-       "      <td>14</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>derivation</th>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>15</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>13</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>13</td>\n",
-       "      <td>11</td>\n",
-       "      <td>13</td>\n",
-       "      <td>11</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "      <td>14</td>\n",
-       "      <td>15</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>similarity</th>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>7</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>15</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             2              9   \n",
-       "attribution              11            14              6   \n",
-       "causation                 6             8             10   \n",
-       "context                  14            13             12   \n",
-       "derivation               12            12             15   \n",
-       "distinctness              3             3              4   \n",
-       "inheritance              10             7              7   \n",
-       "material                 13            11             13   \n",
-       "meronymy                  8             5              8   \n",
-       "ordering                  1             1              1   \n",
-       "properties                9            10             14   \n",
-       "similarity                5             6              2   \n",
-       "synonymy                  4             4              3   \n",
-       "utility                   7             9             11   \n",
-       "other                    15            15              5   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         2                               7  \n",
-       "attribution                      5                               2  \n",
-       "causation                        8                               9  \n",
-       "context                         14                              13  \n",
-       "derivation                       9                              11  \n",
-       "distinctness                     3                               4  \n",
-       "inheritance                     13                              15  \n",
-       "material                        11                              12  \n",
-       "meronymy                         7                               8  \n",
-       "ordering                         1                               1  \n",
-       "properties                      15                              14  \n",
-       "similarity                       6                               6  \n",
-       "synonymy                         4                               3  \n",
-       "utility                         10                              10  \n",
-       "other                           12                               5  "
-      ]
-     },
-     "execution_count": 353,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosColWise(colVals):\n",
-    "    return pd.Series(((np.array([colVals.to_list()])).argsort().argsort() + 1)[0], index=summs.index, name=colVals.name)\n",
-    "pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "id": "confused-miami",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='dim'>"
-      ]
-     },
-     "execution_count": 354,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = pyplot.subplots(figsize=(15,10))\n",
-    "sns.heatmap(pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15), annot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "rocky-power",
-   "metadata": {},
-   "source": [
-    "# Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "alive-surprise",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "properties\n",
-      "                        node1;label node2;label\n",
-      "0                            temple    quantity\n",
-      "1                              tram    quantity\n",
-      "2                 number of objects    quantity\n",
-      "4                    award ceremony    facet of\n",
-      "5  geography of geographic location    facet of\n",
-      "other\n",
-      "                node1;label    node2;label\n",
-      "3   student organization of     represents\n",
-      "20                     name       nickname\n",
-      "25        mission statement     motto text\n",
-      "42              house organ  official blog\n",
-      "44                 defender     prosecutor\n",
-      "inheritance\n",
-      "             node1;label           node2;label\n",
-      "69              nickname                  name\n",
-      "214    geometric algebra  algebra over a field\n",
-      "215     division algebra  algebra over a field\n",
-      "217     operator algebra  algebra over a field\n",
-      "219  alternative algebra  algebra over a field\n",
-      "context\n",
-      "            node1;label             node2;label\n",
-      "216            mutation    algebra over a field\n",
-      "228           filmmaker     cinematic technique\n",
-      "254     film technician     cinematic technique\n",
-      "264  periodical library              periodical\n",
-      "393                 web  mechanical engineering\n",
-      "distinctness\n",
-      "             node1;label           node2;label\n",
-      "218  algebra over a ring  algebra over a field\n",
-      "300         power series   formal power series\n",
-      "311              testing                  test\n",
-      "315   test  (assessment)                  test\n",
-      "338                 lawn             grassland\n",
-      "meronymy\n",
-      "             node1;label         node2;label\n",
-      "257  decomposition model  geometric modeling\n",
-      "258          solid model  geometric modeling\n",
-      "262               volume          periodical\n",
-      "279               column          periodical\n",
-      "281                issue          periodical\n",
-      "antonymy\n",
-      "                    node1;label   node2;label\n",
-      "260  non-periodical publication    periodical\n",
-      "340              younger sister  elder sister\n",
-      "341               elder brother  elder sister\n",
-      "425              composite knot    prime knot\n",
-      "526                      prefix        suffix\n",
-      "utility\n",
-      "             node1;label       node2;label\n",
-      "308  elevator test tower              test\n",
-      "319           dummy load              test\n",
-      "479            prototype        experiment\n",
-      "591        running shoes  indoor athletics\n",
-      "597       room and board          boarding\n",
-      "attribution\n",
-      "             node1;label                node2;label\n",
-      "324          test result                       test\n",
-      "328   electronic circuit    through-hole technology\n",
-      "1239             shotgun              rifle factory\n",
-      "1644   image information  information visualization\n",
-      "1985    furniture fabric            tapestry stitch\n",
-      "derivation\n",
-      "               node1;label  node2;label\n",
-      "386              surrogate      acronym\n",
-      "428              milestone    milestone\n",
-      "435  fictional brown dwarf  brown dwarf\n",
-      "582                   rose         rose\n",
-      "755  fictional cargo pants  cargo pants\n",
-      "synonymy\n",
-      "     node1;label     node2;label\n",
-      "418   after-name     family name\n",
-      "534   playwright    stage author\n",
-      "539       knight          knight\n",
-      "575  resort town  seaside resort\n",
-      "608     mourning           grief\n",
-      "material\n",
-      "                    node1;label        node2;label\n",
-      "586       list of minor planets       minor planet\n",
-      "676   list of educational years   educational year\n",
-      "724  list of martial arts films  martial arts film\n",
-      "828                  fur collar           fake fur\n",
-      "857          champagne cocktail         sugar cube\n",
-      "causation\n",
-      "            node1;label      node2;label\n",
-      "606                loss            grief\n",
-      "736   double beta decay    beta particle\n",
-      "778              flight  wake turbulence\n",
-      "1896              spice        longevity\n",
-      "1961       brain cancer          vertigo\n",
-      "ordering\n",
-      "              node1;label         node2;label\n",
-      "766              tomorrow  day after tomorrow\n",
-      "896              group 15            group 16\n",
-      "1740     secondary source     tertiary source\n",
-      "1965  construction permit      groundbreaking\n",
-      "2065                 kilo                mega\n",
-      "similarity\n",
-      "             node1;label            node2;label\n",
-      "1039           expertise              specialty\n",
-      "1040       skilled trade              specialty\n",
-      "1224  multi-purpose hall  multi-purpose stadium\n",
-      "1369      signature song         one-hit wonder\n",
-      "1370  nine days\\' wonder         one-hit wonder\n"
-     ]
-    }
-   ],
-   "source": [
-    "for dim1 in wikiDF_wabs1.dim.unique():\n",
-    "    print(dim1)\n",
-    "    print(wikiDF_wabs1[wikiDF_wabs1.dim == dim1].head()[['node1;label', 'node2;label']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "compact-right",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vocal-convertible",
-   "metadata": {},
-   "source": [
-    "# Sampling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "laden-kentucky",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils.random import sample_without_replacement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "id": "eastern-worth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_wikiDF = wikiDF_wabs1.iloc[sample_without_replacement(len(wikiDF_wabs1), 100, random_state=1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "id": "junior-service",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-247-5e0097ea79b5>:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['complex_score'] = sample_wikiDF['complex_cosSim'].apply(changeScale, args=(sample_wikiDF['complex_cosSim'].where(sample_wikiDF['complex_cosSim'] != -1).min(), sample_wikiDF['complex_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['transe_score'] = sample_wikiDF['transe_cosSim'].apply(changeScale, args=(sample_wikiDF['transe_cosSim'].where(sample_wikiDF['transe_cosSim'] != -1).min(), sample_wikiDF['transe_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['textEmb_score'] = sample_wikiDF['textEmb_cosSim'].apply(changeScale, args=(sample_wikiDF['textEmb_cosSim'].where(sample_wikiDF['textEmb_cosSim'] != -1).min(), sample_wikiDF['textEmb_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['BERT_abs_label_score'] = sample_wikiDF['BERT_abs_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_label_cosSim'].where(sample_wikiDF['BERT_abs_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_label_cosSim'].max()))\n",
-      "<ipython-input-247-5e0097ea79b5>:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  sample_wikiDF['BERT_abs_firstSent_label_score'] = sample_wikiDF['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_firstSent_label_cosSim'].where(sample_wikiDF['BERT_abs_firstSent_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_firstSent_label_cosSim'].max()))\n"
-     ]
-    }
-   ],
-   "source": [
-    "sample_wikiDF['complex_score'] = sample_wikiDF['complex_cosSim'].apply(changeScale, args=(sample_wikiDF['complex_cosSim'].where(sample_wikiDF['complex_cosSim'] != -1).min(), sample_wikiDF['complex_cosSim'].max()))\n",
-    "sample_wikiDF['transe_score'] = sample_wikiDF['transe_cosSim'].apply(changeScale, args=(sample_wikiDF['transe_cosSim'].where(sample_wikiDF['transe_cosSim'] != -1).min(), sample_wikiDF['transe_cosSim'].max()))\n",
-    "sample_wikiDF['textEmb_score'] = sample_wikiDF['textEmb_cosSim'].apply(changeScale, args=(sample_wikiDF['textEmb_cosSim'].where(sample_wikiDF['textEmb_cosSim'] != -1).min(), sample_wikiDF['textEmb_cosSim'].max()))\n",
-    "sample_wikiDF['BERT_abs_label_score'] = sample_wikiDF['BERT_abs_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_label_cosSim'].where(sample_wikiDF['BERT_abs_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_label_cosSim'].max()))\n",
-    "sample_wikiDF['BERT_abs_firstSent_label_score'] = sample_wikiDF['BERT_abs_firstSent_label_cosSim'].apply(changeScale, args=(sample_wikiDF['BERT_abs_firstSent_label_cosSim'].where(sample_wikiDF['BERT_abs_firstSent_label_cosSim'] != -1).min(), sample_wikiDF['BERT_abs_firstSent_label_cosSim'].max()))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "id": "musical-penny",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.373734</td>\n",
-       "      <td>2.467629</td>\n",
-       "      <td>2.171393</td>\n",
-       "      <td>2.162535</td>\n",
-       "      <td>2.348227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.897240</td>\n",
-       "      <td>0.942942</td>\n",
-       "      <td>0.727955</td>\n",
-       "      <td>0.594187</td>\n",
-       "      <td>0.658818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>1.926772</td>\n",
-       "      <td>2.081556</td>\n",
-       "      <td>1.711501</td>\n",
-       "      <td>1.788340</td>\n",
-       "      <td>1.874007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.457755</td>\n",
-       "      <td>2.581206</td>\n",
-       "      <td>2.042745</td>\n",
-       "      <td>2.161676</td>\n",
-       "      <td>2.360556</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.872336</td>\n",
-       "      <td>3.102654</td>\n",
-       "      <td>2.599425</td>\n",
-       "      <td>2.539433</td>\n",
-       "      <td>2.828806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       complex_score  transe_score  textEmb_score  BERT_abs_label_score  \\\n",
-       "count     100.000000    100.000000     100.000000            100.000000   \n",
-       "mean        2.373734      2.467629       2.171393              2.162535   \n",
-       "std         0.897240      0.942942       0.727955              0.594187   \n",
-       "min        -1.000000     -1.000000      -1.000000              1.000000   \n",
-       "25%         1.926772      2.081556       1.711501              1.788340   \n",
-       "50%         2.457755      2.581206       2.042745              2.161676   \n",
-       "75%         2.872336      3.102654       2.599425              2.539433   \n",
-       "max         4.000000      4.000000       4.000000              4.000000   \n",
-       "\n",
-       "       BERT_abs_firstSent_label_score  \n",
-       "count                      100.000000  \n",
-       "mean                         2.348227  \n",
-       "std                          0.658818  \n",
-       "min                          1.000000  \n",
-       "25%                          1.874007  \n",
-       "50%                          2.360556  \n",
-       "75%                          2.828806  \n",
-       "max                          4.000000  "
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "sample_wikiDF[scoreCols].describe()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "id": "guilty-berkeley",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inheritance     75\n",
-       "context          6\n",
-       "meronymy         5\n",
-       "distinctness     4\n",
-       "utility          3\n",
-       "antonymy         2\n",
-       "synonymy         1\n",
-       "causation        1\n",
-       "ordering         1\n",
-       "material         1\n",
-       "properties       1\n",
-       "Name: dim, dtype: int64"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample_wikiDF.dim.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "id": "funded-geology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "cnts = []\n",
-    "for col in scoreCols:\n",
-    "    cnts.append(sample_wikiDF[sample_wikiDF[col] > -1].groupby('dim')[col].agg('count'))\n",
-    "cnts = pd.concat(cnts, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "id": "threaded-writer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>74</td>\n",
-       "      <td>74</td>\n",
-       "      <td>75</td>\n",
-       "      <td>75</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             2              2   \n",
-       "causation                 1             1              1   \n",
-       "context                   6             6              6   \n",
-       "distinctness              3             3              3   \n",
-       "inheritance              74            74             75   \n",
-       "material                  1             1              1   \n",
-       "meronymy                  4             4              5   \n",
-       "ordering                  1             1              1   \n",
-       "properties                1             1              1   \n",
-       "synonymy                  1             1              1   \n",
-       "utility                   3             3              3   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         2                               2  \n",
-       "causation                        1                               1  \n",
-       "context                          6                               6  \n",
-       "distinctness                     4                               4  \n",
-       "inheritance                     75                              75  \n",
-       "material                         1                               1  \n",
-       "meronymy                         5                               5  \n",
-       "ordering                         1                               1  \n",
-       "properties                       1                               1  \n",
-       "synonymy                         1                               1  \n",
-       "utility                          3                               3  "
-      ]
-     },
-     "execution_count": 251,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "id": "weird-employer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                      97\n",
-       "transe_score                       97\n",
-       "textEmb_score                      99\n",
-       "BERT_abs_label_score              100\n",
-       "BERT_abs_firstSent_label_score    100\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "id": "excellent-principal",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex_score                     0.97\n",
-       "transe_score                      0.97\n",
-       "textEmb_score                     0.99\n",
-       "BERT_abs_label_score              1.00\n",
-       "BERT_abs_firstSent_label_score    1.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cnts.sum()/len(sample_wikiDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
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-       "      <th>dim</th>\n",
-       "      <th></th>\n",
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-       "      <th>antonymy</th>\n",
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-       "      <td>1.808202</td>\n",
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-       "      <th>distinctness</th>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>0.309635</td>\n",
-       "      <td>0.496549</td>\n",
-       "      <td>0.469327</td>\n",
-       "      <td>0.541985</td>\n",
-       "      <td>0.448966</td>\n",
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-       "      <th>ordering</th>\n",
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-       "      <th>properties</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>0.516512</td>\n",
-       "      <td>0.497344</td>\n",
-       "      <td>0.489440</td>\n",
-       "      <td>0.809166</td>\n",
-       "      <td>0.995372</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "</div>"
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-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           0.664200      0.078457       1.808202   \n",
-       "causation               NaN           NaN            NaN   \n",
-       "context            0.608626      0.665737       1.014546   \n",
-       "distinctness       0.478050      0.832449       0.247507   \n",
-       "inheritance        0.677163      0.717881       0.591921   \n",
-       "material                NaN           NaN            NaN   \n",
-       "meronymy           0.309635      0.496549       0.469327   \n",
-       "ordering                NaN           NaN            NaN   \n",
-       "properties              NaN           NaN            NaN   \n",
-       "synonymy                NaN           NaN            NaN   \n",
-       "utility            0.516512      0.497344       0.489440   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  1.247541                        1.091346  \n",
-       "causation                      NaN                             NaN  \n",
-       "context                   0.713613                        0.737005  \n",
-       "distinctness              0.662065                        0.737140  \n",
-       "inheritance               0.558481                        0.625973  \n",
-       "material                       NaN                             NaN  \n",
-       "meronymy                  0.541985                        0.448966  \n",
-       "ordering                       NaN                             NaN  \n",
-       "properties                     NaN                             NaN  \n",
-       "synonymy                       NaN                             NaN  \n",
-       "utility                   0.809166                        0.995372  "
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summsStd = []\n",
-    "for col in scoreCols:\n",
-    "    summsStd.append(sample_wikiDF[(sample_wikiDF[col] > -1) & ~(sample_wikiDF[col].isna())].groupby('dim')[col].agg('std'))\n",
-    "summsStd = pd.concat(summsStd, axis=1)\n",
-    "summsStd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "id": "bulgarian-essence",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>1.641262</td>\n",
-       "      <td>1.635409</td>\n",
-       "      <td>2.535406</td>\n",
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-       "      <td>2.550744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>2.803628</td>\n",
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-       "      <th>context</th>\n",
-       "      <td>2.733342</td>\n",
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-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>1.532177</td>\n",
-       "      <td>1.746123</td>\n",
-       "      <td>1.437266</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>2.564439</td>\n",
-       "      <td>2.598065</td>\n",
-       "      <td>2.190634</td>\n",
-       "      <td>2.110764</td>\n",
-       "      <td>2.324834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>2.501223</td>\n",
-       "      <td>2.950043</td>\n",
-       "      <td>3.033550</td>\n",
-       "      <td>2.970278</td>\n",
-       "      <td>3.233506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>2.344857</td>\n",
-       "      <td>2.852795</td>\n",
-       "      <td>2.102023</td>\n",
-       "      <td>1.742755</td>\n",
-       "      <td>1.817167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>1.531941</td>\n",
-       "      <td>1.496260</td>\n",
-       "      <td>1.084469</td>\n",
-       "      <td>2.612951</td>\n",
-       "      <td>2.893667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>2.118738</td>\n",
-       "      <td>3.175830</td>\n",
-       "      <td>3.486557</td>\n",
-       "      <td>2.236175</td>\n",
-       "      <td>2.988376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1.524502</td>\n",
-       "      <td>1.543396</td>\n",
-       "      <td>2.030404</td>\n",
-       "      <td>2.199188</td>\n",
-       "      <td>2.016465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>2.155438</td>\n",
-       "      <td>3.004060</td>\n",
-       "      <td>2.201133</td>\n",
-       "      <td>2.468939</td>\n",
-       "      <td>2.770659</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy           1.641262      1.635409       2.535406   \n",
-       "causation          2.803628      2.030427       2.769243   \n",
-       "context            2.733342      2.896265       2.490245   \n",
-       "distinctness       1.532177      1.746123       1.437266   \n",
-       "inheritance        2.564439      2.598065       2.190634   \n",
-       "material           2.501223      2.950043       3.033550   \n",
-       "meronymy           2.344857      2.852795       2.102023   \n",
-       "ordering           1.531941      1.496260       1.084469   \n",
-       "properties         2.118738      3.175830       3.486557   \n",
-       "synonymy           1.524502      1.543396       2.030404   \n",
-       "utility            2.155438      3.004060       2.201133   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                  2.525113                        2.550744  \n",
-       "causation                 2.061884                        2.102512  \n",
-       "context                   2.657254                        2.815580  \n",
-       "distinctness              2.187860                        1.958226  \n",
-       "inheritance               2.110764                        2.324834  \n",
-       "material                  2.970278                        3.233506  \n",
-       "meronymy                  1.742755                        1.817167  \n",
-       "ordering                  2.612951                        2.893667  \n",
-       "properties                2.236175                        2.988376  \n",
-       "synonymy                  2.199188                        2.016465  \n",
-       "utility                   2.468939                        2.770659  "
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scoreCols = ['complex_score', 'transe_score', 'textEmb_score', 'BERT_abs_label_score', 'BERT_abs_firstSent_label_score']\n",
-    "summs = []\n",
-    "for col in scoreCols:\n",
-    "    summs.append(sample_wikiDF[(sample_wikiDF[col] > -1) & ~(sample_wikiDF[col].isna())].groupby('dim')[col].agg('mean'))\n",
-    "summs = pd.concat(summs, axis=1)\n",
-    "summs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "elegant-statement",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dim\n",
-       "distinctness    1.772331\n",
-       "synonymy        1.862791\n",
-       "ordering        1.923858\n",
-       "meronymy        2.171920\n",
-       "antonymy        2.177587\n",
-       "causation       2.353539\n",
-       "inheritance     2.357747\n",
-       "utility         2.520046\n",
-       "context         2.718537\n",
-       "properties      2.801135\n",
-       "material        2.937720\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summs.apply(lambda p: np.mean(list(filter(lambda y12: y12 != -1, [p[x] if not(pd.isna(p[x])) else -1 for x in scoreCols]))), axis=1).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "equal-headset",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
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-       "      <th>dim</th>\n",
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-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  2             1              4   \n",
-       "causation                 5             1              4   \n",
-       "context                   3             5              1   \n",
-       "distinctness              2             3              1   \n",
-       "inheritance               4             5              2   \n",
-       "material                  1             2              4   \n",
-       "meronymy                  4             5              3   \n",
-       "ordering                  3             2              1   \n",
-       "properties                1             4              5   \n",
-       "synonymy                  1             2              4   \n",
-       "utility                   1             5              2   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         3                               5  \n",
-       "causation                        2                               3  \n",
-       "context                          2                               4  \n",
-       "distinctness                     5                               4  \n",
-       "inheritance                      1                               3  \n",
-       "material                         3                               5  \n",
-       "meronymy                         1                               2  \n",
-       "ordering                         4                               5  \n",
-       "properties                       2                               3  \n",
-       "synonymy                         5                               3  \n",
-       "utility                          3                               4  "
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosRowWise(row):\n",
-    "    return (np.array([row[col] for col in scoreCols])).argsort().argsort() + 1\n",
-    "pd.DataFrame(summs.apply(rankAlgosRowWise, axis=1).to_list(), columns=scoreCols, index=summs.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "funky-acting",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>antonymy</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>causation</th>\n",
-       "      <td>11</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>context</th>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>distinctness</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inheritance</th>\n",
-       "      <td>9</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>material</th>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "      <td>10</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meronymy</th>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ordering</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>properties</th>\n",
-       "      <td>5</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>synonymy</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>utility</th>\n",
-       "      <td>6</td>\n",
-       "      <td>10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              complex_score  transe_score  textEmb_score  \\\n",
-       "dim                                                        \n",
-       "antonymy                  4             3              8   \n",
-       "causation                11             5              9   \n",
-       "context                  10             8              7   \n",
-       "distinctness              3             4              2   \n",
-       "inheritance               9             6              5   \n",
-       "material                  8             9             10   \n",
-       "meronymy                  7             7              4   \n",
-       "ordering                  2             1              1   \n",
-       "properties                5            11             11   \n",
-       "synonymy                  1             2              3   \n",
-       "utility                   6            10              6   \n",
-       "\n",
-       "              BERT_abs_label_score  BERT_abs_firstSent_label_score  \n",
-       "dim                                                                 \n",
-       "antonymy                         8                               6  \n",
-       "causation                        2                               4  \n",
-       "context                         10                               8  \n",
-       "distinctness                     4                               2  \n",
-       "inheritance                      3                               5  \n",
-       "material                        11                              11  \n",
-       "meronymy                         1                               1  \n",
-       "ordering                         9                               9  \n",
-       "properties                       6                              10  \n",
-       "synonymy                         5                               3  \n",
-       "utility                          7                               7  "
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def rankAlgosColWise(colVals):\n",
-    "    return pd.Series(((np.array([colVals.to_list()])).argsort().argsort() + 1)[0], index=summs.index, name=colVals.name)\n",
-    "pd.concat([rankAlgosColWise(summs[col]) for col in scoreCols], axis=1).head(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "filled-bridge",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "armed-incidence",
-   "metadata": {},
-   "source": [
-    "# Retrofitting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "id": "satisfied-china",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import euclidean_distances\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import scipy.stats as stats\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm.notebook import tqdm\n",
-    "from itertools import combinations\n",
-    "from math import comb\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "\n",
-    "def normalizeTargetScores(val1, minVal, maxVal):\n",
-    "    return (val1 - minVal) / (maxVal - minVal) if val1 != -1 else -1\n",
-    "\n",
-    "def normalize(embedDict):\n",
-    "    for key, val in embedDict.items():\n",
-    "        temp = np.array([float(val1) for val1 in val])\n",
-    "        temp2 = temp**2\n",
-    "        embedDict[key] = temp / np.sqrt((temp2.sum() + 1e-6))\n",
-    "    return embedDict\n",
-    "\n",
-    "def fetchEmbeddings(df):\n",
-    "    embedDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        embedDict[row.node] = row.value\n",
-    "    return normalize(embedDict)\n",
-    "\n",
-    "def standardizeDataset(df, targetCol, val1):\n",
-    "#     df1 = df[df[targetCol] > -1]\n",
-    "    df1 = df.copy()\n",
-    "    df1['bert2SentSim'] = val1\n",
-    "#     df1['bert2SentSim'] = df1[targetCol].apply(normalizeTargetScores, args=(df1[targetCol].where(df1[targetCol] != -1).min(), df1[targetCol].max()))\n",
-    "#     print(df1[targetCol].describe())\n",
-    "#     print(df1[targetCol+'_norm'].describe())\n",
-    "    return df1\n",
-    "\n",
-    "def fetchNeighbours(df):\n",
-    "    \n",
-    "    neighboursDict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        if row.node1 not in neighboursDict:\n",
-    "            neighboursDict[row.node1] = []\n",
-    "        neighboursDict[row.node1].append((row.node2, row['bert2SentSim']))\n",
-    "        \n",
-    "        if row.node2 not in neighboursDict:\n",
-    "            neighboursDict[row.node2] = []\n",
-    "        neighboursDict[row.node2].append((row.node1, row['bert2SentSim']))\n",
-    "    print(max([len(neigh) for neigh in neighboursDict.values()]))\n",
-    "    return neighboursDict\n",
-    "\n",
-    "def retrofit(embedDict, neighDict, weightCase):\n",
-    "    newEmbedDict = {}\n",
-    "    for word in embedDict.keys():\n",
-    "        if word in neighDict:\n",
-    "            neighbs = neighDict[word]\n",
-    "            neighbs = list(filter(lambda p: p[0] in embedDict, neighbs))\n",
-    "            if len(neighbs) == 0:\n",
-    "                newEmbedDict[word] = embedDict[word]\n",
-    "                continue\n",
-    "#             assert len(neighbs) == 1\n",
-    "            sumOfSims = sum([neighb[1] for neighb in neighbs])\n",
-    "            sumOfEmbs = sum([embedDict[neighb[0]] * float(neighb[1]) for neighb in neighbs])\n",
-    "            if weightCase == 1:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs)) + sumOfEmbs) / ((len(neighbs)) + sumOfSims)\n",
-    "            elif weightCase == 2:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**2 + sumOfEmbs) / ((len(neighbs))**2 + sumOfSims)\n",
-    "            elif weightCase == 0.5:\n",
-    "                newEmbedDict[word] = (embedDict[word] * (len(neighbs))**0.5 + sumOfEmbs) / ((len(neighbs))**0.5 + sumOfSims)\n",
-    "            else:\n",
-    "                raise\n",
-    "        else:\n",
-    "            newEmbedDict[word] = embedDict[word]\n",
-    "    return newEmbedDict\n",
-    "\n",
-    "def determineDistances(embedDict, newEmbedDict):\n",
-    "    dist = []\n",
-    "    for word in embedDict.keys():\n",
-    "        dist.append(euclidean_distances([embedDict[word]], [newEmbedDict[word]])[0][0])\n",
-    "    return dist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "id": "enhanced-journalist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'\n",
-    "\n",
-    "LABELS = ['I','U','M']\n",
-    "\n",
-    "def fetchCorrelationResults(embedDict, newEmbedDict):\n",
-    "    wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')\n",
-    "    print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "    assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "    assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "    wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "    wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "    wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "#     wordSimMissingSet\n",
-    "    print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-    "    wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n",
-    "    print(f\"KT Corr of old emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of new emb with Annotated Avg: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['Avg'])}\")\n",
-    "    print(f\"KT Corr of old emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textOld'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    print(f\"KT Corr of new emb with Human Avg Reversed: {stats.kendalltau(wordSim353AnnotDF_New2['textNew'], wordSim353AnnotDF_New2['H_reversed'])}\")\n",
-    "    \n",
-    "    print(f\"Classification Accuracy of old embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    print(f\"Classification Accuracy of new embeddings categories vs annotated averages categories: {accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])}\")\n",
-    "    \n",
-    "    cm_old = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textOld'].apply(labelSamples), labels=LABELS)\n",
-    "    cm_new = confusion_matrix(wordSim353AnnotDF_New2['category'], wordSim353AnnotDF_New2['textNew'].apply(labelSamples), labels=LABELS)\n",
-    "    \n",
-    "    return wordSimMissingSet, cm_old, cm_new, accuracy_score(wordSim353AnnotDF_New2['textOld'].apply(labelSamples), wordSim353AnnotDF_New2['category']), accuracy_score(wordSim353AnnotDF_New2['textNew'].apply(labelSamples), wordSim353AnnotDF_New2['category'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "id": "dried-subcommittee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>transe_embedding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q18037012</td>\n",
-       "      <td>[0.000987129, -0.157959029, 0.601584613, -0.41...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q13112971</td>\n",
-       "      <td>[0.323111564, -0.157194719, 0.350547582, -0.13...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q59336660</td>\n",
-       "      <td>[0.067076892, -0.706452787, -0.070606612, 0.06...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q289472</td>\n",
-       "      <td>[-0.444723994, -0.020702582, 0.109241754, -0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q1931010</td>\n",
-       "      <td>[-0.055137988, -0.652317405, 0.043370813, -0.3...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        node                                   transe_embedding\n",
-       "0  Q18037012  [0.000987129, -0.157959029, 0.601584613, -0.41...\n",
-       "1  Q13112971  [0.323111564, -0.157194719, 0.350547582, -0.13...\n",
-       "2  Q59336660  [0.067076892, -0.706452787, -0.070606612, 0.06...\n",
-       "3    Q289472  [-0.444723994, -0.020702582, 0.109241754, -0.4...\n",
-       "4   Q1931010  [-0.055137988, -0.652317405, 0.043370813, -0.3..."
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transeEmbeddingsDF = pd.read_csv('../data/wordsim353_transe_embeddings.csv')\n",
-    "transeEmbeddingsDF['transe_embedding'] = transeEmbeddingsDF['transe_embedding'].apply(lambda p: [float(p1) for p1 in p.split(',')])\n",
-    "transeEmbeddingsDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "id": "italic-stations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transeEmbeddings = {}\n",
-    "for _, row in transeEmbeddingsDF.iterrows():\n",
-    "    transeEmbeddings[row['node']] = np.array(row['transe_embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "id": "imperial-baptist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node2</th>\n",
-       "      <th>node1</th>\n",
-       "      <th>relation</th>\n",
-       "      <th>node1;label</th>\n",
-       "      <th>node2;label</th>\n",
-       "      <th>relation;label</th>\n",
-       "      <th>relation;dimension</th>\n",
-       "      <th>source</th>\n",
-       "      <th>sentence</th>\n",
-       "      <th>node1_compl_emb</th>\n",
-       "      <th>...</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb</th>\n",
-       "      <th>BERT_abs_firstSent_label_emb_right</th>\n",
-       "      <th>BERT_abs_firstSent_label_cosSim</th>\n",
-       "      <th>BERT_abstract_score</th>\n",
-       "      <th>BERT_abstract_firstSent_score</th>\n",
-       "      <th>complex_score</th>\n",
-       "      <th>transe_score</th>\n",
-       "      <th>textEmb_score</th>\n",
-       "      <th>BERT_abs_label_score</th>\n",
-       "      <th>BERT_abs_firstSent_label_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q2142152</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>temple</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.056723248, -0.984291673, -0.140366003, -0....</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6187984943389893, 1.4799031019210815, 0.515...</td>\n",
-       "      <td>[-0.21400201320648193, -0.1853264421224594, 2....</td>\n",
-       "      <td>0.111954</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.533456</td>\n",
-       "      <td>3.459105</td>\n",
-       "      <td>3.219849</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q3407658</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>tram</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.208795503, -0.935872674, 0.694316924, 0.018...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.1387496143579483, -1.0609047412872314, -0....</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.066402</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.623771</td>\n",
-       "      <td>3.430562</td>\n",
-       "      <td>3.333714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P1114</td>\n",
-       "      <td>Q41792217</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>number of objects</td>\n",
-       "      <td>quantity</td>\n",
-       "      <td>Wikidata property</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[-0.3635993, -0.764536679, 0.060853526, -0.071...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.6716414093971252, -0.08505986630916595, 1.6...</td>\n",
-       "      <td>[-0.21400196850299835, -0.1853266954421997, 2....</td>\n",
-       "      <td>0.701743</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.483466</td>\n",
-       "      <td>1.746096</td>\n",
-       "      <td>1.745552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P1268</td>\n",
-       "      <td>P6840</td>\n",
-       "      <td>/r/RelatedTo</td>\n",
-       "      <td>student organization of</td>\n",
-       "      <td>represents</td>\n",
-       "      <td>see also</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[0.11234442889690399, -0.34427452087402344, 1....</td>\n",
-       "      <td>[0.1607293337583542, -0.1969822645187378, 2.40...</td>\n",
-       "      <td>0.546000</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.084781</td>\n",
-       "      <td>2.135693</td>\n",
-       "      <td>2.134864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P1269</td>\n",
-       "      <td>Q4504495</td>\n",
-       "      <td>/r/HasProperty</td>\n",
-       "      <td>award ceremony</td>\n",
-       "      <td>facet of</td>\n",
-       "      <td>properties for this type</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[0.0430861, -0.041153915, -0.603652894, -0.464...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>[-0.02503090538084507, 0.4087623953819275, 1.9...</td>\n",
-       "      <td>[-0.03644487261772156, -0.5494412183761597, 2....</td>\n",
-       "      <td>0.372485</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.409778</td>\n",
-       "      <td>3.333835</td>\n",
-       "      <td>2.568600</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node2      node1        relation              node1;label node2;label  \\\n",
-       "0  P1114   Q2142152  /r/HasProperty                   temple    quantity   \n",
-       "1  P1114   Q3407658  /r/HasProperty                     tram    quantity   \n",
-       "2  P1114  Q41792217  /r/HasProperty        number of objects    quantity   \n",
-       "3  P1268      P6840    /r/RelatedTo  student organization of  represents   \n",
-       "4  P1269   Q4504495  /r/HasProperty           award ceremony    facet of   \n",
-       "\n",
-       "             relation;label  relation;dimension source  sentence  \\\n",
-       "0  properties for this type                 NaN     WD       NaN   \n",
-       "1  properties for this type                 NaN     WD       NaN   \n",
-       "2         Wikidata property                 NaN     WD       NaN   \n",
-       "3                  see also                 NaN     WD       NaN   \n",
-       "4  properties for this type                 NaN     WD       NaN   \n",
-       "\n",
-       "                                     node1_compl_emb  ...  \\\n",
-       "0  [-0.056723248, -0.984291673, -0.140366003, -0....  ...   \n",
-       "1  [0.208795503, -0.935872674, 0.694316924, 0.018...  ...   \n",
-       "2  [-0.3635993, -0.764536679, 0.060853526, -0.071...  ...   \n",
-       "3                                               None  ...   \n",
-       "4  [0.0430861, -0.041153915, -0.603652894, -0.464...  ...   \n",
-       "\n",
-       "                        BERT_abs_firstSent_label_emb  \\\n",
-       "0  [0.6187984943389893, 1.4799031019210815, 0.515...   \n",
-       "1  [-0.1387496143579483, -1.0609047412872314, -0....   \n",
-       "2  [0.6716414093971252, -0.08505986630916595, 1.6...   \n",
-       "3  [0.11234442889690399, -0.34427452087402344, 1....   \n",
-       "4  [-0.02503090538084507, 0.4087623953819275, 1.9...   \n",
-       "\n",
-       "                  BERT_abs_firstSent_label_emb_right  \\\n",
-       "0  [-0.21400201320648193, -0.1853264421224594, 2....   \n",
-       "1  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "2  [-0.21400196850299835, -0.1853266954421997, 2....   \n",
-       "3  [0.1607293337583542, -0.1969822645187378, 2.40...   \n",
-       "4  [-0.03644487261772156, -0.5494412183761597, 2....   \n",
-       "\n",
-       "  BERT_abs_firstSent_label_cosSim BERT_abstract_score  \\\n",
-       "0                        0.111954                -1.0   \n",
-       "1                        0.066402                -1.0   \n",
-       "2                        0.701743                -1.0   \n",
-       "3                        0.546000                -1.0   \n",
-       "4                        0.372485                -1.0   \n",
-       "\n",
-       "  BERT_abstract_firstSent_score complex_score transe_score textEmb_score  \\\n",
-       "0                          -1.0          -1.0         -1.0      2.533456   \n",
-       "1                          -1.0          -1.0         -1.0      2.623771   \n",
-       "2                          -1.0          -1.0         -1.0      2.483466   \n",
-       "3                          -1.0          -1.0         -1.0      2.084781   \n",
-       "4                          -1.0          -1.0         -1.0      2.409778   \n",
-       "\n",
-       "  BERT_abs_label_score BERT_abs_firstSent_label_score  \n",
-       "0             3.459105                       3.219849  \n",
-       "1             3.430562                       3.333714  \n",
-       "2             1.746096                       1.745552  \n",
-       "3             2.135693                       2.134864  \n",
-       "4             3.333835                       2.568600  \n",
-       "\n",
-       "[5 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wikiDF_wabs1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "id": "mexican-dispatch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    2.000000\n",
-      "mean    -0.031714\n",
-      "std      0.002003\n",
-      "min     -0.033131\n",
-      "25%     -0.032423\n",
-      "50%     -0.031714\n",
-      "75%     -0.031006\n",
-      "max     -0.030298\n",
-      "Name: complex_cosSim, dtype: float64\n",
-      "count    12.000000\n",
-      "mean     -0.063752\n",
-      "std       0.041979\n",
-      "min      -0.137044\n",
-      "25%      -0.093370\n",
-      "50%      -0.051462\n",
-      "75%      -0.039529\n",
-      "max      -0.009669\n",
-      "Name: transe_cosSim, dtype: float64\n",
-      "count    0.0\n",
-      "mean     NaN\n",
-      "std      NaN\n",
-      "min      NaN\n",
-      "25%      NaN\n",
-      "50%      NaN\n",
-      "75%      NaN\n",
-      "max      NaN\n",
-      "Name: textEmb_cosSim, dtype: float64\n",
-      "count    313.000000\n",
-      "mean      -0.041530\n",
-      "std        0.036422\n",
-      "min       -0.181201\n",
-      "25%       -0.059406\n",
-      "50%       -0.031702\n",
-      "75%       -0.013665\n",
-      "max       -0.000024\n",
-      "Name: BERT_abs_label_cosSim, dtype: float64\n",
-      "count    79.000000\n",
-      "mean     -0.035752\n",
-      "std       0.029876\n",
-      "min      -0.122427\n",
-      "25%      -0.054458\n",
-      "50%      -0.026226\n",
-      "75%      -0.009441\n",
-      "max      -0.000182\n",
-      "Name: BERT_abs_firstSent_label_cosSim, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "cosSimCols = ['complex_cosSim', 'transe_cosSim', 'textEmb_cosSim', 'BERT_abs_label_cosSim', 'BERT_abs_firstSent_label_cosSim']\n",
-    "repelDims = {'other', 'meronymy', 'context', 'material', 'properties', 'utility'}\n",
-    "for col1 in cosSimCols:\n",
-    "#     print((wikiDF_wabs1[col1] == -1).sum())\n",
-    "    print(wikiDF_wabs1[(wikiDF_wabs1[col1] > -1) & (wikiDF_wabs1[col1] < 0) & (wikiDF_wabs1.dim.apply(lambda p: p not in repelDims))][col1].describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "id": "talented-indie",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p279WordSimSeededDF_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_ChildPar_19k_WEmbAndCosSim.csv\")\n",
-    "p279Seeded_SiblingsDF3_wabs_text = pd.read_csv(\"../data/P279_dataset/P279_Siblings_19k_WEmbAndCosSim.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "id": "expensive-warner",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n",
-      "374\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     7.504525e-03\n",
-      "std      4.784244e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      4.424263e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24716970867633406, pvalue=7.762158676245256e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22502397615435152, pvalue=5.730424928972872e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6034985422740525\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    1.910100e+04\n",
-      "mean     6.937777e-03\n",
-      "std      4.561719e-02\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      4.424263e-01\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24843225530937377, pvalue=6.217152652954295e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22380720194731576, pvalue=7.083559265864719e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "374\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    1.910100e+04\n",
-      "mean     2.274155e-04\n",
-      "std      1.449770e-03\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      1.340686e-02\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2507469034952738, pvalue=4.1314425443641745e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22557152137805614, pvalue=5.206423573456538e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    1.910100e+04\n",
-      "mean     2.120965e-04\n",
-      "std      1.386917e-03\n",
-      "min      0.000000e+00\n",
-      "25%      0.000000e+00\n",
-      "50%      0.000000e+00\n",
-      "75%      1.490116e-08\n",
-      "max      1.340686e-02\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.2510476149348689, pvalue=3.912200370865155e-11)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2257539655761838, pvalue=5.037446475691195e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.332525\n",
-      "std          0.085513\n",
-      "min          0.000000\n",
-      "25%          0.269043\n",
-      "50%          0.327801\n",
-      "75%          0.393660\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23983260775448598, pvalue=2.752926819013835e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21456745507793118, pvalue=3.4267954366117823e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6559766763848397\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.242979\n",
-      "std          0.150562\n",
-      "min          0.000000\n",
-      "25%          0.094960\n",
-      "50%          0.237342\n",
-      "75%          0.378069\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.244711271374783, pvalue=1.194655795369227e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22603182014110224, pvalue=4.814978154680426e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.60932944606414\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.329680\n",
-      "std          0.085445\n",
-      "min          0.000000\n",
-      "25%          0.266447\n",
-      "50%          0.323794\n",
-      "75%          0.390543\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24010565224095312, pvalue=2.6288081698945844e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21420995798618558, pvalue=3.6387685007723226e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6530612244897959\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.238297\n",
-      "std          0.149913\n",
-      "min          0.000000\n",
-      "25%          0.093200\n",
-      "50%          0.228559\n",
-      "75%          0.374263\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.244711271374783, pvalue=1.194655795369227e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.22445752023550897, pvalue=6.343021648612184e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n",
-      "\n",
-      "\n",
-      "\n",
-      "Results for col: complex_cosSim\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2662\n",
-      "No. of keys in embedDict: 19101\n",
-      "\n",
-      "\n",
-      "Weight Case: 1\n",
-      "count    19101.000000\n",
-      "mean         0.329605\n",
-      "std          0.085511\n",
-      "min          0.000000\n",
-      "25%          0.266179\n",
-      "50%          0.323686\n",
-      "75%          0.390543\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.23974431943095922, pvalue=2.7955198144322416e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.21383352064488867, pvalue=3.87455333255265e-09)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6530612244897959\n",
-      "\n",
-      "\n",
-      "Weight Case: 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    19101.000000\n",
-      "mean         0.238361\n",
-      "std          0.149862\n",
-      "min          0.000000\n",
-      "25%          0.093316\n",
-      "50%          0.228559\n",
-      "75%          0.374263\n",
-      "max          0.679789\n",
-      "dtype: float64\n",
-      "\n",
-      "Length of wordsim dataset: 344\n",
-      "No. of pairs with some value for embeddings: 343\n",
-      "KT Corr of old emb with Annotated Avg: KendalltauResult(correlation=0.2510699623325116, pvalue=3.899611216307421e-11)\n",
-      "KT Corr of new emb with Annotated Avg: KendalltauResult(correlation=0.24453059264517965, pvalue=1.2326605897936912e-10)\n",
-      "KT Corr of old emb with Human Avg Reversed: KendalltauResult(correlation=0.2258262577446998, pvalue=4.97723935846524e-10)\n",
-      "KT Corr of new emb with Human Avg Reversed: KendalltauResult(correlation=0.2245944158794736, pvalue=6.193255676859552e-10)\n",
-      "Classification Accuracy of old embeddings categories vs annotated averages categories: 0.6005830903790087\n",
-      "Classification Accuracy of new embeddings categories vs annotated averages categories: 0.6064139941690962\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-312-25816f8dd83d>:16: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:17: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0], axis=1)\n",
-      "<ipython-input-312-25816f8dd83d>:18: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textOld'] = wordSim353AnnotDF_New2.textOld.apply(lambda p: 4 - 3 * p)\n",
-      "<ipython-input-312-25816f8dd83d>:19: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  wordSim353AnnotDF_New2['textNew'] = wordSim353AnnotDF_New2.textNew.apply(lambda p: 4 - 3 * p)\n"
-     ]
-    }
-   ],
-   "source": [
-    "results1 = []\n",
-    "confusionMatrixMaster = {}\n",
-    "embedDictMaster = {}\n",
-    "newEmbedDictMaster = {}\n",
-    "\n",
-    "\n",
-    "embedDict = normalize(transeEmbeddings)\n",
-    "embedDictMaster['transe_19k'] = embedDict\n",
-    "\n",
-    "# Version 1: 0.25 for 6 dims of Wiki CS\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.25)\n",
-    "    neighDict = fetchNeighbours(wikiCSDF)\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['wikiCS_0.25_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, 'wikiCS_0.25',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 2: 0.01 for 6 dims of Wiki CS\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.01)\n",
-    "    neighDict = fetchNeighbours(wikiCSDF)\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['wikiCS_0.01_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, 'wikiCS_0.01',oldAcc, newAcc))\n",
-    "        \n",
-    "# Version 3: 19k Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "#     wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, 0.25)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 4: 19k+0.25 Wiki CS Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.25)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, wikiCSDF]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wikiCS_0.25_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k+wikiCS_0.25',oldAcc, newAcc))\n",
-    "\n",
-    "# Version 5: 19k+0.01 Wiki CS Dataset\n",
-    "for col1 in ['complex_cosSim']:\n",
-    "    print(\"\\n\\n\\nResults for col: \"+str(col1)+\"\\n\\n\")\n",
-    "    wikiCSDF = standardizeDataset(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)], col1, -0.01)\n",
-    "    neighDict = fetchNeighbours(pd.concat([p279WordSimSeededDF_wabs_text, p279Seeded_SiblingsDF3_wabs_text, wikiCSDF]))\n",
-    "    print(f\"No. of keys in embedDict: {len(embedDict.keys())}\")\n",
-    "    for weightCase in [1,2]:\n",
-    "        print(f\"\\n\\nWeight Case: {weightCase}\")\n",
-    "        newEmbedDict = retrofit(embedDict, neighDict, weightCase)\n",
-    "        newEmbedDictMaster['19k_wikiCS_0.01_wt_'+str(weightCase)] = newEmbedDict\n",
-    "        dists = determineDistances(embedDict, newEmbedDict)\n",
-    "        print(pd.Series(dists).describe())\n",
-    "        print()\n",
-    "        _,confusionMatrixMaster['transe_19k_old_'+str(weightCase)], confusionMatrixMaster['transe_19k_new_'+str(weightCase)], oldAcc, newAcc = fetchCorrelationResults(embedDict, newEmbedDict)\n",
-    "        results1.append((col1, weightCase, '19k+wikiCS_0.01',oldAcc, newAcc))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "id": "specialized-syria",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'transe_19k_old_1': array([[  9,   1,  10],\n",
-       "        [  0,  20,  83],\n",
-       "        [  2,  41, 177]]),\n",
-       " 'transe_19k_new_1': array([[ 13,   0,   7],\n",
-       "        [  0,  10,  93],\n",
-       "        [  7,  12, 201]]),\n",
-       " 'transe_19k_old_2': array([[  9,   1,  10],\n",
-       "        [  0,  20,  83],\n",
-       "        [  2,  41, 177]]),\n",
-       " 'transe_19k_new_2': array([[ 10,   1,   9],\n",
-       "        [  0,  20,  83],\n",
-       "        [  3,  39, 178]])}"
-      ]
-     },
-     "execution_count": 337,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusionMatrixMaster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "id": "damaged-education",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17187"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "id": "conservative-compact",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "280"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(wikiDF_wabs1[wikiDF_wabs1['dim'].apply(lambda p: p in repelDims)].apply(lambda p: p.node1 in embedDict and p.node2 in embedDict, axis=1)).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "theoretical-delay",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "id": "swedish-cornwall",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CS_column</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>Target</th>\n",
-       "      <th>Old Accuracy</th>\n",
-       "      <th>New Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.603499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.600583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.655977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.609329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k+wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.653061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k+wikiCS_0.25</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.606414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19k+wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.653061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>complex_cosSim</td>\n",
-       "      <td>2</td>\n",
-       "      <td>19k+wikiCS_0.01</td>\n",
-       "      <td>0.600583</td>\n",
-       "      <td>0.606414</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        CS_column  weight           Target  Old Accuracy  New Accuracy\n",
-       "0  complex_cosSim       1      wikiCS_0.25      0.600583      0.603499\n",
-       "1  complex_cosSim       2      wikiCS_0.25      0.600583      0.600583\n",
-       "2  complex_cosSim       1      wikiCS_0.01      0.600583      0.600583\n",
-       "3  complex_cosSim       2      wikiCS_0.01      0.600583      0.600583\n",
-       "4  complex_cosSim       1              19k      0.600583      0.655977\n",
-       "5  complex_cosSim       2              19k      0.600583      0.609329\n",
-       "6  complex_cosSim       1  19k+wikiCS_0.25      0.600583      0.653061\n",
-       "7  complex_cosSim       2  19k+wikiCS_0.25      0.600583      0.606414\n",
-       "8  complex_cosSim       1  19k+wikiCS_0.01      0.600583      0.653061\n",
-       "9  complex_cosSim       2  19k+wikiCS_0.01      0.600583      0.606414"
-      ]
-     },
-     "execution_count": 335,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(results1, columns=['CS_column', 'weight', 'Target', 'Old Accuracy', 'New Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "id": "palestinian-strategy",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of wordsim dataset: 344\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb4967f59df544e2b0cb6806d06dd1bf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/10 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353_all_embeddings_with_retrofits.csv')\n",
-    "wordSim353AnnotDF_New = wordSim353AnnotDF_New[['word1_kg_id', 'word2_kg_id', 'Word 1', 'Word 2', 'Avg', 'category']]\n",
-    "print(f\"Length of wordsim dataset: {len(wordSim353AnnotDF_New)}\")\n",
-    "assert wordSim353AnnotDF_New.word1_kg_id.isna().sum() == 0\n",
-    "assert wordSim353AnnotDF_New.word2_kg_id.isna().sum() == 0\n",
-    "wordSim353AnnotDF_New['category'] = wordSim353AnnotDF_New.Avg.apply(labelSamples)\n",
-    "\n",
-    "key1 = 'transe_19k'\n",
-    "\n",
-    "for key2 in tqdm(newEmbedDictMaster.keys()):\n",
-    "#     wordSim353AnnotDF_New2 = wordSim353AnnotDF_New[wordSim353AnnotDF_New.apply(lambda p: p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict, axis=1)]\n",
-    "#     wordSimMissingSet = set(wordSim353AnnotDF_New[wordSim353AnnotDF_New.word1_kg_id.apply(lambda p: p not in embedDict)].word1_kg_id.to_list() + wordSim353AnnotDF_New[wordSim353AnnotDF_New.word2_kg_id.apply(lambda p: p not in embedDict)].word2_kg_id.to_list())\n",
-    "    #     wordSimMissingSet\n",
-    "#     print(f\"No. of pairs with some value for embeddings: {len(wordSim353AnnotDF_New2)}\")\n",
-    "\n",
-    "    embedDict = embedDictMaster[key1]\n",
-    "    newEmbedDict = newEmbedDictMaster[key2]\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in embedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_old'] = wordSim353AnnotDF_New.apply(lambda p: embedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in embedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_word1_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word1_kg_id']].tolist() if p['word1_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_word2_new'] = wordSim353AnnotDF_New.apply(lambda p: newEmbedDict[p['word2_kg_id']].tolist() if p['word2_kg_id'] in newEmbedDict else None, axis=1)\n",
-    "    \n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(embedDict[p['word1_kg_id']]).reshape(1,-1), np.array(embedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in embedDict and p['word2_kg_id'] in embedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New.apply(lambda p: cosine_similarity(np.array(newEmbedDict[p['word1_kg_id']]).reshape(1,-1), np.array(newEmbedDict[p['word2_kg_id']]).reshape(1,-1))[0][0] if p['word1_kg_id'] in newEmbedDict and p['word2_kg_id'] in newEmbedDict else -1, axis=1)\n",
-    "    wordSim353AnnotDF_New[key1+'_old_cosSim'] = wordSim353AnnotDF_New[key1+'_old_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "    wordSim353AnnotDF_New[key2+'_new_cosSim'] = wordSim353AnnotDF_New[key2+'_new_cosSim'].apply(lambda p: 4 - 3 * p)\n",
-    "wordSim353AnnotDF_New_Merged_DF = wordSim353AnnotDF_New"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "id": "dressed-confidentiality",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>word1_kg_id</th>\n",
-       "      <th>word2_kg_id</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>category</th>\n",
-       "      <th>transe_19k_word1_old</th>\n",
-       "      <th>transe_19k_word2_old</th>\n",
-       "      <th>transe_19k_word1_new</th>\n",
-       "      <th>transe_19k_word2_new</th>\n",
-       "      <th>...</th>\n",
-       "      <th>wikiCS_0.25_wt_1_new_cosSim</th>\n",
-       "      <th>wikiCS_0.25_wt_2_new_cosSim</th>\n",
-       "      <th>wikiCS_0.01_wt_1_new_cosSim</th>\n",
-       "      <th>wikiCS_0.01_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.25_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.25_wt_2_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.01_wt_1_new_cosSim</th>\n",
-       "      <th>19k_wikiCS_0.01_wt_2_new_cosSim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q454</td>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.056786171992006404, 0.01787462254737516, 0...</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.050833232942279184, 0.012123900440314235, ...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "      <td>3.477665</td>\n",
-       "      <td>3.676618</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Q34211</td>\n",
-       "      <td>Q13648784</td>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.03512899124845519, -0.06824488430804913, 0...</td>\n",
-       "      <td>[-0.020990896052782256, -0.0948794050152691, -...</td>\n",
-       "      <td>[-0.01589834711067725, -0.07306407013094582, 0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "      <td>2.952181</td>\n",
-       "      <td>2.947066</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q178022</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>U</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.017732972826050936, -0.023766644378558594, ...</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[-0.01674760225052191, -0.04519811208586652, 0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "      <td>3.126209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Q8333</td>\n",
-       "      <td>Q21004260</td>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.052228162576186316, -0.07922249700554501, -...</td>\n",
-       "      <td>[0.02753406452429862, -0.14605954884804623, -0...</td>\n",
-       "      <td>[0.051399986151325704, -0.07859687607569352, -...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.079927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Q13371</td>\n",
-       "      <td>Q49112</td>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>M</td>\n",
-       "      <td>[-0.015944469265519467, 0.22193716590314821, 0...</td>\n",
-       "      <td>[-0.01835552362856266, 0.11140088837689806, -0...</td>\n",
-       "      <td>[-0.015944469265519467, 0.22193716590314821, 0...</td>\n",
-       "      <td>[-0.01835552362856266, 0.11140088837689806, -0...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>2.080022</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  word1_kg_id word2_kg_id   Word 1         Word 2  Avg category  \\\n",
-       "0      Q34211        Q454   Arafat          peace  3.6        U   \n",
-       "1      Q34211   Q13648784   Arafat         terror  3.6        U   \n",
-       "2       Q8333     Q178022      FBI    fingerprint  3.6        U   \n",
-       "3       Q8333   Q21004260      FBI  investigation  3.0        M   \n",
-       "4      Q13371      Q49112  Harvard           Yale  2.2        M   \n",
-       "\n",
-       "                                transe_19k_word1_old  \\\n",
-       "0  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "1  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "2  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "3  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "4  [-0.015944469265519467, 0.22193716590314821, 0...   \n",
-       "\n",
-       "                                transe_19k_word2_old  \\\n",
-       "0  [-0.056786171992006404, 0.01787462254737516, 0...   \n",
-       "1  [-0.03512899124845519, -0.06824488430804913, 0...   \n",
-       "2  [0.017732972826050936, -0.023766644378558594, ...   \n",
-       "3  [0.052228162576186316, -0.07922249700554501, -...   \n",
-       "4  [-0.01835552362856266, 0.11140088837689806, -0...   \n",
-       "\n",
-       "                                transe_19k_word1_new  \\\n",
-       "0  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "1  [-0.020990896052782256, -0.0948794050152691, -...   \n",
-       "2  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "3  [0.02753406452429862, -0.14605954884804623, -0...   \n",
-       "4  [-0.015944469265519467, 0.22193716590314821, 0...   \n",
-       "\n",
-       "                                transe_19k_word2_new  ...  \\\n",
-       "0  [-0.050833232942279184, 0.012123900440314235, ...  ...   \n",
-       "1  [-0.01589834711067725, -0.07306407013094582, 0...  ...   \n",
-       "2  [-0.01674760225052191, -0.04519811208586652, 0...  ...   \n",
-       "3  [0.051399986151325704, -0.07859687607569352, -...  ...   \n",
-       "4  [-0.01835552362856266, 0.11140088837689806, -0...  ...   \n",
-       "\n",
-       "   wikiCS_0.25_wt_1_new_cosSim  wikiCS_0.25_wt_2_new_cosSim  \\\n",
-       "0                     3.747795                     3.747795   \n",
-       "1                     2.955021                     2.955021   \n",
-       "2                     3.149125                     3.149125   \n",
-       "3                     3.104421                     3.104421   \n",
-       "4                     2.080022                     2.080022   \n",
-       "\n",
-       "   wikiCS_0.01_wt_1_new_cosSim  wikiCS_0.01_wt_2_new_cosSim  \\\n",
-       "0                     3.747795                     3.747795   \n",
-       "1                     2.955021                     2.955021   \n",
-       "2                     3.149125                     3.149125   \n",
-       "3                     3.104421                     3.104421   \n",
-       "4                     2.080022                     2.080022   \n",
-       "\n",
-       "   19k_wt_1_new_cosSim  19k_wt_2_new_cosSim  19k_wikiCS_0.25_wt_1_new_cosSim  \\\n",
-       "0             3.477665             3.676618                         3.477665   \n",
-       "1             2.952181             2.947066                         2.952181   \n",
-       "2             3.126209             3.126209                         3.126209   \n",
-       "3             2.943750             3.079927                         2.943750   \n",
-       "4             2.080022             2.080022                         2.080022   \n",
-       "\n",
-       "   19k_wikiCS_0.25_wt_2_new_cosSim  19k_wikiCS_0.01_wt_1_new_cosSim  \\\n",
-       "0                         3.676618                         3.477665   \n",
-       "1                         2.947066                         2.952181   \n",
-       "2                         3.126209                         3.126209   \n",
-       "3                         3.079927                         2.943750   \n",
-       "4                         2.080022                         2.080022   \n",
-       "\n",
-       "   19k_wikiCS_0.01_wt_2_new_cosSim  \n",
-       "0                         3.676618  \n",
-       "1                         2.947066  \n",
-       "2                         3.126209  \n",
-       "3                         3.079927  \n",
-       "4                         2.080022  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New_Merged_DF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "id": "associate-cleaning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['transe_19k_old_cosSim',\n",
-       " 'wikiCS_0.25_wt_1_new_cosSim',\n",
-       " 'wikiCS_0.25_wt_2_new_cosSim',\n",
-       " 'wikiCS_0.01_wt_1_new_cosSim',\n",
-       " 'wikiCS_0.01_wt_2_new_cosSim',\n",
-       " '19k_wt_1_new_cosSim',\n",
-       " '19k_wt_2_new_cosSim',\n",
-       " '19k_wikiCS_0.25_wt_1_new_cosSim',\n",
-       " '19k_wikiCS_0.25_wt_2_new_cosSim',\n",
-       " '19k_wikiCS_0.01_wt_1_new_cosSim',\n",
-       " '19k_wikiCS_0.01_wt_2_new_cosSim']"
-      ]
-     },
-     "execution_count": 323,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda p: 'cosSim' in p, wordSim353AnnotDF_New_Merged_DF.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "id": "tight-location",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "id": "scheduled-massachusetts",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, colList):\n",
-    "    X = wordSim353AnnotDF_New_Merged_DF[colList]\n",
-    "    Y = wordSim353AnnotDF_New_Merged_DF['category']\n",
-    "\n",
-    "    N_SPLITS = 10\n",
-    "\n",
-    "    skf = StratifiedKFold(n_splits=N_SPLITS, random_state=19, shuffle=True)\n",
-    "    X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "    for train_index, test_index in skf.split(X, Y):\n",
-    "        X_train_splits.append(X.iloc[train_index])\n",
-    "        X_test_splits.append(X.iloc[test_index])\n",
-    "        Y_train_splits.append(Y.iloc[train_index])\n",
-    "        Y_test_splits.append(Y.iloc[test_index])\n",
-    "\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf.fit(X_train1, Y_train1)\n",
-    "        preds.append(clf.predict(X_test1))\n",
-    "\n",
-    "    tempVals = []\n",
-    "\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "        \n",
-    "\n",
-    "    return acc/N_SPLITS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "id": "starting-slovenia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.668655462184874"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, ['transe_19k_old_cosSim'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "id": "looking-color",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cosSimNewCols = ['wikiCS_0.25_wt_1_new_cosSim',\n",
-    " 'wikiCS_0.25_wt_2_new_cosSim',\n",
-    " 'wikiCS_0.01_wt_1_new_cosSim',\n",
-    " 'wikiCS_0.01_wt_2_new_cosSim',\n",
-    " '19k_wt_1_new_cosSim',\n",
-    " '19k_wt_2_new_cosSim',\n",
-    " '19k_wikiCS_0.25_wt_1_new_cosSim',\n",
-    " '19k_wikiCS_0.25_wt_2_new_cosSim',\n",
-    " '19k_wikiCS_0.01_wt_1_new_cosSim',\n",
-    " '19k_wikiCS_0.01_wt_2_new_cosSim']\n",
-    "\n",
-    "svmResults = []\n",
-    "for col1 in cosSimNewCols:\n",
-    "    svmResults.append((col1, trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, [col1])*100))\n",
-    "svmResults = pd.DataFrame(svmResults, columns=['target', 'Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "id": "nutritional-chicken",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svmResults['Increase'] = svmResults['Accuracy'] - trainAndFindAccuracy(wordSim353AnnotDF_New_Merged_DF, ['transe_19k_old_cosSim'])*100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "id": "charitable-journalism",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>target</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>Increase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>wikiCS_0.25_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>wikiCS_0.25_wt_2_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>wikiCS_0.01_wt_1_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>wikiCS_0.01_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>19k_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>19k_wt_2_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>19k_wikiCS_0.25_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>19k_wikiCS_0.25_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>19k_wikiCS_0.01_wt_1_new_cosSim</td>\n",
-       "      <td>66.571429</td>\n",
-       "      <td>-0.294118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>19k_wikiCS_0.01_wt_2_new_cosSim</td>\n",
-       "      <td>66.865546</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            target   Accuracy  Increase\n",
-       "0      wikiCS_0.25_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "1      wikiCS_0.25_wt_2_new_cosSim  66.571429 -0.294118\n",
-       "2      wikiCS_0.01_wt_1_new_cosSim  66.865546  0.000000\n",
-       "3      wikiCS_0.01_wt_2_new_cosSim  66.865546  0.000000\n",
-       "4              19k_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "5              19k_wt_2_new_cosSim  66.571429 -0.294118\n",
-       "6  19k_wikiCS_0.25_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "7  19k_wikiCS_0.25_wt_2_new_cosSim  66.865546  0.000000\n",
-       "8  19k_wikiCS_0.01_wt_1_new_cosSim  66.571429 -0.294118\n",
-       "9  19k_wikiCS_0.01_wt_2_new_cosSim  66.865546  0.000000"
-      ]
-     },
-     "execution_count": 332,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svmResults"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "naval-avenue",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Wordsim353 Annotated File Analyser.ipynb b/Wordsim353 Annotated File Analyser.ipynb
deleted file mode 100644
index fa3a69a..0000000
--- a/Wordsim353 Annotated File Analyser.ipynb	
+++ /dev/null
@@ -1,4468 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "spectacular-separate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "medical-explanation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['class', 'transe', 'text', 'complex', 'labEmb', 'textEmb', 'textSubsetEmb', 'absEmb', 'absFirstSentEmb']\n",
-    "indNames = ['class (Amandeep)', 'transe (Amandeep)', 'text (Amandeep)', 'complex (Amandeep)', 'Labels Embedding', 'Text Embedding - 7 props', 'Text Embedding - P279 + P31', 'DBPedia short abstracts', 'DBPedia short abstracts first sentence']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "timely-history",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_Old = pd.read_csv('../data/wordsim353annotated_old.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "scenic-advisory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSim353AnnotDF_New = pd.read_csv('../data/wordsim353LatestAnnot.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "assumed-lawyer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Ind</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>DS</th>\n",
-       "      <th>HC</th>\n",
-       "      <th>NK</th>\n",
-       "      <th>FI</th>\n",
-       "      <th>Average</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>admission</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>3.611154</td>\n",
-       "      <td>4.466324</td>\n",
-       "      <td>4.856529</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.809672</td>\n",
-       "      <td>4.949738</td>\n",
-       "      <td>5.145601</td>\n",
-       "      <td>4.268633</td>\n",
-       "      <td>3.770548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>3.021635</td>\n",
-       "      <td>2.743883</td>\n",
-       "      <td>4.793135</td>\n",
-       "      <td>0.058511</td>\n",
-       "      <td>4.554877</td>\n",
-       "      <td>4.818231</td>\n",
-       "      <td>3.987047</td>\n",
-       "      <td>3.399217</td>\n",
-       "      <td>4.168040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>6.944618</td>\n",
-       "      <td>4.292894</td>\n",
-       "      <td>6.267841</td>\n",
-       "      <td>3.936571</td>\n",
-       "      <td>7.643815</td>\n",
-       "      <td>6.407051</td>\n",
-       "      <td>5.669631</td>\n",
-       "      <td>6.865782</td>\n",
-       "      <td>4.997217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>news</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>3.248249</td>\n",
-       "      <td>3.790767</td>\n",
-       "      <td>6.186442</td>\n",
-       "      <td>2.088125</td>\n",
-       "      <td>7.194899</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>6.186497</td>\n",
-       "      <td>2.961755</td>\n",
-       "      <td>5.296330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>warning</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>3.612144</td>\n",
-       "      <td>3.603834</td>\n",
-       "      <td>8.638458</td>\n",
-       "      <td>8.886211</td>\n",
-       "      <td>8.137879</td>\n",
-       "      <td>8.556664</td>\n",
-       "      <td>9.098408</td>\n",
-       "      <td>3.408112</td>\n",
-       "      <td>5.621890</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Ind        Word 1     Word 2  DS  HC  NK  FI  Average   complex    transe  \\\n",
-       "0    1     admission     ticket   3   3   3   4     3.25  3.611154  4.466324   \n",
-       "1    2       alcohol  chemistry   4   3   3   4     3.50  3.021635  2.743883   \n",
-       "2    3      aluminum      metal   2   2   2   3     2.25  6.944618  4.292894   \n",
-       "3    4  announcement       news   2   2   2   3     2.25  3.248249  3.790767   \n",
-       "4    5  announcement    warning   3   2   3   3     2.75  3.612144  3.603834   \n",
-       "\n",
-       "       text     class    labEmb   textEmb  textSubsetEmb    absEmb  \\\n",
-       "0  4.856529  0.000000  6.809672  4.949738       5.145601  4.268633   \n",
-       "1  4.793135  0.058511  4.554877  4.818231       3.987047  3.399217   \n",
-       "2  6.267841  3.936571  7.643815  6.407051       5.669631  6.865782   \n",
-       "3  6.186442  2.088125  7.194899  6.186497       6.186497  2.961755   \n",
-       "4  8.638458  8.886211  8.137879  8.556664       9.098408  3.408112   \n",
-       "\n",
-       "   absFirstSentEmb  \n",
-       "0         3.770548  \n",
-       "1         4.168040  \n",
-       "2         4.997217  \n",
-       "3         5.296330  \n",
-       "4         5.621890  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_Old.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "artificial-identity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>D_Dim</th>\n",
-       "      <th>P_Sim</th>\n",
-       "      <th>P_Dim</th>\n",
-       "      <th>Avg</th>\n",
-       "      <th>Stdev</th>\n",
-       "      <th>H_orig</th>\n",
-       "      <th>H_reversed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>admission</td>\n",
-       "      <td>ticket</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>u</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.5360</td>\n",
-       "      <td>4.4640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>alcohol</td>\n",
-       "      <td>chemistry</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>H</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>4.1250</td>\n",
-       "      <td>5.8750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>aluminum</td>\n",
-       "      <td>metal</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.547723</td>\n",
-       "      <td>6.6250</td>\n",
-       "      <td>3.3750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>effort</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.0625</td>\n",
-       "      <td>7.9375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>announcement</td>\n",
-       "      <td>news</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>I</td>\n",
-       "      <td>3</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2</td>\n",
-       "      <td>i</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>I</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>0.447214</td>\n",
-       "      <td>7.1875</td>\n",
-       "      <td>2.8125</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID        Word 1     Word 2  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   1     admission     ticket      3     U      3     U      3     U      3   \n",
-       "1   2       alcohol  chemistry      3     D      3     D      3     D      4   \n",
-       "2   3      aluminum      metal      2     I      3     S      2     I      3   \n",
-       "3   4  announcement     effort      4   NaN      4   NaN      4   NaN      4   \n",
-       "4   5  announcement       news      2     I      2     I      3     I      2   \n",
-       "\n",
-       "  D_Dim  P_Sim P_Dim  Avg     Stdev  H_orig  H_reversed  \n",
-       "0     u    3.0     U  3.0  0.000000  5.5360      4.4640  \n",
-       "1   NaN    3.0     H  3.2  0.447214  4.1250      5.8750  \n",
-       "2     i    2.0     I  2.4  0.547723  6.6250      3.3750  \n",
-       "3   NaN    4.0   NaN  4.0  0.000000  2.0625      7.9375  \n",
-       "4     i    2.0     I  2.2  0.447214  7.1875      2.8125  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSim353AnnotDF_New.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "polished-establishment",
-   "metadata": {},
-   "source": [
-    "# Determine Stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "previous-davis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stat1 = wordSim353AnnotDF_Old.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "alternate-watershed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def analyseMetrics(stat1):\n",
-    "    global cols, indNames\n",
-    "\n",
-    "    statsDF = []\n",
-    "\n",
-    "    for col in cols:\n",
-    "        # Identical part\n",
-    "        identTemp = stat1[stat1['Average'] <= 1.75][col]\n",
-    "        iM = identTemp.mean()\n",
-    "        iS = identTemp.std()\n",
-    "\n",
-    "        unrelatedTemp = stat1[stat1['Average'] >= 3.5][col]\n",
-    "        uM = unrelatedTemp.mean()\n",
-    "        uS = unrelatedTemp.std()\n",
-    "        \n",
-    "        midTemp = stat1[(1.75 < stat1['Average']) & (stat1['Average'] < 3.5)][col]\n",
-    "        mM = unrelatedTemp.mean()\n",
-    "        mS = unrelatedTemp.std()\n",
-    "\n",
-    "        statsDF.append([iM, mM, uM, iM - uM, iM + uM, iS, mS, uS])\n",
-    "    statsDF = pd.DataFrame(statsDF, index = indNames, columns = ['Average - I', 'Average - M', 'Average - U', 'Average - I-U', 'Average - I+U', 'StdDev - I', 'StdDev - M', 'StdDev - U'])\n",
-    "    return statsDF.sort_values(by=['Average - I-U'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "chemical-toronto",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Average - I</th>\n",
-       "      <th>Average - M</th>\n",
-       "      <th>Average - U</th>\n",
-       "      <th>Average - I-U</th>\n",
-       "      <th>Average - I+U</th>\n",
-       "      <th>StdDev - I</th>\n",
-       "      <th>StdDev - M</th>\n",
-       "      <th>StdDev - U</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>6.545289</td>\n",
-       "      <td>1.160639</td>\n",
-       "      <td>1.160639</td>\n",
-       "      <td>5.384650</td>\n",
-       "      <td>7.705928</td>\n",
-       "      <td>4.070360</td>\n",
-       "      <td>1.831149</td>\n",
-       "      <td>1.831149</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>7.324299</td>\n",
-       "      <td>4.128195</td>\n",
-       "      <td>4.128195</td>\n",
-       "      <td>3.196105</td>\n",
-       "      <td>11.452494</td>\n",
-       "      <td>2.302197</td>\n",
-       "      <td>2.133653</td>\n",
-       "      <td>2.133653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>7.135948</td>\n",
-       "      <td>3.951633</td>\n",
-       "      <td>3.951633</td>\n",
-       "      <td>3.184315</td>\n",
-       "      <td>11.087581</td>\n",
-       "      <td>2.404671</td>\n",
-       "      <td>1.878835</td>\n",
-       "      <td>1.878835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>6.187317</td>\n",
-       "      <td>3.185329</td>\n",
-       "      <td>3.185329</td>\n",
-       "      <td>3.001988</td>\n",
-       "      <td>9.372647</td>\n",
-       "      <td>2.932166</td>\n",
-       "      <td>1.708353</td>\n",
-       "      <td>1.708353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>6.678897</td>\n",
-       "      <td>3.921987</td>\n",
-       "      <td>3.921987</td>\n",
-       "      <td>2.756910</td>\n",
-       "      <td>10.600885</td>\n",
-       "      <td>2.693491</td>\n",
-       "      <td>1.329798</td>\n",
-       "      <td>1.329798</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>7.789926</td>\n",
-       "      <td>5.254242</td>\n",
-       "      <td>5.254242</td>\n",
-       "      <td>2.535684</td>\n",
-       "      <td>13.044168</td>\n",
-       "      <td>2.461561</td>\n",
-       "      <td>1.662520</td>\n",
-       "      <td>1.662520</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>7.750934</td>\n",
-       "      <td>5.390839</td>\n",
-       "      <td>5.390839</td>\n",
-       "      <td>2.360095</td>\n",
-       "      <td>13.141773</td>\n",
-       "      <td>2.386037</td>\n",
-       "      <td>1.664989</td>\n",
-       "      <td>1.664989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>7.535097</td>\n",
-       "      <td>5.360430</td>\n",
-       "      <td>5.360430</td>\n",
-       "      <td>2.174667</td>\n",
-       "      <td>12.895527</td>\n",
-       "      <td>2.933139</td>\n",
-       "      <td>1.639719</td>\n",
-       "      <td>1.639719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>7.946882</td>\n",
-       "      <td>6.169929</td>\n",
-       "      <td>6.169929</td>\n",
-       "      <td>1.776954</td>\n",
-       "      <td>14.116811</td>\n",
-       "      <td>2.035346</td>\n",
-       "      <td>1.582738</td>\n",
-       "      <td>1.582738</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Average - I  Average - M  Average - U  \\\n",
-       "class (Amandeep)                           6.545289     1.160639     1.160639   \n",
-       "DBPedia short abstracts                    7.324299     4.128195     4.128195   \n",
-       "DBPedia short abstracts first sentence     7.135948     3.951633     3.951633   \n",
-       "transe (Amandeep)                          6.187317     3.185329     3.185329   \n",
-       "complex (Amandeep)                         6.678897     3.921987     3.921987   \n",
-       "Text Embedding - 7 props                   7.789926     5.254242     5.254242   \n",
-       "Text Embedding - P279 + P31                7.750934     5.390839     5.390839   \n",
-       "text (Amandeep)                            7.535097     5.360430     5.360430   \n",
-       "Labels Embedding                           7.946882     6.169929     6.169929   \n",
-       "\n",
-       "                                        Average - I-U  Average - I+U  \\\n",
-       "class (Amandeep)                             5.384650       7.705928   \n",
-       "DBPedia short abstracts                      3.196105      11.452494   \n",
-       "DBPedia short abstracts first sentence       3.184315      11.087581   \n",
-       "transe (Amandeep)                            3.001988       9.372647   \n",
-       "complex (Amandeep)                           2.756910      10.600885   \n",
-       "Text Embedding - 7 props                     2.535684      13.044168   \n",
-       "Text Embedding - P279 + P31                  2.360095      13.141773   \n",
-       "text (Amandeep)                              2.174667      12.895527   \n",
-       "Labels Embedding                             1.776954      14.116811   \n",
-       "\n",
-       "                                        StdDev - I  StdDev - M  StdDev - U  \n",
-       "class (Amandeep)                          4.070360    1.831149    1.831149  \n",
-       "DBPedia short abstracts                   2.302197    2.133653    2.133653  \n",
-       "DBPedia short abstracts first sentence    2.404671    1.878835    1.878835  \n",
-       "transe (Amandeep)                         2.932166    1.708353    1.708353  \n",
-       "complex (Amandeep)                        2.693491    1.329798    1.329798  \n",
-       "Text Embedding - 7 props                  2.461561    1.662520    1.662520  \n",
-       "Text Embedding - P279 + P31               2.386037    1.664989    1.664989  \n",
-       "text (Amandeep)                           2.933139    1.639719    1.639719  \n",
-       "Labels Embedding                          2.035346    1.582738    1.582738  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "analyseMetrics(stat1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "extensive-royalty",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "informative-period",
-   "metadata": {},
-   "source": [
-    "# Merge new annotations and old metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "protected-growth",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF = wordSim353AnnotDF_New.set_index(['Word 1', 'Word 2']).join(wordSim353AnnotDF_Old.set_index(['Word 1', 'Word 2']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "favorite-plate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF = wordSimMergedDF.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "challenging-comment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF.labEmb.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "multiple-oxford",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def labelSamples(score):\n",
-    "    return 'I' if score <= 1.75 else 'U' if score >= 3.5 else 'M'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "dirty-behalf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF['category'] = wordSimMergedDF.Avg.apply(labelSamples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "damaged-skating",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDFBkp = wordSimMergedDF.copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "addressed-therapy",
-   "metadata": {},
-   "source": [
-    "## Change scale of scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "treated-murray",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_scale(score):\n",
-    "    return 4 - (score) * 3 / 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "numerical-mistake",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in cols:\n",
-    "    wordSimMergedDF[col] = wordSimMergedDF[col].apply(update_scale)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "packed-lindsay",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.534530</td>\n",
-       "      <td>3.978657</td>\n",
-       "      <td>2.709135</td>\n",
-       "      <td>2.588876</td>\n",
-       "      <td>2.668504</td>\n",
-       "      <td>2.932919</td>\n",
-       "      <td>3.009117</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>2.934640</td>\n",
-       "      <td>3.987265</td>\n",
-       "      <td>2.745663</td>\n",
-       "      <td>3.046814</td>\n",
-       "      <td>2.937807</td>\n",
-       "      <td>3.247398</td>\n",
-       "      <td>3.584387</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>2.722262</td>\n",
-       "      <td>3.965681</td>\n",
-       "      <td>2.179657</td>\n",
-       "      <td>2.816739</td>\n",
-       "      <td>2.832863</td>\n",
-       "      <td>3.178391</td>\n",
-       "      <td>3.252778</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>2.346065</td>\n",
-       "      <td>3.999655</td>\n",
-       "      <td>2.512379</td>\n",
-       "      <td>2.407004</td>\n",
-       "      <td>2.476275</td>\n",
-       "      <td>2.402353</td>\n",
-       "      <td>3.177374</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>1.226374</td>\n",
-       "      <td>1.271662</td>\n",
-       "      <td>1.870028</td>\n",
-       "      <td>1.301520</td>\n",
-       "      <td>1.268170</td>\n",
-       "      <td>1.487959</td>\n",
-       "      <td>1.405575</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...   complex    transe      text     class    labEmb   textEmb  \\\n",
-       "0  ...  3.103668  2.955021  2.534530  3.978657  2.709135  2.588876   \n",
-       "1  ...  3.076303  3.747795  2.934640  3.987265  2.745663  3.046814   \n",
-       "2  ...  2.881763  3.104421  2.722262  3.965681  2.179657  2.816739   \n",
-       "3  ...  2.911334  3.149125  2.346065  3.999655  2.512379  2.407004   \n",
-       "4  ...  2.135042  2.080022  1.226374  1.271662  1.870028  1.301520   \n",
-       "\n",
-       "   textSubsetEmb    absEmb  absFirstSentEmb  category  \n",
-       "0       2.668504  2.932919         3.009117         U  \n",
-       "1       2.937807  3.247398         3.584387         U  \n",
-       "2       2.832863  3.178391         3.252778         U  \n",
-       "3       2.476275  2.402353         3.177374         M  \n",
-       "4       1.268170  1.487959         1.405575         M  \n",
-       "\n",
-       "[5 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "soviet-exploration",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    224\n",
-       "U    104\n",
-       "I     21\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "included-absolute",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Average - I</th>\n",
-       "      <th>Average - M</th>\n",
-       "      <th>Average - U</th>\n",
-       "      <th>Average - I-U</th>\n",
-       "      <th>Average - I+U</th>\n",
-       "      <th>StdDev - I</th>\n",
-       "      <th>StdDev - M</th>\n",
-       "      <th>StdDev - U</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>1.615935</td>\n",
-       "      <td>2.149021</td>\n",
-       "      <td>2.149021</td>\n",
-       "      <td>-0.533086</td>\n",
-       "      <td>3.764957</td>\n",
-       "      <td>0.610604</td>\n",
-       "      <td>0.474821</td>\n",
-       "      <td>0.474821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>1.739471</td>\n",
-       "      <td>2.391871</td>\n",
-       "      <td>2.391871</td>\n",
-       "      <td>-0.652400</td>\n",
-       "      <td>4.131342</td>\n",
-       "      <td>0.879942</td>\n",
-       "      <td>0.491916</td>\n",
-       "      <td>0.491916</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>1.674720</td>\n",
-       "      <td>2.382748</td>\n",
-       "      <td>2.382748</td>\n",
-       "      <td>-0.708028</td>\n",
-       "      <td>4.057468</td>\n",
-       "      <td>0.715811</td>\n",
-       "      <td>0.499497</td>\n",
-       "      <td>0.499497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>1.663022</td>\n",
-       "      <td>2.423727</td>\n",
-       "      <td>2.423727</td>\n",
-       "      <td>-0.760705</td>\n",
-       "      <td>4.086750</td>\n",
-       "      <td>0.738468</td>\n",
-       "      <td>0.498756</td>\n",
-       "      <td>0.498756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>1.996331</td>\n",
-       "      <td>2.823404</td>\n",
-       "      <td>2.823404</td>\n",
-       "      <td>-0.827073</td>\n",
-       "      <td>4.819735</td>\n",
-       "      <td>0.808047</td>\n",
-       "      <td>0.398939</td>\n",
-       "      <td>0.398939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>2.143805</td>\n",
-       "      <td>3.044401</td>\n",
-       "      <td>3.044401</td>\n",
-       "      <td>-0.900596</td>\n",
-       "      <td>5.188206</td>\n",
-       "      <td>0.879650</td>\n",
-       "      <td>0.512506</td>\n",
-       "      <td>0.512506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>1.859216</td>\n",
-       "      <td>2.814510</td>\n",
-       "      <td>2.814510</td>\n",
-       "      <td>-0.955294</td>\n",
-       "      <td>4.673726</td>\n",
-       "      <td>0.721401</td>\n",
-       "      <td>0.563650</td>\n",
-       "      <td>0.563650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>1.802710</td>\n",
-       "      <td>2.761542</td>\n",
-       "      <td>2.761542</td>\n",
-       "      <td>-0.958831</td>\n",
-       "      <td>4.564252</td>\n",
-       "      <td>0.690659</td>\n",
-       "      <td>0.640096</td>\n",
-       "      <td>0.640096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>2.036413</td>\n",
-       "      <td>3.651808</td>\n",
-       "      <td>3.651808</td>\n",
-       "      <td>-1.615395</td>\n",
-       "      <td>5.688222</td>\n",
-       "      <td>1.221108</td>\n",
-       "      <td>0.549345</td>\n",
-       "      <td>0.549345</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Average - I  Average - M  Average - U  \\\n",
-       "Labels Embedding                           1.615935     2.149021     2.149021   \n",
-       "text (Amandeep)                            1.739471     2.391871     2.391871   \n",
-       "Text Embedding - P279 + P31                1.674720     2.382748     2.382748   \n",
-       "Text Embedding - 7 props                   1.663022     2.423727     2.423727   \n",
-       "complex (Amandeep)                         1.996331     2.823404     2.823404   \n",
-       "transe (Amandeep)                          2.143805     3.044401     3.044401   \n",
-       "DBPedia short abstracts first sentence     1.859216     2.814510     2.814510   \n",
-       "DBPedia short abstracts                    1.802710     2.761542     2.761542   \n",
-       "class (Amandeep)                           2.036413     3.651808     3.651808   \n",
-       "\n",
-       "                                        Average - I-U  Average - I+U  \\\n",
-       "Labels Embedding                            -0.533086       3.764957   \n",
-       "text (Amandeep)                             -0.652400       4.131342   \n",
-       "Text Embedding - P279 + P31                 -0.708028       4.057468   \n",
-       "Text Embedding - 7 props                    -0.760705       4.086750   \n",
-       "complex (Amandeep)                          -0.827073       4.819735   \n",
-       "transe (Amandeep)                           -0.900596       5.188206   \n",
-       "DBPedia short abstracts first sentence      -0.955294       4.673726   \n",
-       "DBPedia short abstracts                     -0.958831       4.564252   \n",
-       "class (Amandeep)                            -1.615395       5.688222   \n",
-       "\n",
-       "                                        StdDev - I  StdDev - M  StdDev - U  \n",
-       "Labels Embedding                          0.610604    0.474821    0.474821  \n",
-       "text (Amandeep)                           0.879942    0.491916    0.491916  \n",
-       "Text Embedding - P279 + P31               0.715811    0.499497    0.499497  \n",
-       "Text Embedding - 7 props                  0.738468    0.498756    0.498756  \n",
-       "complex (Amandeep)                        0.808047    0.398939    0.398939  \n",
-       "transe (Amandeep)                         0.879650    0.512506    0.512506  \n",
-       "DBPedia short abstracts first sentence    0.721401    0.563650    0.563650  \n",
-       "DBPedia short abstracts                   0.690659    0.640096    0.640096  \n",
-       "class (Amandeep)                          1.221108    0.549345    0.549345  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "analyseMetrics(wordSimMergedDF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "iraqi-horizon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "349"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wordSimMergedDF)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wanted-booking",
-   "metadata": {},
-   "source": [
-    "# Stratified Split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sharp-sailing",
-   "metadata": {},
-   "source": [
-    "## Data Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "dying-rebel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X = wordSimMergedDF[cols]\n",
-    "Y = wordSimMergedDF['category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "raised-local",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n",
-    "                                                    stratify=Y, \n",
-    "                                                    test_size=0.1, \n",
-    "                                                    random_state=19)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "perfect-lexington",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.334137</td>\n",
-       "      <td>2.918850</td>\n",
-       "      <td>1.408463</td>\n",
-       "      <td>2.916357</td>\n",
-       "      <td>1.558636</td>\n",
-       "      <td>1.433001</td>\n",
-       "      <td>1.270478</td>\n",
-       "      <td>2.977566</td>\n",
-       "      <td>2.313433</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>3.959855</td>\n",
-       "      <td>3.137178</td>\n",
-       "      <td>2.662989</td>\n",
-       "      <td>3.256807</td>\n",
-       "      <td>1.405574</td>\n",
-       "      <td>2.641012</td>\n",
-       "      <td>2.641012</td>\n",
-       "      <td>2.994895</td>\n",
-       "      <td>2.523780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>223</th>\n",
-       "      <td>3.789260</td>\n",
-       "      <td>2.577913</td>\n",
-       "      <td>2.584808</td>\n",
-       "      <td>2.625197</td>\n",
-       "      <td>2.132757</td>\n",
-       "      <td>2.667512</td>\n",
-       "      <td>2.727592</td>\n",
-       "      <td>2.587481</td>\n",
-       "      <td>2.908610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>3.997609</td>\n",
-       "      <td>3.379192</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.957218</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>3.901682</td>\n",
-       "      <td>2.414060</td>\n",
-       "      <td>2.662876</td>\n",
-       "      <td>2.683717</td>\n",
-       "      <td>2.491675</td>\n",
-       "      <td>2.525770</td>\n",
-       "      <td>2.595170</td>\n",
-       "      <td>1.872327</td>\n",
-       "      <td>2.476093</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        class    transe      text   complex    labEmb   textEmb  \\\n",
-       "20   1.334137  2.918850  1.408463  2.916357  1.558636  1.433001   \n",
-       "149  3.959855  3.137178  2.662989  3.256807  1.405574  2.641012   \n",
-       "223  3.789260  2.577913  2.584808  2.625197  2.132757  2.667512   \n",
-       "181  3.997609  3.379192  4.000000  2.957218  4.000000  4.000000   \n",
-       "250  3.901682  2.414060  2.662876  2.683717  2.491675  2.525770   \n",
-       "\n",
-       "     textSubsetEmb    absEmb  absFirstSentEmb  \n",
-       "20        1.270478  2.977566         2.313433  \n",
-       "149       2.641012  2.994895         2.523780  \n",
-       "223       2.727592  2.587481         2.908610  \n",
-       "181       4.000000  4.000000         4.000000  \n",
-       "250       2.595170  1.872327         2.476093  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "willing-russell",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    201\n",
-       "U     94\n",
-       "I     19\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_train.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "handmade-invitation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    23\n",
-       "U    10\n",
-       "I     2\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_test.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "centered-library",
-   "metadata": {},
-   "source": [
-    "## SVM Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "stylish-beach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "comparative-heater",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
-       "                ('svc', SVC(gamma='auto', random_state=100))])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf.fit(X_train, Y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "million-representative",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['M', 'M', 'M', 'I', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
-       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'U', 'M',\n",
-       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'U', 'M'], dtype=object)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = clf.predict(X_test)\n",
-    "preds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adolescent-information",
-   "metadata": {},
-   "source": [
-    "## Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "intense-league",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "undefined-dream",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6857142857142857"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy_score(preds, Y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "diverse-welding",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    23\n",
-       "U    10\n",
-       "I     2\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_test.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "viral-beast",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f8fa919f850>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_confusion_matrix(clf, X_test, Y_test,\n",
-    "                                 display_labels=clf.classes_,\n",
-    "                                 cmap=plt.cm.Blues)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "junior-chicago",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>2.550722</td>\n",
-       "      <td>3.110214</td>\n",
-       "      <td>3.049062</td>\n",
-       "      <td>3.092837</td>\n",
-       "      <td>2.061545</td>\n",
-       "      <td>2.943750</td>\n",
-       "      <td>3.050729</td>\n",
-       "      <td>2.721214</td>\n",
-       "      <td>2.462581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>299</th>\n",
-       "      <td>3.883358</td>\n",
-       "      <td>2.796661</td>\n",
-       "      <td>2.143449</td>\n",
-       "      <td>2.835941</td>\n",
-       "      <td>1.841262</td>\n",
-       "      <td>2.143459</td>\n",
-       "      <td>2.143459</td>\n",
-       "      <td>1.841262</td>\n",
-       "      <td>1.841262</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>3.335357</td>\n",
-       "      <td>3.782758</td>\n",
-       "      <td>2.397896</td>\n",
-       "      <td>3.467046</td>\n",
-       "      <td>1.874207</td>\n",
-       "      <td>2.414705</td>\n",
-       "      <td>2.329012</td>\n",
-       "      <td>3.391344</td>\n",
-       "      <td>3.015135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>175</th>\n",
-       "      <td>3.491651</td>\n",
-       "      <td>3.102310</td>\n",
-       "      <td>2.709157</td>\n",
-       "      <td>3.088045</td>\n",
-       "      <td>2.322339</td>\n",
-       "      <td>2.764514</td>\n",
-       "      <td>2.662755</td>\n",
-       "      <td>3.305087</td>\n",
-       "      <td>3.157234</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        class    transe      text   complex    labEmb   textEmb  \\\n",
-       "161  2.550722  3.110214  3.049062  3.092837  2.061545  2.943750   \n",
-       "299  3.883358  2.796661  2.143449  2.835941  1.841262  2.143459   \n",
-       "57   3.335357  3.782758  2.397896  3.467046  1.874207  2.414705   \n",
-       "127  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "175  3.491651  3.102310  2.709157  3.088045  2.322339  2.764514   \n",
-       "\n",
-       "     textSubsetEmb    absEmb  absFirstSentEmb  \n",
-       "161       3.050729  2.721214         2.462581  \n",
-       "299       2.143459  1.841262         1.841262  \n",
-       "57        2.329012  3.391344         3.015135  \n",
-       "127       1.000000  1.000000         1.000000  \n",
-       "175       2.662755  3.305087         3.157234  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "known-bunch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>60.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>57.142857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>51.428571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>45.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "DBPedia short abstracts                                                      62.857143\n",
-       "DBPedia short abstracts first sentence                                       62.857143\n",
-       "Labels Embedding                                                             60.000000\n",
-       "complex (Amandeep)                                                           57.142857\n",
-       "text (Amandeep)                                                              54.285714\n",
-       "Text Embedding - 7 props                                                     54.285714\n",
-       "Text Embedding - P279 + P31                                                  54.285714\n",
-       "class (Amandeep)                                                             51.428571\n",
-       "transe (Amandeep)                                                            45.714286"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    tempVals.append(accuracy_score(Y_test, X_test[col].apply(labelSamples)))\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "international-forwarding",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>68.571429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>65.714286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>65.714286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>65.714286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "Text Embedding - 7 props                                                     68.571429\n",
-       "class (Amandeep)                                                             65.714286\n",
-       "text (Amandeep)                                                              65.714286\n",
-       "DBPedia short abstracts first sentence                                       65.714286\n",
-       "transe (Amandeep)                                                            62.857143\n",
-       "complex (Amandeep)                                                           62.857143\n",
-       "Labels Embedding                                                             62.857143\n",
-       "Text Embedding - P279 + P31                                                  62.857143\n",
-       "DBPedia short abstracts                                                      62.857143"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "    clf1.fit(X_train[[col]], Y_train)\n",
-    "    tempVals.append(accuracy_score(Y_test, clf1.predict(X_test[[col]])))\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "champion-escape",
-   "metadata": {},
-   "source": [
-    "# Stratified K-fold split\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precious-grave",
-   "metadata": {},
-   "source": [
-    "## Data Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "running-illustration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "X = wordSimMergedDF[cols]\n",
-    "Y = wordSimMergedDF['category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "devoted-healing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skf = StratifiedKFold(n_splits=5, random_state=19, shuffle=True)\n",
-    "X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "for train_index, test_index in skf.split(X, Y):\n",
-    "    X_train_splits.append(X.iloc[train_index])\n",
-    "    X_test_splits.append(X.iloc[test_index])\n",
-    "    Y_train_splits.append(Y.iloc[train_index])\n",
-    "    Y_test_splits.append(Y.iloc[test_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "controversial-passport",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3.978657</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.534530</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.709135</td>\n",
-       "      <td>2.588876</td>\n",
-       "      <td>2.668504</td>\n",
-       "      <td>2.932919</td>\n",
-       "      <td>3.009117</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3.987265</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>2.934640</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>2.745663</td>\n",
-       "      <td>3.046814</td>\n",
-       "      <td>2.937807</td>\n",
-       "      <td>3.247398</td>\n",
-       "      <td>3.584387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.999655</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>2.346065</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>2.512379</td>\n",
-       "      <td>2.407004</td>\n",
-       "      <td>2.476275</td>\n",
-       "      <td>2.402353</td>\n",
-       "      <td>3.177374</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.271662</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>1.226374</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>1.870028</td>\n",
-       "      <td>1.301520</td>\n",
-       "      <td>1.268170</td>\n",
-       "      <td>1.487959</td>\n",
-       "      <td>1.405575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.282710</td>\n",
-       "      <td>2.496058</td>\n",
-       "      <td>2.230036</td>\n",
-       "      <td>2.408249</td>\n",
-       "      <td>2.101260</td>\n",
-       "      <td>2.163500</td>\n",
-       "      <td>2.210558</td>\n",
-       "      <td>3.292768</td>\n",
-       "      <td>2.728319</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      class    transe      text   complex    labEmb   textEmb  textSubsetEmb  \\\n",
-       "0  3.978657  2.955021  2.534530  3.103668  2.709135  2.588876       2.668504   \n",
-       "1  3.987265  3.747795  2.934640  3.076303  2.745663  3.046814       2.937807   \n",
-       "3  3.999655  3.149125  2.346065  2.911334  2.512379  2.407004       2.476275   \n",
-       "4  1.271662  2.080022  1.226374  2.135042  1.870028  1.301520       1.268170   \n",
-       "5  1.282710  2.496058  2.230036  2.408249  2.101260  2.163500       2.210558   \n",
-       "\n",
-       "     absEmb  absFirstSentEmb  \n",
-       "0  2.932919         3.009117  \n",
-       "1  3.247398         3.584387  \n",
-       "3  2.402353         3.177374  \n",
-       "4  1.487959         1.405575  \n",
-       "5  3.292768         2.728319  "
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_splits[0].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "opponent-drain",
-   "metadata": {},
-   "source": [
-    "## SVM Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "finnish-language",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "democratic-jesus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = []\n",
-    "for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "    clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "    clf.fit(X_train1, Y_train1)\n",
-    "    preds.append(clf.predict(X_test1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "southwest-reviewer",
-   "metadata": {},
-   "source": [
-    "## Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "secret-plymouth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6445962732919255"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "acc = 0\n",
-    "for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "    acc += accuracy_score(pred, Y_test1)\n",
-    "acc/5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "opening-ancient",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>60.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>57.142857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>51.428571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>45.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "DBPedia short abstracts                                                      62.857143\n",
-       "DBPedia short abstracts first sentence                                       62.857143\n",
-       "Labels Embedding                                                             60.000000\n",
-       "complex (Amandeep)                                                           57.142857\n",
-       "text (Amandeep)                                                              54.285714\n",
-       "Text Embedding - 7 props                                                     54.285714\n",
-       "Text Embedding - P279 + P31                                                  54.285714\n",
-       "class (Amandeep)                                                             51.428571\n",
-       "transe (Amandeep)                                                            45.714286"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    tempVals.append(accuracy_score(Y_test, X_test[col].apply(labelSamples)))\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "secure-barcelona",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>67.337474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>66.757764</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>64.467909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>63.896480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>63.896480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>63.896480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>63.614907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>63.325052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>61.590062</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "Text Embedding - 7 props                                                     67.337474\n",
-       "text (Amandeep)                                                              66.757764\n",
-       "complex (Amandeep)                                                           64.467909\n",
-       "class (Amandeep)                                                             63.896480\n",
-       "transe (Amandeep)                                                            63.896480\n",
-       "DBPedia short abstracts                                                      63.896480\n",
-       "Text Embedding - P279 + P31                                                  63.614907\n",
-       "Labels Embedding                                                             63.325052\n",
-       "DBPedia short abstracts first sentence                                       61.590062"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf1.fit(X_train1[[col]], Y_train1)\n",
-    "        preds.append(clf1.predict(X_test1[[col]]))\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "    tempVals.append(acc/5)\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "passing-screen",
-   "metadata": {},
-   "source": [
-    "# Stratified 15-fold split\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "union-execution",
-   "metadata": {},
-   "source": [
-    "## Data Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "everyday-lounge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "X = wordSimMergedDF[cols]\n",
-    "Y = wordSimMergedDF['category']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "terminal-assault",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skf = StratifiedKFold(n_splits=15, random_state=19, shuffle=True)\n",
-    "X_train_splits, X_test_splits, Y_train_splits, Y_test_splits = [], [], [], []\n",
-    "for train_index, test_index in skf.split(X, Y):\n",
-    "    X_train_splits.append(X.iloc[train_index])\n",
-    "    X_test_splits.append(X.iloc[test_index])\n",
-    "    Y_train_splits.append(Y.iloc[train_index])\n",
-    "    Y_test_splits.append(Y.iloc[test_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "exotic-mount",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3.978657</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.534530</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.709135</td>\n",
-       "      <td>2.588876</td>\n",
-       "      <td>2.668504</td>\n",
-       "      <td>2.932919</td>\n",
-       "      <td>3.009117</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3.987265</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>2.934640</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>2.745663</td>\n",
-       "      <td>3.046814</td>\n",
-       "      <td>2.937807</td>\n",
-       "      <td>3.247398</td>\n",
-       "      <td>3.584387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.965681</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>2.722262</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>2.179657</td>\n",
-       "      <td>2.816739</td>\n",
-       "      <td>2.832863</td>\n",
-       "      <td>3.178391</td>\n",
-       "      <td>3.252778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.999655</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>2.346065</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>2.512379</td>\n",
-       "      <td>2.407004</td>\n",
-       "      <td>2.476275</td>\n",
-       "      <td>2.402353</td>\n",
-       "      <td>3.177374</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.271662</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>1.226374</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>1.870028</td>\n",
-       "      <td>1.301520</td>\n",
-       "      <td>1.268170</td>\n",
-       "      <td>1.487959</td>\n",
-       "      <td>1.405575</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      class    transe      text   complex    labEmb   textEmb  textSubsetEmb  \\\n",
-       "0  3.978657  2.955021  2.534530  3.103668  2.709135  2.588876       2.668504   \n",
-       "1  3.987265  3.747795  2.934640  3.076303  2.745663  3.046814       2.937807   \n",
-       "2  3.965681  3.104421  2.722262  2.881763  2.179657  2.816739       2.832863   \n",
-       "3  3.999655  3.149125  2.346065  2.911334  2.512379  2.407004       2.476275   \n",
-       "4  1.271662  2.080022  1.226374  2.135042  1.870028  1.301520       1.268170   \n",
-       "\n",
-       "     absEmb  absFirstSentEmb  \n",
-       "0  2.932919         3.009117  \n",
-       "1  3.247398         3.584387  \n",
-       "2  3.178391         3.252778  \n",
-       "3  2.402353         3.177374  \n",
-       "4  1.487959         1.405575  "
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_splits[0].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tired-filling",
-   "metadata": {},
-   "source": [
-    "## SVM Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "defensive-instrument",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import seaborn as sns\n",
-    "from sklearn.metrics import plot_confusion_matrix\n",
-    "import matplotlib.pyplot as plt "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "improving-montgomery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = []\n",
-    "for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "    clf = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "    clf.fit(X_train1, Y_train1)\n",
-    "    preds.append(clf.predict(X_test1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "complex-native",
-   "metadata": {},
-   "source": [
-    "## Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "experimental-camel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.656159420289855"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "acc = 0\n",
-    "for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "    acc += accuracy_score(pred, Y_test1)\n",
-    "acc/15"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "exact-vegetable",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>62.857143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>60.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>57.142857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>54.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>51.428571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>45.714286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "DBPedia short abstracts                                                      62.857143\n",
-       "DBPedia short abstracts first sentence                                       62.857143\n",
-       "Labels Embedding                                                             60.000000\n",
-       "complex (Amandeep)                                                           57.142857\n",
-       "text (Amandeep)                                                              54.285714\n",
-       "Text Embedding - 7 props                                                     54.285714\n",
-       "Text Embedding - P279 + P31                                                  54.285714\n",
-       "class (Amandeep)                                                             51.428571\n",
-       "transe (Amandeep)                                                            45.714286"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    tempVals.append(accuracy_score(Y_test, X_test[col].apply(labelSamples)))\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "martial-corpus",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Accuracy (in %) compared to annotated category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - 7 props</th>\n",
-       "      <td>66.473430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>text (Amandeep)</th>\n",
-       "      <td>66.171498</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>transe (Amandeep)</th>\n",
-       "      <td>65.048309</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>complex (Amandeep)</th>\n",
-       "      <td>65.048309</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Labels Embedding</th>\n",
-       "      <td>64.480676</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Text Embedding - P279 + P31</th>\n",
-       "      <td>63.913043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class (Amandeep)</th>\n",
-       "      <td>63.623188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts</th>\n",
-       "      <td>63.623188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DBPedia short abstracts first sentence</th>\n",
-       "      <td>61.026570</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                        Accuracy (in %) compared to annotated category\n",
-       "Text Embedding - 7 props                                                     66.473430\n",
-       "text (Amandeep)                                                              66.171498\n",
-       "transe (Amandeep)                                                            65.048309\n",
-       "complex (Amandeep)                                                           65.048309\n",
-       "Labels Embedding                                                             64.480676\n",
-       "Text Embedding - P279 + P31                                                  63.913043\n",
-       "class (Amandeep)                                                             63.623188\n",
-       "DBPedia short abstracts                                                      63.623188\n",
-       "DBPedia short abstracts first sentence                                       61.026570"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempVals = []\n",
-    "for col in cols:\n",
-    "    preds = []\n",
-    "    for X_train1, Y_train1, X_test1, Y_test1 in zip(X_train_splits, Y_train_splits, X_test_splits, Y_test_splits):\n",
-    "        clf1 = make_pipeline(StandardScaler(), SVC(gamma='auto', random_state=100))\n",
-    "        clf1.fit(X_train1[[col]], Y_train1)\n",
-    "        preds.append(clf1.predict(X_test1[[col]]))\n",
-    "    acc = 0\n",
-    "    for pred, Y_test1 in zip(preds, Y_test_splits):\n",
-    "        acc += accuracy_score(pred, Y_test1)\n",
-    "    tempVals.append(acc/15)\n",
-    "(pd.DataFrame(tempVals, index = indNames, columns = ['Accuracy (in %) compared to annotated category']) * 100).sort_values(by=['Accuracy (in %) compared to annotated category'], ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "native-delivery",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "varying-manchester",
-   "metadata": {},
-   "source": [
-    "# Determine Entropy Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "rolled-burton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import entropy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "finnish-brighton",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Word 1</th>\n",
-       "      <th>Word 2</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>H_Sim</th>\n",
-       "      <th>H_Dim</th>\n",
-       "      <th>F_Sim</th>\n",
-       "      <th>F_Dim</th>\n",
-       "      <th>N_Sim</th>\n",
-       "      <th>N_Dim</th>\n",
-       "      <th>D_Sim</th>\n",
-       "      <th>...</th>\n",
-       "      <th>complex</th>\n",
-       "      <th>transe</th>\n",
-       "      <th>text</th>\n",
-       "      <th>class</th>\n",
-       "      <th>labEmb</th>\n",
-       "      <th>textEmb</th>\n",
-       "      <th>textSubsetEmb</th>\n",
-       "      <th>absEmb</th>\n",
-       "      <th>absFirstSentEmb</th>\n",
-       "      <th>category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>peace</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.103668</td>\n",
-       "      <td>2.955021</td>\n",
-       "      <td>2.534530</td>\n",
-       "      <td>3.978657</td>\n",
-       "      <td>2.709135</td>\n",
-       "      <td>2.588876</td>\n",
-       "      <td>2.668504</td>\n",
-       "      <td>2.932919</td>\n",
-       "      <td>3.009117</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Arafat</td>\n",
-       "      <td>terror</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.076303</td>\n",
-       "      <td>3.747795</td>\n",
-       "      <td>2.934640</td>\n",
-       "      <td>3.987265</td>\n",
-       "      <td>2.745663</td>\n",
-       "      <td>3.046814</td>\n",
-       "      <td>2.937807</td>\n",
-       "      <td>3.247398</td>\n",
-       "      <td>3.584387</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>fingerprint</td>\n",
-       "      <td>109</td>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.881763</td>\n",
-       "      <td>3.104421</td>\n",
-       "      <td>2.722262</td>\n",
-       "      <td>3.965681</td>\n",
-       "      <td>2.179657</td>\n",
-       "      <td>2.816739</td>\n",
-       "      <td>2.832863</td>\n",
-       "      <td>3.178391</td>\n",
-       "      <td>3.252778</td>\n",
-       "      <td>U</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FBI</td>\n",
-       "      <td>investigation</td>\n",
-       "      <td>110</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.911334</td>\n",
-       "      <td>3.149125</td>\n",
-       "      <td>2.346065</td>\n",
-       "      <td>3.999655</td>\n",
-       "      <td>2.512379</td>\n",
-       "      <td>2.407004</td>\n",
-       "      <td>2.476275</td>\n",
-       "      <td>2.402353</td>\n",
-       "      <td>3.177374</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Harvard</td>\n",
-       "      <td>Yale</td>\n",
-       "      <td>137</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.135042</td>\n",
-       "      <td>2.080022</td>\n",
-       "      <td>1.226374</td>\n",
-       "      <td>1.271662</td>\n",
-       "      <td>1.870028</td>\n",
-       "      <td>1.301520</td>\n",
-       "      <td>1.268170</td>\n",
-       "      <td>1.487959</td>\n",
-       "      <td>1.405575</td>\n",
-       "      <td>M</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Word 1         Word 2   ID  H_Sim H_Dim  F_Sim F_Dim  N_Sim N_Dim  D_Sim  \\\n",
-       "0   Arafat          peace    8      3     D      4   NaN      3     U      4   \n",
-       "1   Arafat         terror    9      3     D      4   NaN      3     U      4   \n",
-       "2      FBI    fingerprint  109      3     D      4   NaN      4   NaN      3   \n",
-       "3      FBI  investigation  110      3     U      3     U      3     U      3   \n",
-       "4  Harvard           Yale  137      2     S      3     S      2     S      2   \n",
-       "\n",
-       "   ...   complex    transe      text     class    labEmb   textEmb  \\\n",
-       "0  ...  3.103668  2.955021  2.534530  3.978657  2.709135  2.588876   \n",
-       "1  ...  3.076303  3.747795  2.934640  3.987265  2.745663  3.046814   \n",
-       "2  ...  2.881763  3.104421  2.722262  3.965681  2.179657  2.816739   \n",
-       "3  ...  2.911334  3.149125  2.346065  3.999655  2.512379  2.407004   \n",
-       "4  ...  2.135042  2.080022  1.226374  1.271662  1.870028  1.301520   \n",
-       "\n",
-       "   textSubsetEmb    absEmb  absFirstSentEmb  category  \n",
-       "0       2.668504  2.932919         3.009117         U  \n",
-       "1       2.937807  3.247398         3.584387         U  \n",
-       "2       2.832863  3.178391         3.252778         U  \n",
-       "3       2.476275  2.402353         3.177374         M  \n",
-       "4       1.268170  1.487959         1.405575         M  \n",
-       "\n",
-       "[5 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "incorrect-scheduling",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findEntropy(row, minDims=1):\n",
-    "    dims = {'U': 0, 'A': 0, 'H': 0, 'I': 0, 'S': 0, 'D': 0, 'L': 0, 'X': 0}\n",
-    "    cols = ['H_Dim', 'F_Dim', 'N_Dim', 'D_Dim', 'P_Dim']\n",
-    "    for col in cols:\n",
-    "        try:\n",
-    "            if row[col] == 'O':\n",
-    "                row[col] = 'D'\n",
-    "            if type(row[col]) == str and len(row[col]) > 1:\n",
-    "                row[col] = row[col][:1]\n",
-    "            if not(pd.isna(row[col])):\n",
-    "                dims[row[col].upper()] += 1\n",
-    "            else:\n",
-    "                dims['X'] += 1\n",
-    "        except:\n",
-    "            print(f\"Failed while processing value {row[col]}\")\n",
-    "            dims['X'] += 1\n",
-    "    probArr = [dims[val]/5 for val in ['U', 'A', 'H', 'I', 'S', 'D', 'L', 'X']]\n",
-    "    \n",
-    "    cntDims = sum([dims[val] for val in ['U', 'A', 'H', 'I', 'S', 'D', 'L']])\n",
-    "    \n",
-    "    assert sum(probArr) == 1\n",
-    "    \n",
-    "    if cntDims >= minDims:\n",
-    "        return probArr + [cntDims, entropy(probArr, base=2)]\n",
-    "\n",
-    "    else:\n",
-    "        return [0]*5 + [cntDims, None]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "handmade-import",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No. of anomalies for each annotator: [1, 2, 2, 1, 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "dimMapp = {'U': 0, 'A': 0, 'H': 0, 'I': 0, 'S': 0, 'D': 0, 'L': 0}\n",
-    "print(f\"No. of anomalies for each annotator: {[wordSimMergedDF[col].apply(lambda p: not(pd.isna(p)) and type(p) == str and p.upper() not in dimMapp).sum() for col in ['H_Dim', 'F_Dim', 'N_Dim', 'D_Dim', 'P_Dim']]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "maritime-symbol",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unique list of dimension values: {'A, D', 'S, H', 'D', 'P', 'S', 'A', 'H', '2', 'L', 'U', 'O', 'I', '1'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = [wordSimMergedDF[col].unique() for col in ['H_Dim', 'F_Dim', 'N_Dim', 'D_Dim', 'P_Dim']]\n",
-    "dimValsList = [item for sublist in t for item in sublist]\n",
-    "print(f\"Unique list of dimension values: {set([char1.upper() for char1 in dimValsList if type(char1) == str ])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "structural-cricket",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed while processing value P\n",
-      "Failed while processing value P\n",
-      "Failed while processing value 1\n",
-      "Failed while processing value 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy = pd.DataFrame(wordSimMergedDF.apply(findEntropy,axis=1).to_list(), columns = ['U', 'A', 'H', 'I', 'S', 'D', 'L', 'X', 'countDims', 'Entropy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "secret-burden",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>U</th>\n",
-       "      <th>A</th>\n",
-       "      <th>H</th>\n",
-       "      <th>I</th>\n",
-       "      <th>S</th>\n",
-       "      <th>D</th>\n",
-       "      <th>L</th>\n",
-       "      <th>X</th>\n",
-       "      <th>countDims</th>\n",
-       "      <th>Entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>286.000000</td>\n",
-       "      <td>286.000000</td>\n",
-       "      <td>286.000000</td>\n",
-       "      <td>286.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.059599</td>\n",
-       "      <td>0.018338</td>\n",
-       "      <td>0.108309</td>\n",
-       "      <td>0.130659</td>\n",
-       "      <td>0.108309</td>\n",
-       "      <td>0.151862</td>\n",
-       "      <td>0.041259</td>\n",
-       "      <td>0.254545</td>\n",
-       "      <td>3.727273</td>\n",
-       "      <td>0.982808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.160104</td>\n",
-       "      <td>0.107781</td>\n",
-       "      <td>0.209720</td>\n",
-       "      <td>0.273136</td>\n",
-       "      <td>0.255210</td>\n",
-       "      <td>0.224321</td>\n",
-       "      <td>0.146457</td>\n",
-       "      <td>0.285000</td>\n",
-       "      <td>1.424999</td>\n",
-       "      <td>0.563678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.721928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>0.970951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1.370951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>2.321928</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                U           A           H           I           S           D  \\\n",
-       "count  349.000000  349.000000  349.000000  349.000000  349.000000  349.000000   \n",
-       "mean     0.059599    0.018338    0.108309    0.130659    0.108309    0.151862   \n",
-       "std      0.160104    0.107781    0.209720    0.273136    0.255210    0.224321   \n",
-       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "75%      0.000000    0.000000    0.200000    0.200000    0.000000    0.200000   \n",
-       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
-       "\n",
-       "                L           X   countDims     Entropy  \n",
-       "count  286.000000  286.000000  286.000000  286.000000  \n",
-       "mean     0.041259    0.254545    3.727273    0.982808  \n",
-       "std      0.146457    0.285000    1.424999    0.563678  \n",
-       "min      0.000000    0.000000    1.000000    0.000000  \n",
-       "25%      0.000000    0.000000    3.000000    0.721928  \n",
-       "50%      0.000000    0.200000    4.000000    0.970951  \n",
-       "75%      0.000000    0.400000    5.000000    1.370951  \n",
-       "max      1.000000    0.800000    5.000000    2.321928  "
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "failing-subscription",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>U</th>\n",
-       "      <th>A</th>\n",
-       "      <th>H</th>\n",
-       "      <th>I</th>\n",
-       "      <th>S</th>\n",
-       "      <th>D</th>\n",
-       "      <th>L</th>\n",
-       "      <th>X</th>\n",
-       "      <th>countDims</th>\n",
-       "      <th>Entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.370951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.370951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.370951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     U    A    H    I    S    D    L    X  countDims   Entropy\n",
-       "0  0.2  0.0  0.0  0.0  0.0  0.2  0.0  0.6        2.0  1.370951\n",
-       "1  0.2  0.0  0.0  0.0  0.0  0.2  0.0  0.6        2.0  1.370951\n",
-       "2  0.2  0.0  0.0  0.0  0.0  0.2  0.0  0.6        2.0  1.370951\n",
-       "3  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0        5.0  0.000000\n",
-       "4  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0        5.0  0.000000"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "active-highlight",
-   "metadata": {},
-   "source": [
-    "## Dimension counts analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "northern-shadow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def countDimensions1(row, minDims=1):\n",
-    "    dims = {'U': 0, 'A': 0, 'H': 0, 'I': 0, 'S': 0, 'D': 0, 'L': 0, 'X': 0}\n",
-    "    cols = ['H_Dim', 'F_Dim', 'N_Dim', 'D_Dim', 'P_Dim']\n",
-    "    for col in cols:\n",
-    "        try:\n",
-    "            if row[col] == 'O':\n",
-    "                row[col] = 'D'\n",
-    "            if type(row[col]) == str and len(row[col]) > 1:\n",
-    "                row[col] = row[col][:1]\n",
-    "            if not(pd.isna(row[col])):\n",
-    "                dims[row[col].upper()] += 1\n",
-    "            else:\n",
-    "                dims['X'] += 1\n",
-    "        except:\n",
-    "            print(f\"Failed while processing value {row[col]}\")\n",
-    "            dims['X'] += 1\n",
-    "    probArr = [dims[val] for val in ['U', 'A', 'H', 'I', 'S', 'D', 'L', 'X']]\n",
-    "    \n",
-    "    return probArr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "parallel-tongue",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed while processing value P\n",
-      "Failed while processing value P\n",
-      "Failed while processing value 1\n",
-      "Failed while processing value 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSimMergedDF_dimCount = pd.DataFrame(wordSimMergedDF.apply(countDimensions1,axis=1).to_list(), columns = ['U', 'A', 'H', 'I', 'S', 'D', 'L', 'X'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "opponent-capital",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>U</th>\n",
-       "      <th>A</th>\n",
-       "      <th>H</th>\n",
-       "      <th>I</th>\n",
-       "      <th>S</th>\n",
-       "      <th>D</th>\n",
-       "      <th>L</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.297994</td>\n",
-       "      <td>0.091691</td>\n",
-       "      <td>0.541547</td>\n",
-       "      <td>0.653295</td>\n",
-       "      <td>0.541547</td>\n",
-       "      <td>0.759312</td>\n",
-       "      <td>0.169054</td>\n",
-       "      <td>1.945559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.800518</td>\n",
-       "      <td>0.538906</td>\n",
-       "      <td>1.048600</td>\n",
-       "      <td>1.365680</td>\n",
-       "      <td>1.276048</td>\n",
-       "      <td>1.121604</td>\n",
-       "      <td>0.667440</td>\n",
-       "      <td>1.929777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                U           A           H           I           S           D  \\\n",
-       "count  349.000000  349.000000  349.000000  349.000000  349.000000  349.000000   \n",
-       "mean     0.297994    0.091691    0.541547    0.653295    0.541547    0.759312   \n",
-       "std      0.800518    0.538906    1.048600    1.365680    1.276048    1.121604   \n",
-       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "75%      0.000000    0.000000    1.000000    1.000000    0.000000    1.000000   \n",
-       "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
-       "\n",
-       "                L           X  \n",
-       "count  349.000000  349.000000  \n",
-       "mean     0.169054    1.945559  \n",
-       "std      0.667440    1.929777  \n",
-       "min      0.000000    0.000000  \n",
-       "25%      0.000000    0.000000  \n",
-       "50%      0.000000    1.000000  \n",
-       "75%      0.000000    4.000000  \n",
-       "max      5.000000    5.000000  "
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF_dimCount.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "portuguese-military",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "U    104\n",
-       "A     32\n",
-       "H    189\n",
-       "I    228\n",
-       "S    189\n",
-       "D    265\n",
-       "L     59\n",
-       "X    679\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF_dimCount.sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dated-persian",
-   "metadata": {},
-   "source": [
-    "# Correlation between Entropy Values and Scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "weighted-climate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "regional-lloyd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDFEntropy1 = wordSimMergedDFEntropy.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "concerned-width",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimMergedDF_WEntropy.set_index(['Word 1', 'Word 2']).loc[('announcement', 'effort')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "living-silicon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF_WEntropy = wordSimMergedDF.join(wordSimMergedDFEntropy1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "adaptive-champion",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset is being reduced from 349 to 286\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset is being reduced from {len(wordSimMergedDF_WEntropy)} to {len(wordSimMergedDF_WEntropy[~wordSimMergedDF_WEntropy.Entropy.isna()])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "functioning-fireplace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF_WEntropy = wordSimMergedDF_WEntropy[~wordSimMergedDF_WEntropy.Entropy.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "twenty-series",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.22386618257947485, pvalue=0.00013458813322992746)"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordSimMergedDF_WEntropy.Entropy, wordSimMergedDF_WEntropy.Avg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "passive-teaching",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    224\n",
-       "U     51\n",
-       "I     11\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF_WEntropy.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "adequate-trick",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.634273174149212, pvalue=0.03608419202831131)"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'I'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Entropy, wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "immediate-recruitment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=-0.8140674774414028, pvalue=3.766571866164227e-13)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'U'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Entropy, wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "romance-recipient",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4724114239021895, pvalue=7.472630212984452e-14)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'M'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Entropy, wordSimMergedDF_WEntropy[wordSimMergedDF_WEntropy.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "third-suspect",
-   "metadata": {},
-   "source": [
-    "# Correlation between Entropy Values and Scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "apart-motel",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed while processing value P\n",
-      "Failed while processing value P\n",
-      "Failed while processing value 1\n",
-      "Failed while processing value 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy2 = pd.DataFrame(wordSimMergedDF.apply(findEntropy,axis=1, args=(3,)).to_list(), columns = ['U', 'A', 'H', 'I', 'S', 'D', 'L', 'X', 'countDims', 'Entropy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "distinct-repository",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>U</th>\n",
-       "      <th>A</th>\n",
-       "      <th>H</th>\n",
-       "      <th>I</th>\n",
-       "      <th>S</th>\n",
-       "      <th>D</th>\n",
-       "      <th>L</th>\n",
-       "      <th>X</th>\n",
-       "      <th>countDims</th>\n",
-       "      <th>Entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>349.000000</td>\n",
-       "      <td>219.000000</td>\n",
-       "      <td>219.000000</td>\n",
-       "      <td>219.000000</td>\n",
-       "      <td>219.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.053868</td>\n",
-       "      <td>0.016619</td>\n",
-       "      <td>0.101433</td>\n",
-       "      <td>0.113467</td>\n",
-       "      <td>0.102579</td>\n",
-       "      <td>0.425215</td>\n",
-       "      <td>0.050228</td>\n",
-       "      <td>0.119635</td>\n",
-       "      <td>4.401826</td>\n",
-       "      <td>0.988119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.158543</td>\n",
-       "      <td>0.106453</td>\n",
-       "      <td>0.209332</td>\n",
-       "      <td>0.271155</td>\n",
-       "      <td>0.254430</td>\n",
-       "      <td>0.617601</td>\n",
-       "      <td>0.164338</td>\n",
-       "      <td>0.158340</td>\n",
-       "      <td>0.791698</td>\n",
-       "      <td>0.626279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>0.721928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>0.970951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1.521928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>2.321928</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                U           A           H           I           S           D  \\\n",
-       "count  349.000000  349.000000  349.000000  349.000000  349.000000  349.000000   \n",
-       "mean     0.053868    0.016619    0.101433    0.113467    0.102579    0.425215   \n",
-       "std      0.158543    0.106453    0.209332    0.271155    0.254430    0.617601   \n",
-       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.200000   \n",
-       "75%      0.000000    0.000000    0.200000    0.000000    0.000000    0.600000   \n",
-       "max      1.000000    1.000000    1.000000    1.000000    1.000000    2.000000   \n",
-       "\n",
-       "                L           X   countDims     Entropy  \n",
-       "count  219.000000  219.000000  219.000000  219.000000  \n",
-       "mean     0.050228    0.119635    4.401826    0.988119  \n",
-       "std      0.164338    0.158340    0.791698    0.626279  \n",
-       "min      0.000000    0.000000    3.000000    0.000000  \n",
-       "25%      0.000000    0.000000    4.000000    0.721928  \n",
-       "50%      0.000000    0.000000    5.000000    0.970951  \n",
-       "75%      0.000000    0.200000    5.000000    1.521928  \n",
-       "max      1.000000    0.400000    5.000000    2.321928  "
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy2.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "another-kuwait",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>U</th>\n",
-       "      <th>A</th>\n",
-       "      <th>H</th>\n",
-       "      <th>I</th>\n",
-       "      <th>S</th>\n",
-       "      <th>D</th>\n",
-       "      <th>L</th>\n",
-       "      <th>X</th>\n",
-       "      <th>countDims</th>\n",
-       "      <th>Entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     U    A    H    I    S    D    L    X  countDims  Entropy\n",
-       "0  0.0  0.0  0.0  0.0  0.0  2.0  NaN  NaN        NaN      NaN\n",
-       "1  0.0  0.0  0.0  0.0  0.0  2.0  NaN  NaN        NaN      NaN\n",
-       "2  0.0  0.0  0.0  0.0  0.0  2.0  NaN  NaN        NaN      NaN\n",
-       "3  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0        5.0      0.0\n",
-       "4  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0        5.0      0.0"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDFEntropy2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "second-valuation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wordSimMergedDF_WEntropy.set_index(['Word 1', 'Word 2']).loc[('announcement', 'effort')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "demonstrated-explanation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF_WEntropy2 = wordSimMergedDF.join(wordSimMergedDFEntropy2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "genetic-inspiration",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset is being reduced from 286 to 219\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Dataset is being reduced from {len(wordSimMergedDF_WEntropy)} to {len(wordSimMergedDF_WEntropy2[~wordSimMergedDF_WEntropy2.Entropy.isna()])}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "seventh-syracuse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordSimMergedDF_WEntropy2 = wordSimMergedDF_WEntropy2[~wordSimMergedDF_WEntropy2.Entropy.isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "human-entrance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.4630150942094212, pvalue=4.909163356811773e-13)"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.spearmanr(wordSimMergedDF_WEntropy2.Entropy, wordSimMergedDF_WEntropy2.Avg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "capable-hacker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "M    216\n",
-       "I      3\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wordSimMergedDF_WEntropy2.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "relevant-flood",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/nas/home/kshenoy/miniconda3/envs/kgtkEnv/lib/python3.8/site-packages/scipy/stats/stats.py:4264: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
-      "  warnings.warn(SpearmanRConstantInputWarning())\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=nan, pvalue=nan)"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'I'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Entropy, wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "partial-malawi",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=nan, pvalue=nan)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'U'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Entropy, wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "beautiful-borough",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SpearmanrResult(correlation=0.48264071045505, pvalue=5.282364887488181e-14)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat1 = 'M'\n",
-    "stats.spearmanr(wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Entropy, wordSimMergedDF_WEntropy2[wordSimMergedDF_WEntropy2.category == cat1].Avg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "literary-patrick",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "congressional-assumption",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kgtkEnv",
-   "language": "python",
-   "name": "kgtkenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/model.pt b/model.pt
deleted file mode 100644
index 4cac0c2..0000000
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diff --git a/semantic_similarity/config.json b/semantic_similarity/config.json
deleted file mode 100644
index f2240ac..0000000
--- a/semantic_similarity/config.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "es_url": "http://ckg07.isi.edu:9200",
-  "es_index": "wikidatadwd-augmented",
-  "qnode_to_ids_file": "",
-  "faiss_index_file": ""
-}
diff --git a/ws353simrel.tar.gz b/ws353simrel.tar.gz
deleted file mode 100644
index bc72a9e..0000000
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